--- base_model: bobox/DeBERTa-ST-AllLayers-v3-checkpoints-tmp datasets: - sentence-transformers/all-nli - tals/vitaminc - nyu-mll/glue - allenai/scitail - sentence-transformers/xsum - sentence-transformers/sentence-compression - allenai/sciq - allenai/qasc - sentence-transformers/msmarco-msmarco-distilbert-base-v3 - sentence-transformers/natural-questions - sentence-transformers/trivia-qa - sentence-transformers/quora-duplicates - sentence-transformers/gooaq language: - en library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - dot_accuracy - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - manhattan_accuracy - manhattan_accuracy_threshold - manhattan_f1 - manhattan_f1_threshold - manhattan_precision - manhattan_recall - manhattan_ap - euclidean_accuracy - euclidean_accuracy_threshold - euclidean_f1 - euclidean_f1_threshold - euclidean_precision - euclidean_recall - euclidean_ap - max_accuracy - max_accuracy_threshold - max_f1 - max_f1_threshold - max_precision - max_recall - max_ap pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:165061 - loss:AdaptiveLayerLoss - loss:GISTEmbedLoss - loss:OnlineContrastiveLoss - loss:MultipleNegativesSymmetricRankingLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to move money from one ira account to another? sentences: - Apart from getting EPF withdrawal form from your employer, you can download it from the EPFO portal. If your Universal Account Number (UAN) is linked to your Aadhar then you can use the Aadhar-based withdrawal form. Otherwise, you can apply for the withdrawal through UAN's portal. - PJ Tucker is the worst starter on the Rockets, mainly because he is used as a minutes eater. He has the third-worst player efficiency rating on the team. He just doesn't compare in value to the other Houston starters. - Sign the paperwork and submit it to the new bank or brokerage. Contact your original trustee to request a direct transfer of your IRA funds to the IRA at the new institution. If you want to maintain your current investments, your trustee may be able to make an in-kind transfer, leaving your investments in place. - source_sentence: The number of COVID-19 cases was more than 241,500 worldwide by March 20 , 2020 . sentences: - More than 5,500 people have died from the disease and over 72,000 have recovered . - more than 6,500 deaths have been attributed to COVID-19 . - As of 20 March , more than 242,000 cases of COVID-19 have been reported in over 170 countries and territories , resulting in more than 9,900 deaths and 87,000 recoveries. On 13 March , the WHO announced that Europe had become the new epicentre of the pandemic . - source_sentence: 'Reese Witherspoon says she''s ``deeply embarrassed'''' by her arrest, Kim Kardashian thinks she would get married again and Farrah Abraham brings her dad and 3-year-old daughter to her sex tape negotiation:' sentences: - Scouts sell $3.5 million in popcorn - 'Reese Witherspoon is ``deeply embarrassed'''' by arrest, Kim Kardashian thinks she would get married again:' - Horse diagnosed with disease - source_sentence: who played snow queen in once upon a time sentences: - Terracotta Army The figures, dating from approximately the late third century BCE,[1] were discovered in 1974 by local farmers in Lintong District, Xi'an, Shaanxi province. The figures vary in height according to their roles, with the tallest being the generals. The figures include warriors, chariots and horses. Estimates from 2007 were that the three pits containing the Terracotta Army held more than 8,000 soldiers, 130 chariots with 520 horses and 150 cavalry horses, the majority of which remained buried in the pits nearby Qin Shi Huang's mausoleum.[2] Other terracotta non-military figures were found in other pits, including officials, acrobats, strongmen and musicians. - 'Elizabeth Mitchell Elizabeth Mitchell (born Elizabeth Joanna Robertson: March 27, 1970) is an American actress known for her role as Dr. Juliet Burke on the ABC series Lost.[1] She also had lead roles on the TV series V and Revolution, as well as the Snow Queen on Once Upon a Time and as Deb Carpenter on Dead of Summer. Mitchell has starred in such films as The Santa Clause 2 & 3: The Escape Clause, Gia and The Purge: Election Year.' - First Amendment to the United States Constitution The First Amendment (Amendment I) to the United States Constitution prevents Congress from making any law respecting an establishment of religion, prohibiting the free exercise of religion, or abridging the freedom of speech, the freedom of the press, the right to peaceably assemble, or to petition for a governmental redress of grievances. It was adopted on December 15, 1791, as one of the ten amendments that constitute the Bill of Rights. - source_sentence: what are light bulbs powered by? sentences: - an incandescent light bulb converts electricity into light by sending electricity through a filament. Incandescence is light from heat energy.. light bulbs are powered by heat - Viruses infect and live inside the cells of living organisms.. Life is a living organism.. viruses infect and live inside the cells of life. - Pressure receptors are found mainly in the skin.. Pacinian corpuscles are pressure receptors.. Pacinian corpuscles are found mainly in the skin. model-index: - name: SentenceTransformer based on bobox/DeBERTa-ST-AllLayers-v3-checkpoints-tmp results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: StS test type: StS-test metrics: - type: pearson_cosine value: 0.8793189500083313 name: Pearson Cosine - type: spearman_cosine value: 0.8947908169042798 name: Spearman Cosine - type: pearson_manhattan value: 0.8560302620930028 name: Pearson Manhattan - type: spearman_manhattan value: 0.8597992045669297 name: Spearman Manhattan - type: pearson_euclidean value: 0.8583553404512858 name: Pearson Euclidean - type: spearman_euclidean value: 0.8608598483729061 name: Spearman Euclidean - type: pearson_dot value: 0.7762320489548236 name: Pearson Dot - type: spearman_dot value: 0.7778451219338482 name: Spearman Dot - type: pearson_max value: 0.8793189500083313 name: Pearson Max - type: spearman_max value: 0.8947908169042798 name: Spearman Max - task: type: binary-classification name: Binary Classification dataset: name: mrpc test type: mrpc-test metrics: - type: cosine_accuracy value: 0.7315789473684211 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7114981412887573 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.8213689482470784 name: Cosine F1 - type: cosine_f1_threshold value: 0.6159781813621521 name: Cosine F1 Threshold - type: cosine_precision value: 0.7008547008547008 name: Cosine Precision - type: cosine_recall value: 0.9919354838709677 name: Cosine Recall - type: cosine_ap value: 0.8422966412940616 name: Cosine Ap - type: dot_accuracy value: 0.6973684210526315 name: Dot Accuracy - type: dot_accuracy_threshold value: 6.44296932220459 name: Dot Accuracy Threshold - type: dot_f1 value: 0.8047538200339559 name: Dot F1 - type: dot_f1_threshold value: 6.360085487365723 name: Dot F1 Threshold - type: dot_precision value: 0.6950146627565983 name: Dot Precision - type: dot_recall value: 0.9556451612903226 name: Dot Recall - type: dot_ap value: 0.7568855067656524 name: Dot Ap - type: manhattan_accuracy value: 0.7342105263157894 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 51.98240280151367 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.8178571428571427 name: Manhattan F1 - type: manhattan_f1_threshold value: 53.52705383300781 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.7339743589743589 name: Manhattan Precision - type: manhattan_recall value: 0.9233870967741935 name: Manhattan Recall - type: manhattan_ap value: 0.8235858099435117 name: Manhattan Ap - type: euclidean_accuracy value: 0.7315789473684211 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 2.6293671131134033 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.8169014084507041 name: Euclidean F1 - type: euclidean_f1_threshold value: 2.794234275817871 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.725 name: Euclidean Precision - type: euclidean_recall value: 0.9354838709677419 name: Euclidean Recall - type: euclidean_ap value: 0.8251648254909114 name: Euclidean Ap - type: max_accuracy value: 0.7342105263157894 name: Max Accuracy - type: max_accuracy_threshold value: 51.98240280151367 name: Max Accuracy Threshold - type: max_f1 value: 0.8213689482470784 name: Max F1 - type: max_f1_threshold value: 53.52705383300781 name: Max F1 Threshold - type: max_precision value: 0.7339743589743589 name: Max Precision - type: max_recall value: 0.9919354838709677 name: Max Recall - type: max_ap value: 0.8422966412940616 name: Max Ap - task: type: binary-classification name: Binary Classification dataset: name: Vitaminc test type: Vitaminc-test metrics: - type: cosine_accuracy value: 0.5736842105263158 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7548408508300781 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.6717267552182163 name: Cosine F1 - type: cosine_f1_threshold value: 0.4373416006565094 name: Cosine F1 Threshold - type: cosine_precision value: 0.5175438596491229 name: Cosine Precision - type: cosine_recall value: 0.9567567567567568 name: Cosine Recall - type: cosine_ap value: 0.5582893785596661 name: Cosine Ap - type: dot_accuracy value: 0.5552631578947368 name: Dot Accuracy - type: dot_accuracy_threshold value: 9.62537956237793 name: Dot Accuracy Threshold - type: dot_f1 value: 0.6703910614525139 name: Dot F1 - type: dot_f1_threshold value: 6.372426509857178 name: Dot F1 Threshold - type: dot_precision value: 0.5113636363636364 name: Dot Precision - type: dot_recall value: 0.972972972972973 name: Dot Recall - type: dot_ap value: 0.5545955028730921 name: Dot Ap - type: manhattan_accuracy value: 0.5736842105263158 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 57.34288787841797 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.6666666666666666 name: Manhattan F1 - type: manhattan_f1_threshold value: 100.97366333007812 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.5013623978201635 name: Manhattan Precision - type: manhattan_recall value: 0.9945945945945946 name: Manhattan Recall - type: manhattan_ap value: 0.5522704905667226 name: Manhattan Ap - type: euclidean_accuracy value: 0.5789473684210527 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 2.8532462120056152 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.6654611211573237 name: Euclidean F1 - type: euclidean_f1_threshold value: 5.014068603515625 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.5 name: Euclidean Precision - type: euclidean_recall value: 0.9945945945945946 name: Euclidean Recall - type: euclidean_ap value: 0.5536180101802943 name: Euclidean Ap - type: max_accuracy value: 0.5789473684210527 name: Max Accuracy - type: max_accuracy_threshold value: 57.34288787841797 name: Max Accuracy Threshold - type: max_f1 value: 0.6717267552182163 name: Max F1 - type: max_f1_threshold value: 100.97366333007812 name: Max F1 Threshold - type: max_precision value: 0.5175438596491229 name: Max Precision - type: max_recall value: 0.9945945945945946 name: Max Recall - type: max_ap value: 0.5582893785596661 name: Max Ap --- # SentenceTransformer based on bobox/DeBERTa-ST-AllLayers-v3-checkpoints-tmp This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [bobox/DeBERTa-ST-AllLayers-v3-checkpoints-tmp](https://huggingface.co/bobox/DeBERTa-ST-AllLayers-v3-checkpoints-tmp) on the [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli), [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc), [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue), [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail), [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum), [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression), [compression-pairs2](https://huggingface.co/datasets/sentence-transformers/sentence-compression), [compression-pairs3](https://huggingface.co/datasets/sentence-transformers/sentence-compression), [sciq_pairs](https://huggingface.co/datasets/allenai/sciq), [qasc_pairs](https://huggingface.co/datasets/allenai/qasc), [qasc_facts_sym](https://huggingface.co/datasets/allenai/qasc), openbookqa_pairs, [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [msmarco_pairs2](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [nq_pairs2](https://huggingface.co/datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates), [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq), [gooaq_pairs2](https://huggingface.co/datasets/sentence-transformers/gooaq) and [mrpc_pairs](https://huggingface.co/datasets/nyu-mll/glue) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [bobox/DeBERTa-ST-AllLayers-v3-checkpoints-tmp](https://huggingface.co/bobox/DeBERTa-ST-AllLayers-v3-checkpoints-tmp) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Datasets:** - [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) - [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) - [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) - [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) - [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) - [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) - [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression) - [compression-pairs2](https://huggingface.co/datasets/sentence-transformers/sentence-compression) - [compression-pairs3](https://huggingface.co/datasets/sentence-transformers/sentence-compression) - [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) - [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) - [qasc_facts_sym](https://huggingface.co/datasets/allenai/qasc) - openbookqa_pairs - [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) - [msmarco_pairs2](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) - [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) - [nq_pairs2](https://huggingface.co/datasets/sentence-transformers/natural-questions) - [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) - [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) - [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) - [gooaq_pairs2](https://huggingface.co/datasets/sentence-transformers/gooaq) - [mrpc_pairs](https://huggingface.co/datasets/nyu-mll/glue) - **Language:** en ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("bobox/DeBERTa-ST-AllLayers-v3.1bis") # Run inference sentences = [ 'what are light bulbs powered by?', 'an incandescent light bulb converts electricity into light by sending electricity through a filament. Incandescence is light from heat energy.. light bulbs are powered by heat', 'Viruses infect and live inside the cells of living organisms.. Life is a living organism.. viruses infect and live inside the cells of life.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `StS-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8793 | | **spearman_cosine** | **0.8948** | | pearson_manhattan | 0.856 | | spearman_manhattan | 0.8598 | | pearson_euclidean | 0.8584 | | spearman_euclidean | 0.8609 | | pearson_dot | 0.7762 | | spearman_dot | 0.7778 | | pearson_max | 0.8793 | | spearman_max | 0.8948 | #### Binary Classification * Dataset: `mrpc-test` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.7316 | | cosine_accuracy_threshold | 0.7115 | | cosine_f1 | 0.8214 | | cosine_f1_threshold | 0.616 | | cosine_precision | 0.7009 | | cosine_recall | 0.9919 | | cosine_ap | 0.8423 | | dot_accuracy | 0.6974 | | dot_accuracy_threshold | 6.443 | | dot_f1 | 0.8048 | | dot_f1_threshold | 6.3601 | | dot_precision | 0.695 | | dot_recall | 0.9556 | | dot_ap | 0.7569 | | manhattan_accuracy | 0.7342 | | manhattan_accuracy_threshold | 51.9824 | | manhattan_f1 | 0.8179 | | manhattan_f1_threshold | 53.5271 | | manhattan_precision | 0.734 | | manhattan_recall | 0.9234 | | manhattan_ap | 0.8236 | | euclidean_accuracy | 0.7316 | | euclidean_accuracy_threshold | 2.6294 | | euclidean_f1 | 0.8169 | | euclidean_f1_threshold | 2.7942 | | euclidean_precision | 0.725 | | euclidean_recall | 0.9355 | | euclidean_ap | 0.8252 | | max_accuracy | 0.7342 | | max_accuracy_threshold | 51.9824 | | max_f1 | 0.8214 | | max_f1_threshold | 53.5271 | | max_precision | 0.734 | | max_recall | 0.9919 | | **max_ap** | **0.8423** | #### Binary Classification * Dataset: `Vitaminc-test` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.5737 | | cosine_accuracy_threshold | 0.7548 | | cosine_f1 | 0.6717 | | cosine_f1_threshold | 0.4373 | | cosine_precision | 0.5175 | | cosine_recall | 0.9568 | | cosine_ap | 0.5583 | | dot_accuracy | 0.5553 | | dot_accuracy_threshold | 9.6254 | | dot_f1 | 0.6704 | | dot_f1_threshold | 6.3724 | | dot_precision | 0.5114 | | dot_recall | 0.973 | | dot_ap | 0.5546 | | manhattan_accuracy | 0.5737 | | manhattan_accuracy_threshold | 57.3429 | | manhattan_f1 | 0.6667 | | manhattan_f1_threshold | 100.9737 | | manhattan_precision | 0.5014 | | manhattan_recall | 0.9946 | | manhattan_ap | 0.5523 | | euclidean_accuracy | 0.5789 | | euclidean_accuracy_threshold | 2.8532 | | euclidean_f1 | 0.6655 | | euclidean_f1_threshold | 5.0141 | | euclidean_precision | 0.5 | | euclidean_recall | 0.9946 | | euclidean_ap | 0.5536 | | max_accuracy | 0.5789 | | max_accuracy_threshold | 57.3429 | | max_f1 | 0.6717 | | max_f1_threshold | 100.9737 | | max_precision | 0.5175 | | max_recall | 0.9946 | | **max_ap** | **0.5583** | ## Training Details ### Training Datasets #### nli-pairs * Dataset: [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 10,000 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------|:-------------------------------------------------| | A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | | Children smiling and waving at camera | There are children present | | A boy is jumping on skateboard in the middle of a red bridge. | The boy does a skateboarding trick. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 11,500 training samples * Columns: claim and evidence * Approximate statistics based on the first 1000 samples: | | claim | evidence | |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | claim | evidence | |:--------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Danny Trevathan made more than 100 tackles in the 2013 season . | In the 2013 season , Trevathan led the team with 125 tackles . | | Katy Perry : Part of Me has grossed over $ 6.7 million internationally and more than $ 32 million globally . | Katy Perry : Part of Me has grossed $ 25,326,071 domestically and $ 6,882,884 internationally , totaling $ 32,208,955 worldwide . | | Bark at the Moon became Platinum for selling more than 700,000 copies in the United States . | The album peaked at number 19 on the Billboard album chart and within several weeks of release was certified Platinum for over a million sales in the United States alone . | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### qnli-contrastive * Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) * Size: 13,300 training samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:--------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | How does the Center for Measuring University performance rank Washington University? | According to the Center for Measuring University Performance, it is considered to be one of the top 10 private research universities in the nation. | 0 | | Besides tax shifting, what is another need? | Just as there is a need for tax shifting, there is also a need for subsidy shifting. | 0 | | How large is the Irish diaspora that was caused by the Great Irish Famine? | However, since the Great Irish Famine, the population of Ireland has fallen to less than one tenth of the population of the British Isles. | 0 | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "OnlineContrastiveLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 0.8, "kl_temperature": 0.75 } ``` #### scitail-pairs-qa * Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 11,155 training samples * Columns: sentence2 and sentence1 * Approximate statistics based on the first 1000 samples: | | sentence2 | sentence1 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence2 | sentence1 | |:----------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------| | The first living cells may have evolved around 4 billion years ago. | The first living cells may have evolved around how long ago? | | By 8 weeks, all major organs start developing. | By how many weeks do all major organs start developing? | | Intrinsic muscles allow your fingers to also make precise movements for actions. | Which muscles allow your fingers to also make precise movements for actions? | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### scitail-pairs-pos * Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 8,600 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------| | Retina The retina uses nerve cells known as rods and cones (photoreceptors) to detect light and color. | The light-sensing cells in the retina are called rods and cones. | | Breakwaters are structures that protect the coast like barrier islands. | The name of artificial barriers that people build to protect shorelines is breakwaters. | | The speed of the wave is equal to the wavelength times the frequency. | The product of a wave's wavelength and its frequency is its speed. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### xsum-pairs * Dataset: [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) at [788ddaf](https://huggingface.co/datasets/sentence-transformers/xsum/tree/788ddafe04e539956d56b567bc32a036ee7b9206) * Size: 7,000 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------| | The Surrey force said a 51-year-old woman from East Molesey voluntarily spoke to officers.
The incident at Hampton Court was filmed by cycling instructor David Williams, 47, who posted it on YouTube.
Officers said its traffic and process unit would consider whether to take further action.
The woman was given a referral notice of "driving whilst not in proper control of motor vehicle".
The police force said it would consider whether the woman should face penalties, including being required to attend a driver improvement course or receive a fixed penalty notice.
The alleged incident happened at the junction of Creek Road and the A309 Hampton Court Way in Molesey. | A woman who was filmed allegedly eating a bowl of cereal while driving at a busy junction in south-west London has been interviewed by police. | | Paul Hegarty was sacked last month, with the Gable Endies picking up one point from three games since his departure.
Petrie, 46, leaves his post as as assistant manager at Junior Super League side Broughty Athletic.
Born in Dundee, he played for Forfar, Dunfermline and Ross County and had spells in Australia and Singapore.
Petrie has previously served as assistant manager at Forfar and Arbroath.
"He is highly motivated and enthusiastic, comes with a wealth of playing and coaching experience and most importantly understands the lower leagues of Scottish football," said chairman John Crawford.
"I would like to thank John Holt for his support in stepping in and looking after the team since the departure of Paul Hegarty and wish him every success for the future." | Stewart Petrie is the new manager at struggling Montrose, taking over with the club at the foot of League Two. | | The 28-year-old former Bournemouth, Portsmouth and Leeds United centre-back has signed a three-year contract with the Addicks.
Pearce made 33 appearances last season as the Latics won the League One title and becomes Charlton's seventh signing of the summer transfer window.
"He's an absolute warrior," boss Russell Slade told the club website.
Find all the latest football transfers on our dedicated page. | League One club Charlton Athletic have signed defender Jason Pearce from Wigan for an undisclosed fee. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 0.8, "kl_temperature": 0.75 } ``` #### compression-pairs * Dataset: [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co/datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90) * Size: 4,014 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------| | one who survived one of the city's worst mass shootings but left behind a trail of death and violence in British Columbia and Alberta going back almost a decade. | Victim had survived mass shooting | | ``It's an awesome feeling,'' Vick said of getting the bankruptcy plan approved, which includes how his creditors will be repaid more than $20 million. | Michael Vick's bankruptcy plan approved | | Kevin Youkilis put on pinstripes and played at Yankee Stadium for the first time as a member of the New York Yankees on Monday afternoon. | Youkilis downplays putting on pinstripes at Yankee Stadium | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 0.8, "kl_temperature": 0.75 } ``` #### compression-pairs2 * Dataset: [compression-pairs2](https://huggingface.co/datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co/datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90) * Size: 7,960 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| | Industrial Parks in Asia are seeing very strong demand as Foreign Companies take advantage of the many investment incentives on offer. | Industrial Parks in Asia see strong demand | | Western Australia has the highest loan delinquency rates in the country with two percent of its home loan payments delayed by more than a month compared to the national average of 1.54 percent says Fitch Ratings. | Western Australia has highest delinquency rate in the country | | Governor Chet Culver today issued a disaster emergency proclamation for Des Moines County in response to severe thunderstorms that hit the area on May 12. | Governor Culver issues disaster proclamation for Des Moines County | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 3, "last_layer_weight": 0.15, "prior_layers_weight": 2.5, "kl_div_weight": 0.75, "kl_temperature": 0.5 } ``` #### compression-pairs3 * Dataset: [compression-pairs3](https://huggingface.co/datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co/datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90) * Size: 7,960 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| | WHILE London 2012 might seem a bit farfetched for a 17-year-old, water polo player Tuesday Birmingham is hoping her undefeated streak makes selectors sit up and take notice. | Birmingham hoping selectors sit up and take notice | | Police are searching for a man accused of trying to kidnap a girl from her North Charlotte bus stop. | Police search for man accused of trying to kidnap girl at bus stop | | NASCAR has ``called a mandatory meeting for all Sprint Cup drivers and team owners for Tuesday morning'' at its Research and Development Center in Concord, North Carolina, according to David Newton of ESPN.com. | NASCAR calls mandatory meeting for Sprint Cup drivers, owners | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "n_layers_per_step": 3, "last_layer_weight": 0.05, "prior_layers_weight": 10, "kl_div_weight": 5, "kl_temperature": 0.2 } ``` #### sciq_pairs * Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) * Size: 10,750 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What do secondary spermatocytes form when completing meiosis? | Spermatogonia lining the seminiferous tubule undergo mitosis to form primary spermatocytes , which are also diploid. The primary spermatocytes undergo the first meiotic division to form secondary spermatocytes , which are haploid. Spermatocytes make up the next layer of cells inside the seminiferous tubule. Finally, the secondary spermatocytes complete meiosis to form spermatids . Spermatids make up a third layer of cells in the tubule. | | Unlike ammonia, oxygen cannot be liquefied at room temperature because its what is below room temperature? | Check Your Learning Ammonia can be liquefied by compression at room temperature; oxygen cannot be liquefied under these conditions. Why do the two gases exhibit different behavior? Answer: The critical temperature of ammonia is 405.5 K, which is higher than room temperature. The critical temperature of oxygen is below room temperature; thus oxygen cannot be liquefied at room temperature. | | The distance between two consecutive z discs or z lines is called what? | When a sarcomere shortens, some regions shorten whereas others stay the same length. A sarcomere is defined as the distance between two consecutive Z discs or Z lines; when a muscle contracts, the distance between the Z discs is reduced. The H zone—the central region of the A zone—contains only thick filaments and is shortened during contraction. The I band contains only thin filaments and also shortens. The A band does not shorten—it remains the same length—but A bands of different sarcomeres move closer together during contraction, eventually disappearing. Thin filaments are pulled by the thick filaments toward the center of the sarcomere until the Z discs approach the thick filaments. The zone of overlap, in which thin filaments and thick filaments occupy the same area, increases as the thin filaments move inward. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### qasc_pairs * Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 7,889 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What is a type of the basic units of the structure and function of living things? | Cells are the basic units of the structure and function of living things.. Osteogenic cell types are indicated.. Osteogenic is a type of the basic units of the structure and function of living things. | | bacteria can cause people to need what? | bacteria can cause people to become ill. Serious illness needing hospitalization is uncommon.. bacteria can cause people to need hospitalization. | | What type of energy generated by the bulbs is wasted? | some light bulbs convert electricity into light and heat energy. Since the purpose of a light bulb is to generate light, the heat is wasted energy.. The heat energy generated by the bulbs gets wasted. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### qasc_facts_sym * Dataset: [qasc_facts_sym](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 7,889 training samples * Columns: combinedfact and facts * Approximate statistics based on the first 1000 samples: | | combinedfact | facts | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | combinedfact | facts | |:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------| | weathering means that rocks are eroded down again. | weathering means breaking down rocks from larger whole into smaller pieces by weather. Erosion breaks the rock down again, and the cycle continues.. | | cutting down trees has a negative impact on the health of humankind | cutting down trees has a negative impact on an ecosystem. When ecosystems are healthy, humankind is healthy.. | | Fossils are formed with sand and mud cover the remains over time | fossils are formed when layers of sediment cover the remains of organisms over time. Sand and mud are examples of sediments.. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 0.8, "kl_temperature": 0.75 } ``` #### openbookqa_pairs * Dataset: openbookqa_pairs * Size: 4,505 training samples * Columns: question and fact * Approximate statistics based on the first 1000 samples: | | question | fact | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | fact | |:-----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | What is animal competition? | if two animals eat the same prey then those animals compete for that pey | | If you wanted to make a metal bed frame, where would you start? | alloys are made of two or more metals | | Places lacking warmth have few what | cold environments contain few organisms | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### msmarco_pairs * Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9) * Size: 6,875 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | benefits of reishi mushroom | Reishi mushroom has been used to help enhance the immune system, reduce stress, improve sleep, and lessen fatigue. People also take reishi mushroom for health conditions such as: High blood pressure | | shoulder replacement how long driving | Shoulder Replacement Surger. After undergoing shoulder replacement surgery, it is important to have realistic expectations about the types of activities you may do. Minimal stretching of your new shoulder is recommended throughout your lifetime to maintain your full range of motion.You may give you permission to drive within four weeks after the surgery. If your surgery was on the right side, driving permission may not be. given until a month or six weeks following the surgery.inimal stretching of your new shoulder is recommended throughout your lifetime to maintain your full range of motion. You may give you permission to drive within four weeks after the surgery. If your surgery was on the right side, driving permission may not be. given until a month or six weeks following the surgery. | | cabinet has how many people in it trump | Should all of President-elect Donald Trump’s Cabinet nominees eventually be confirmed, he will start his administration with one of the most heavily business-oriented Cabinets in U.S. history. Five of the 15 people Trump has nominated to be Cabinet secretaries have spent all or nearly all their careers in the business world, with no significant public office or senior military service on their résumés. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### msmarco_pairs2 * Dataset: [msmarco_pairs2](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9) * Size: 4,500 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | when is grant of probate not required | We are frequently asked whether a grant of probate is always required after somebody’s death. Very small estates under £15,000 do not require a grant while those assets held in joint names (such as bank accounts or property) pass to the survivor on the Deceased persons death, meaning that no grant is required.he grant is a legal document which states the Deceased’s full name, as well as the Executors entrusted with the administration of the estate. The grant can then be presented to various financial institutions to collect the Deceased’s assets. | | what does speculation mean | Wiktionary(3.00 / 1 vote)Rate this definition: 1 speculation(Noun) The process of thinking or meditating on a subject. 2 speculation(Noun) A judgment or conclusion reached by speculating. 3 speculation(Noun) An investment involving higher than normal risk in order to obtain a higher than normal return. | | what county is mint hill nc | 8. 21 Acres Mint Hill, Mecklenburg County, North Carolina $2,310,000. 21 Acres Mint Hill, Mecklenburg County, North Carolina $2,310,000. Great piece of property just waiting for the right buyer. Flat and open and conveniently located at 485 and Idlewild Rd....a rare find indeed! | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 3, "last_layer_weight": 0.15, "prior_layers_weight": 2.5, "kl_div_weight": 0.75, "kl_temperature": 0.5 } ``` #### nq_pairs * Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 3,667 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | where are the powers of congress listed in the constitution | Powers of the United States Congress Article I of the Constitution sets forth most of the powers of Congress, which include numerous explicit powers enumerated in Section 8. Constitutional amendments have granted Congress additional powers. Congress also has implied powers derived from the Necessary and Proper Clause of the Constitution. | | who is the man in we are never ever getting back together | We Are Never Ever Getting Back Together After writing Speak Now (2010) entirely solo, Swift opted to collaborate with different songwriters and producers for Red. Thus, she called Max Martin and Shellback, two songwriters and producers whose work she admired, to discuss a possible collaboration. The trio conceived the concept for "We Are Never Ever Getting Back Together" shortly after a friend of Swift's ex-boyfriend walked into the recording studio and spoke of rumors he heard that Swift and her former flame were reuniting. After the friend left, Martin and Shellback asked Swift to elaborate on the details of the relationship, which she described as "break up, get back together, break up, get back together, just, ugh, the worst". When Martin suggested that they write about the incident. Swift began playing the guitar and singing, "We are never ever......", and the song flowed rapidly afterwards. She described the process as one of the most humorous experiences she had while recording, and said the musical partners matched her expectations. An audio clip of her sarcastically speaking about breakups can be heard before the final chorus.[2] The song is reportedly about Swift's ex, Jake Gyllenhaal, as the two had broken up in January 2011 but had been seen on a date a few days later.[3] After the release of the music video, more clues linking the song to Gyllenhaal emerged,[3] with the actor looking like Gyllenhaal,[4] the actor in the video giving her a scarf as Gyllenhaal had reportedly done for Swift and a bracelet Swift wears in the video that is speculated to look similar to that of which Gyllenhaal was rumored to have given Swift for her birthday.[3] | | which agreement settled a boundary dispute between british canada and the united states | Alaska boundary dispute Finally, in 1903, the Hay-Herbert Treaty between the United States and Britain entrusted the decision to an arbitration by a mixed tribunal of six members: three Americans (Elihu Root, Secretary of War; Henry Cabot Lodge, senator from Massachusetts; and George Turner, ex-senator from Washington), two Canadians (Sir Louis A. Jette, Lieutenant Governor of Quebec; and Allen B. Aylesworth, K.C., from Toronto), and one Briton (Baron Alverstone). All sides respected Root, but he was a member of the U.S. Cabinet. Canadians ridiculed the choice of the obscure ex-Senator Turner and, especially, Lodge, a leading historian and diplomatic specialist whom they saw as an unobjective.[8] | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### nq_pairs2 * Dataset: [nq_pairs2](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 3,001 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what do i need to carry a gun in indiana | Gun laws in Indiana Indiana requires a license for carrying a handgun generally, although there are several exceptions. Manner of carry, whether open or concealed, is not explicitly specified in the code, thus the license is required for concealed, open, or transport within vehicle, unless covered by one of the exceptions. A license is not required if carrying on property "owned, rented, leased, or otherwise legally controlled" by the carrier, or carried on another person's legally controlled property if permission has been obtained. A license is not required for carrying at a "gun show, firearm expo, gun owner's club or convention, hunting club, shooting club, or training course", or places where carrier is receiving "firearm related services." A license is also not required at a "shooting range", at a location where one is participating in a "firearms instructional course", or during "legal hunting activity." One may transport a handgun in a vehicle without a license if the handgun is "unloaded", "not readily accessible", and "secured in a case." A violation of is a class A misdemeanor. [5] | | who plays kristen and susan on days of our lives | Susan Banks Susan Banks is a fictional character on NBC's daytime drama Days of Our Lives. She was played by Eileen Davidson from November 4, 1996 to April 8, 1998, and again in 2014 and 2017. Susan is the eccentric mother of Elvis "EJ" DiMera, and once acted as Kristen Blake's doppelganger. In November 2011, it was announced that Brynn Thayer would take over the role of Susan, since Davidson was committed to The Young and the Restless. Thayer made her brief one-off appearance as Susan on December 7, 2011.[1] | | what is the revelation in the hero journey | Hero's journey This is the point of realization in which a greater understanding is achieved. Armed with this new knowledge and perception, the hero is resolved and ready for the more difficult part of the adventure. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 3, "last_layer_weight": 0.15, "prior_layers_weight": 2.5, "kl_div_weight": 0.75, "kl_temperature": 0.5 } ``` #### trivia_pairs * Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) * Size: 9,700 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Most of the Three Tenors come from which country? | The Three Tenors - Pavarotti, Domingo, and Carreras The Three Tenors The Three Tenors Jose Carreras, Placido Domingo, and Luciano Pavarotti The Three Tenors - Luciano Pavarotti, Placido Domingo, and Jose Carreras.  Photo By: Yoshikatsu Tsuno / Getty Images By Aaron Green Who Are the Three Tenors? The Three Tenors are made up of three of the world's most famous and beloved operatic tenors which include Jose Carreras, Placido Domingo, and Luciano Pavarotti. Jose Carreras (1946-) Born in Barcelona, Spain, Jose Carreras has been performing since he was 11 years old.  Professionally, Carreras began his operatic career on December 19, 1970, when he sang the principal role of Gennaro alongside Montserrat Caballe in Donizetti's Lucrezia Borgia .  Aside from performing, Carreras serves as president for the José Carreras International Leukaemia Foundation, which he founded after successfully overcoming his own battle with cancer. Placido Domingo (1941-) With over 100 operas and 147 roles under his belt, Placido Domingo is a seasoned operatic star.  Born in Madrid, Spain, the celebrated tenor made his operatic debut as “Alfredo” in La Triviata at Monterrey, Mexico in 1961.  Just as Carreras and Pavarotti, Domingo has performed in opera houses around the world.  Now in his mid-70s and understanding the changes in his voice, Domingo sings baritone roles instead.  In 1993, Domingo founded a young singer competition called Operalia.  The competition is open to 18-32 year olds, and is hosted in a different city every year.  Out of nearly 1,000 entrants, only the top 40 are selected for the competition. continue reading below our video Great Singers Gone too Soon Luciano Pavarotti (1935-2007) Born in Modena, Italy, Pavarotti had dreams of becoming a soccer goalkeeper, and it turned out he was quite good.  However, his interest in music edged its way ahead after he won first place in the Llangollen International Singing Competition in Wales.  Pavarotti went on to become one of the first opera stars to have nearly his entire performing career recorded musically and visually.  He easily sold out shows and performed for millions of people in single performances. The Origin of the Three Tenors The idea for the Three Tenors came from Mario Dradi, an Italian manager and producer.  Dradi's idea was to create a group of tenors for a concert and donate a portion of the proceeds to Jose Carreras's foundation after his successful treatment of leukemia.  Jose Carreras, along with his two friends,  Placido Domingo and Luciano Pavarotti , agreed to perform as the Three Tenors .  Dradi's idea came to fruition on July 7, 1990, the day before the FIFA World Cup in Rome.  The concert was watched by over 800 million viewers, and was so well received that when a recording of the concert was released, it became the biggest selling classical album in history.  The album, Carreras - Domingo - Pavarotti: the Three Tenors in Concert , set a Guinness World Record.  Because of the trio's instant success, they performed at the following three FIFA World Cups: Los Angeles in 1994, Paris in 1998, and Yokohama in 2002.  The tremendous reception of the Three Tenors was due largely in part to their incredible voices, down-to-earth, likable personalities, and song selections. The trio would regularly perform classic and well-known operatic arias, as well as popular Broadway show tunes that even the most novice classical music listener could love and appreciate.  Given the trio's enormous popularity, imitations of the Three Tenors quickly arose all over the world, including the Three Canadian Tenors, the Chinese Tenors, as well as the Three Mo' Tenors. The Three Tenors: Recommended YouTube Videos The Three Tenors sing "O Sole Mio" (1994) ( Watch on YouTube ) The Three Tenors sing "La donna e mobile" (1994) ( Watch on YouTube ) The Three Tenors sing "Singin' in the Rain" (1994) ( Watch on YouTube ) The Three Tenors sing "New York, New York" (1996) ( Watch on YouTube ) The Three Tenors sing "Ti Voglio Tanto Bene" (1998) ( Watch on YouTube ) The Three Tenors sing "Nessun Dorm | | What are the two inferior planets in our solar system? | An Overview of the Solar System, it's alignment and pictures The inner solar system contains the Sun , Mercury , Venus , Earth and Mars : The main asteroid belt (not shown) lies between the orbits of Mars and Jupiter. The planets of the outer solar system are Jupiter , Saturn , Uranus , and Neptune ( Pluto is now classified as a dwarf planet): The first thing to notice is that the solar system is mostly empty space. The planets are very small compared to the space between them. Even the dots on the diagrams above are too big to be in proper scale with respect to the sizes of the orbits. The orbits of the planets are ellipses with the Sun at one focus, though all except Mercury are very nearly circular. The orbits of the planets are all more or less in the same plane (called the ecliptic and defined by the plane of the Earth's orbit) . The ecliptic is inclined only 7 degrees from the plane of the Sun's equator. The above diagrams show the relative sizes of the orbits of the eight planets (plus Pluto) from a perspective somewhat above the ecliptic (hence their non-circular appearance). They all orbit in the same direction (counter-clockwise looking down from above the Sun's north pole); all but Venus, Uranus and Pluto also rotate in that same sense. (The above diagrams show correct positions for October 1996 as generated by the excellent planetarium program Starry Night ; there are also many other similar programs available, some free. You can also use Emerald Chronometer on your iPhone or Emerald Observatory on your iPad to find the current positions.) Sizes The above composite shows the eight planets and Pluto with approximately correct relative sizes (see another similar composite and a comparison of the terrestrial planets or Appendix 2 for more). One way to help visualize the relative sizes in the solar system is to imagine a model in which everything is reduced in size by a factor of a billion. Then the model Earth would be about 1.3 cm in diameter (the size of a grape). The Moon would be about 30 cm (about a foot) from the Earth. The Sun would be 1.5 meters in diameter (about the height of a man) and 150 meters (about a city block) from the Earth. Jupiter would be 15 cm in diameter (the size of a large grapefruit) and 5 blocks away from the Sun. Saturn (the size of an orange) would be 10 blocks away; Uranus and Neptune (lemons) 20 and 30 blocks away. A human on this scale would be the size of an atom but the nearest star would be over 40000 km away. Not shown in the above illustrations are the numerous smaller bodies that inhabit the solar system: the satellites of the planets; the large number of asteroids (small rocky bodies) orbiting the Sun, mostly between Mars and Jupiter but also elsewhere; the comets (small icy bodies) which come and go from the inner parts of the solar system in highly elongated orbits and at random orientations to the ecliptic; and the many small icy bodies beyond Neptune in the Kuiper Belt . With a few exceptions, the planetary satellites orbit in the same sense as the planets and approximately in the plane of the ecliptic but this is not generally true for comets and asteroids. The classification of these objects is a matter of minor controversy. Traditionally, the solar system has been divided into planets (the big bodies orbiting the Sun), their satellites (a.k.a. moons, variously sized objects orbiting the planets), asteroids (small dense objects orbiting the Sun) and comets (small icy objects with highly eccentric orbits). Unfortunately, the solar system has been found to be more complicated than this would suggest: there are several moons larger than Pluto and two larger than Mercury; there are many small moons that are probably started out as asteroids and were only later captured by a planet; comets sometimes fizzle out and become indistinguishable from asteroids; the Kuiper Belt objects (including Pluto) and others like Chiron don't fit this scheme well The Earth/Moon and Pluto/Charon systems are sometimes considered "double planets". Other classification | | What is the word for ‘Friend’ in Swahili? | Swahili Plural Swahili Plural Swahili Lessons Swahili Plural If you're trying to learn Swahili Plural which is also called Kiswahili, check our courses about Plural and Singular... to help you with your Swahili grammar. Try to concentrate on the lesson and notice the pattern that occurs each time the word changes its place. Also don't forget to check the rest of our other lessons listed on Learn Swahili . Enjoy the rest of the lesson! Swahili Plural Learning the Swahili Plural displayed below is vital to the language. Swahili Plurals are grammatical numbers, typically referring to more than one of the referent in the real world. In the English language, singular and plural are the only grammatical numbers. Grammar Tips:  mtu amekuja( a person hascome), watu wamekuja( persons have come) msichana ameingia( a girl hasentered), wasichana wameingia ( girls have entered) daktari ametoka( the doctor hasgone ot) Madaktari wametoka( doctors have gone out)   This class is called A-WA class as you cansee from sentence construction. * In this class many nouns have prefix m- inthe singular and wa- in the prulal. However there are very many irregular nounsthat don’t follow this rule. E.g Rafiki( friend) Daktari( doctor) Kiwete(lameperson), Rafiki(a friend) becomes: marafiki ( friends) Mwanamke(one woman) becomes: Wanawake (manywomen) Hilii ni gari langu lekundu (this is my redcar) becomes: Haya ni magari yangu mekundu (these are my red cars) Here are some examples: | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### quora_pairs * Dataset: [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) * Size: 9,822 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------| | If the world war 3 breaks out, which country would be safest to live and why? | If a global war, or WW3 broke out, which country would be the safest to live in? | | From where and how to learn math? | How can I learn math? | | Does any one exist in this universe who never broke down any rules? | Does any one exist in this universe who never broke down the rules? | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 3, "last_layer_weight": 0.15, "prior_layers_weight": 2.5, "kl_div_weight": 0.75, "kl_temperature": 0.5 } ``` #### gooaq_pairs * Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 6,875 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | are sandwiches bad for cholesterol? | However, if you have started watching your cholesterol and triglycerides, adding those plentiful layers of deli meat could sabotage an otherwise heart-healthy meal. Animal meats contain varying amounts of saturated fat — which may increase lipid levels in your blood. | | what are the vocal music of mindanao? | Mindanao folk music includes the ancient Muslim folk song and dance called estijaro, and a Mindanao folk song called uruyan. These are usually accompanied by drums, gongs, or other percussion instruments like the subing, a gong. | | what are immediate cause of death? | Immediate cause of death:The final disease or injury causing the death. Intermediate cause of death: A disease or condition that preceded and caused the immediate cause of death. Underlying cause of death: A disease or condition present before, and leading to, the intermediate or immediate cause of death. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### gooaq_pairs2 * Dataset: [gooaq_pairs2](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 5,625 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | is mhrb legal in canada? | Canada: The Mimosa Hostilis plant is legal in Canada. | | are 4k blu ray discs region free? | When it comes to regional restrictions, the good news is 4K Blu-ray discs have just one region code: worldwide. Essentially, they're region-free. They can be played on any 4K player, in any part of the world. | | are rsd and crps the same? | What is Reflex Sympathetic Dystrophy (RSD) Syndrome? RSD is an older term used to describe one form of Complex Regional Pain Syndrome (CRPS). | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 3, "last_layer_weight": 0.15, "prior_layers_weight": 2.5, "kl_div_weight": 0.75, "kl_temperature": 0.5 } ``` #### mrpc_pairs * Dataset: [mrpc_pairs](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) * Size: 2,474 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------| | Prosecutors filed a motion informing Lee they intend to seek the death penalty . | He added that prosecutors will seek the death penalty . | | Although Prempro pills were used in the study , the researchers said they had no evidence that other brands were safer . | Athough Prempro pills were used in the study , the researchers say they have no evidence that other brands are safer . | | D 'Cunha said , from a science standpoint , Toronto 's last case was April 19 , so the all-clear day was actually yesterday . | He said , from a science standpoint , the city 's last case was April 19 , so the all clear day was actually yesterday . | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 0.8, "kl_temperature": 0.75 } ``` ### Evaluation Datasets #### nli-pairs * Dataset: [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 160 evaluation samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------| | Two women are embracing while holding to go packages. | Two woman are holding packages. | | Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink. | Two kids in numbered jerseys wash their hands. | | A man selling donuts to a customer during a world exhibition event held in the city of Angeles | A man selling donuts to a customer. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 133 evaluation samples * Columns: claim and evidence * Approximate statistics based on the first 1000 samples: | | claim | evidence | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | claim | evidence | |:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Dragon Con had over 5000 guests . | Among the more than 6000 guests and musical performers at the 2009 convention were such notables as Patrick Stewart , William Shatner , Leonard Nimoy , Terry Gilliam , Bruce Boxleitner , James Marsters , and Mary McDonnell . | | COVID-19 has reached more than 185 countries . | As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths . | | In March , Italy had 3.6x times more cases of coronavirus than China . | As of 12 March , among nations with at least one million citizens , Italy has the world 's highest per capita rate of positive coronavirus cases at 206.1 cases per million people ( 3.6x times the rate of China ) and is the country with the second-highest number of positive cases as well as of deaths in the world , after China . | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### qnli-contrastive * Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) * Size: 160 evaluation samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:--------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | What came into force after the new constitution was herald? | As of that day, the new constitution heralding the Second Republic came into force. | 0 | | What is the first major city in the stream of the Rhine? | The most important tributaries in this area are the Ill below of Strasbourg, the Neckar in Mannheim and the Main across from Mainz. | 0 | | What is the minimum required if you want to teach in Canada? | In most provinces a second Bachelor's Degree such as a Bachelor of Education is required to become a qualified teacher. | 0 | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "OnlineContrastiveLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 0.8, "kl_temperature": 0.75 } ``` #### scitail-pairs-qa * Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 160 evaluation samples * Columns: sentence2 and sentence1 * Approximate statistics based on the first 1000 samples: | | sentence2 | sentence1 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence2 | sentence1 | |:--------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------| | Modern members of the reptiles group live in many different habitats and are found on every continent except antarctica. | Modern members of what broad animal group live in many different habitats and are found on every continent except antarctica? | | Plants not only contribute food but oxygen for organisms. | Plants not only contribute food but what else for organisms? | | The water cycle involves movement of water between air and land. | The water cycle involves movement of water between air and what? | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### scitail-pairs-pos * Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 160 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------| | An introduction to atoms and elements, compounds, atomic structure and bonding, the molecule and chemical reactions. | Replace another in a molecule happens to atoms during a substitution reaction. | | Wavelength The distance between two consecutive points on a sinusoidal wave that are in phase; | Wavelength is the distance between two corresponding points of adjacent waves called. | | humans normally have 23 pairs of chromosomes. | Humans typically have 23 pairs pairs of chromosomes. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### xsum-pairs * Dataset: [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) at [788ddaf](https://huggingface.co/datasets/sentence-transformers/xsum/tree/788ddafe04e539956d56b567bc32a036ee7b9206) * Size: 160 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------| | Lily the kid became unwell when she was three weeks old and was taken inside to be looked after.
Her owner Rebecca Mineards hopes Lily, who has to wear nappies in the house, will soon be able to return to her mother.
Meanwhile, she is learning to play with the household pups and even wrestling their toys away from them. | A sickly baby goat being nursed back to health in Northamptonshire has started behaving like the dogs she shares a home with. | | It was spotted at Antrim Grammar School on Tuesday morning.
"Several attempts were made to safely and humanely manage the animal," a PSNI spokesperson said.
"Regrettably, after consultation with the vet working with officers at the scene, we were required to shoot the stag as it had become very agitated and posed a risk to the public."
The office of the Police Ombudsman has been informed.
Greg Kayne, chairman of the British Deer Society in Northern Ireland, told the BBC: "The professionals on the ground would have had to make a risk assessment and that risk assessment would have been focused on public safety.
"Unpalatable though the outcome was for the deer, it sounds as though they had few if any options other than to do what they actually had to do."
School principal Hilary Woods said the incident had upset some pupils.
"We had to basically keep the school in lockdown until the situation was resolved, and there were a number of pupils who were obviously very distressed when they heard about the final outcome," she said.
"It actually ran past some of the pupils when they were outside.
"It could have caused damage and it would have been far worse for me as a principal to deal with, if a child or a member of the public had been injured."
Pupil Jordan McKelvey, who is 17, said: "I just saw the deer trapped and a lot of people and hearing the gunshots and it was quite distracting in class.
"It was quite sad and distressing to see that and hear it." | A police armed response unit has shot dead a large wild stag on the grounds of a school in County Antrim. | | Rubble fell down the 30m-high (100ft) cliff in East Cliff on 24 April and partially engulfed the Edwardian funicular carriages.
Bournemouth Borough Council said the carriages had been removed but work to stabilise the slope was continuing.
Environment boss Larry Austin said "all options" were being looked at for reinstating the lift.
He said: "We remain committed to the future of a cliff lift. However, it will be some time before these assessments are made and any necessary stabilisation work commences. The cliff lift will not reopen this summer.
"Our next step after the removal of the final debris is a thorough assessment of the site to consider the next course of action."
An abseiling team was used to secure the carriages of the East Cliff Lift to the running rails ahead of their removal.
A temporary road closure remains in place along East Overcliff Drive while the work is carried out. | A cliff railway which was damaged by a landslide in Bournemouth will not reopen this summer, a council has said. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 0.8, "kl_temperature": 0.75 } ``` #### compression-pairs * Dataset: [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co/datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90) * Size: 160 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| | Harris Corporation, an international communications and information technology company, has acquired key infrastructure assets of the government business of Core180, Inc. Based in Fairfax, Virginia, Core180 is a telecom network integrator that provides wide area networks and management services to government agencies, large enterprises, telecom carriers and systems integrators. | Harris Corporation acquires infrastructure assets from Core180 | | Road rage is believed to have led to the deadly shooting of a Shreveport man this afternoon. | Road rage could be behind deadly shooting | | Vice President Hamid Ansari will be undertaking a six-day bilateral visit to Turkey starting from Monday that will further strengthen trade and commerce ties between the two countries. | Hamid Ansari to visit Turkey to strengthen trade and commerce ties | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 0.8, "kl_temperature": 0.75 } ``` #### sciq_pairs * Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) * Size: 160 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Transport epithelia that function in maintaining water balance also often function in disposal of what? | | | What is the 'stuff' that all things are made of? | Living things are made of matter. In fact, matter is the “stuff” of which all things are made. Anything that occupies space and has mass is known as matter. Matter, in turn, consists of chemical substances. A chemical substance is a material that has a definite chemical composition. It is also homogeneous, so the same chemical composition is found uniformly throughout the substance. A chemical substance may be an element or a chemical compound. | | What is transmitted that makes up the electromagnetic spectrum? | Electromagnetic radiation is energy transmitted as waves with different wavelengths. This makes up the electromagnetic spectrum. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### qasc_pairs * Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 160 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what uses unsaturated fatty acids to store energy? | Plants use unsaturated fatty acids to store energy.. All plants are of bush type.. bushes use unsaturated fatty acids to store energy | | What happens when faults move? | faulting of rock in Earth 's crust causes earthquakes. Faults, cracks in the Earth's crust, produce earthquakes when they move or slip.. Earthquakes happen when faults move. | | ats and proteins can be used for energy by the cells of what? | Glucose is used for energy by the cells of most organisms.. After hours of no glucose ingestion, fats and proteins can be used for energy.. fats and proteins can be used for energy by the cells of most organisms | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### qasc_facts_sym * Dataset: [qasc_facts_sym](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 160 evaluation samples * Columns: combinedfact and facts * Approximate statistics based on the first 1000 samples: | | combinedfact | facts | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | combinedfact | facts | |:---------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------| | plasma ionizes metal | plasma is formed by electrons separating from atoms in stars. Metal atoms are ionized in an intense plasma.. | | Sharks use gills to breathe | breathing is when a gill converts from oxygen in water into oxygen in blood. Sharks breathe under water.. | | Gases released during the use of fossil fuels threaten the entire planet. | gases released during the use of fossil fuels causes global warming. Global warming threatens the entire planet.. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 0.8, "kl_temperature": 0.75 } ``` #### openbookqa_pairs * Dataset: openbookqa_pairs * Size: 160 evaluation samples * Columns: question and fact * Approximate statistics based on the first 1000 samples: | | question | fact | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | fact | |:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------| | The thermal production of a stove is generically used for | a stove generates heat for cooking usually | | What creates a valley? | a valley is formed by a river flowing | | when it turns day and night on a planet, what cause this? | a planet rotating causes cycles of day and night on that planet | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### msmarco_pairs * Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9) * Size: 160 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | who is jesse james decker | Jessie James Decker. Singer/songwriter/designer Kittenish/fave4 For inquiries beautybloggerjjd@gmail.com www.kittenish.com smarturl.it/LDL. | | what sick day means | sick day noun [C]. › a day for which an ​employee ​receives ​pay while ​absent from ​work because of ​illness. (Definition of sick day from the Cambridge Academic Content Dictionary © Cambridge University Press). | | what is pressors | Antihypotensive agent. An antihypotensive agent, also known as a vasopressor agent or pressor, is any medication that tends to raise reduced blood pressure. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### nq_pairs * Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 160 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what tsar was overthrown in the russian revolution | Russian Revolution The Russian Revolution was a pair of revolutions in Russia in 1917 which dismantled the Tsarist autocracy and led to the rise of the Soviet Union. The Russian Empire collapsed with the abdication of Emperor Nicholas II and the old regime was replaced by a provisional government during the first revolution of February 1917 (March in the Gregorian calendar; the older Julian calendar was in use in Russia at the time). Alongside it arose grassroots community assemblies (called 'soviets') which contended for authority. In the second revolution that October, the Provisional Government was toppled and all power was given to the soviets. | | who was the longest reigning monarch in the history of england | List of monarchs in Britain by length of reign Queen Elizabeth II became the longest-reigning British monarch on 9 September 2015 when she surpassed the reign of her great-great-grandmother Victoria.[1][2] On 6 February 2017 she became the first British monarch to celebrate a sapphire jubilee, commemorating 65 years on the throne. | | where does isle of man tt take place | Isle of Man TT The International Isle of Man TT (Tourist Trophy) Race is an annual motorcycle sport event run on the Isle of Man in May or June of most years since its inaugural race in 1907.[3] | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### trivia_pairs * Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) * Size: 160 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Mr Kitty is the pet cat of which character in the US animated tv series ‘South Park’? | South Park Other Recurring Characters / Characters - TV Tropes Supporting Leader : During the "Imaginationland" trilogy. Took a Level in Badass : He was originally quite meek. Then, starting in "Red Sleigh Down", Jesus kicks all sorts of ass.     Mayor MacDaniels  "My geologist? Now? Tell him the infection is fine and I don't need another check-up." Voiced by: Mary Kay Bergman (1997-1999), Eliza Schneider (1999-2003), April Stewart (2004-present) Debut: "Weight Gain 4000" Corrupt and idiotic mayor of South Park. Bi the Way : She's implied to be in a relationship with Officer Barbrady and slept with Chef, but she's also slept with Mrs. Cartman and was shown looking at a porn magazine of busty women in one episode. Characterization Marches On : Originally she was a lot stupider than most of the other South Park adults, but as they got dumber she's generally become smarter; modern episodes tend to vary on whether she's just as dumb as them or the Only Sane Adult in town. Jerkass : She considers everyone in town a bunch of hillbillies and isn't above trying to convince Barbrady to shoot kids. Took a Level in Kindness : From "Red Hot Catholic Love" and on, he's a Good Shepherd instead of a Fundamentalist .     Officer Barbrady  "Okay, people, move along. Nothing to see here." Voiced by: Trey Parker Debut: "Cartman Gets an Anal Probe" The town cop. Incredibly stupid and unhelpful, at least in the earlier seasons. His squad car says "To Patronise And Annoy". Coinciding with decreased prominence, it seems that he has gotten more competent over the years. A Day in the Limelight : "Naughty Ninjas" gave him more focus and development than he'd received in years. Anti-Hero : However his heart is in the right place. Bad Cop/Incompetent Cop : Incompetent; the mayor can make him believe the people he left to die in jail never came despite the bodies being in front of him. He apparently killed the chicken lover just to show Cartman how you hit someone with a nightstick. Man Child : When he returns to class so he can learn to read he reverts quickly to a child. The Only Believer : The only member of the South Park Police Department who actually cares about justice. Out of Focus : In later seasons when South Park gets an actual police force. Police Are Useless : Sometimes is unable to deal with the problems in the town. Token Good Teammate : He might be an incompetent moron, but Barbrady is often portrayed as the only member of South Park's police force who is neither corrupt nor racist. "Naughty Ninjas" seems to imply he's the only member of the force who joined to protect the town rather than to beat up minorities. Took a Level in Badass : Grew more competent in later seasons, helping to arrest a pharmacist for selling cough syrup to minors in "Quest for Ratings", warned people about the giant hamsters attacking in "Pandemic", attempted to stop the drunken party in "About Last Night" (here he fails because the partiers tip his car over), and in "Medicinal Fried Chicken", helps to take down the illegal KFC cartel, surviving the shootout between the cops and Colonel Sanders' enforcers. He also saves Jimmy's life from a psychotic GEICO representative in "Sponsored Content". Trauma Conga Line : The entirety of "Naughty Ninjas", with him losing his job, being unable to provide for his sick and elderly dog, and ending up on the street and homeless. Vetinari Job Security : He had this in the first few seasons, when he was the only police officer in South Park.     Sergeant Harrison Yates  " JESUS CHRIST MONKEY BALLS!!! We could have made an innocent man go to jail who wasn't black!" Voiced by: Trey Parker Debut: "Christian Rock Hard" An incompetent and corrupt police officer who hates rich black people. Works under the Park County Police Department. Anti-Hero : Beating minorities is part of the policework and why the whole precinct save Barbrady joined in. But he tries getting the police job done. Bad Cop/Incompetent Cop : Abuses minorities, arrests someone for calling for a different type of cock magic and takes hours to figure out an obvious | | Which Roman God is one of the symbols of St Valentine's Day? | The Truth Behind St. Valentine’s Day   Home   Library   Articles     Man’s Holidays   The Truth Behind St. Valentine’s Day St. Valentine’s Day is the world’s “holiday of love.” Since the Bible states that God is love ( I John 4:8 , 16 ), does He approve of the celebration of this day? Does He want His people—true Christians—partaking of the candy and cards, or any customs associated with this day? Save Subscribe When God says He wants you to live life abundantly ( John 10:10 ), does that include celebrating a festive, seemingly harmless holiday like Valentine’s Day? The God who gives us everything—life, food, drink, the ability to think for ourselves, etc.—surely approves of St. Valentine’s Day, the holiday for lovers to exchange gifts—right? Do not be so certain. Do not assume anything. Do not even take this article’s word for it. Go to history books and encyclopedias. Go to the Bible. Then you will know the real truth behind St. Valentine’s Day. And you will know what God expects you to do about it! Valentine’s Past Like Christmas, Easter, Halloween, New Year’s and other holidays of this world, St. Valentine’s Day is another attempt to “whitewash” perverted customs and observances of pagan gods and idols by “Christianizing” them. As innocent and harmless as St. Valentine’s Day may appear, its traditions and customs originate from two of the most sexually perverted pagan festivals of ancient history: Lupercalia and the feast day of Juno Februata. Celebrated on February 15, Lupercalia (known as the “festival of sexual license”) was held by the ancient Romans in honor of Lupercus, god of fertility and husbandry, protector of herds and crops, and a mighty hunter—especially of wolves. The Romans believed that Lupercus would protect Rome from roving bands of wolves, which devoured livestock and people. Assisted by Vestal Virgins, the Luperci (male priests) conducted purification rites by sacrificing goats and a dog in the Lupercal cave on Palatine Hill, where the Romans believed the twins Romulus and Remus had been sheltered and nursed by a she-wolf before they eventually founded Rome. Clothed in loincloths made from sacrificed goats and smeared in their blood, the Luperci would run about Rome, striking women with februa, thongs made from skins of the sacrificed goats. The Luperci believed that the floggings purified women and guaranteed their fertility and ease of childbirth. February derives from februa or “means of purification.” To the Romans, February was also sacred to Juno Februata, the goddess of febris (“fever”) of love, and of women and marriage. On February 14, billets (small pieces of paper, each of which had the name of a teen-aged girl written on it) were put into a container. Teen-aged boys would then choose one billet at random. The boy and the girl whose name was drawn would become a “couple,” joining in erotic games at feasts and parties celebrated throughout Rome. After the festival, they would remain sexual partners for the rest of the year. This custom was observed in the Roman Empire for centuries. Whitewashing Perversion In A.D. 494, Pope Gelasius renamed the festival of Juno Februata as the “Feast of the Purification of the Virgin Mary.” The date of its observance was later changed from February 14 to February 2, then changed back to the 14. It is also known as Candlemas, the Presentation of the Lord, the Purification of the Blessed Virgin and the Feast of the Presentation of Christ in the Temple. After Constantine had made the Roman church’s brand of Christianity the official religion of the Roman Empire (A.D. 325), church leaders wanted to do away with the pagan festivals of the people. Lupercalia was high on their list. But the Roman citizens thought otherwise. It was not until A.D. 496 that the church at Rome was able to do anything about Lupercalia. Powerless to get rid of it, Pope Gelasius instead changed it from February 15 to the 14th and called it St. Valentine’s Day. It was named after one of that church’s saints, who, in A.D. 270, was executed by the emperor for his beliefs. According to th | | 46664 was the prison number of which famous political figure? | Nelson Mandela Fast Facts - CNN.com Nelson Mandela Fast Facts CNN Library Updated 8:13 PM ET, Tue September 22, 2015 Chat with us in Facebook Messenger. Find out what's happening in the world as it unfolds. Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – Nelson Mandela, the prisoner-turned-president who reconciled South Africa after the end of apartheid, died on December 5, 2013. He was 95. Hide Caption 1 of 31 Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – Mandela became president of the African National Congress Youth League in 1951. Hide Caption Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – Mandela poses for a photo, circa 1950. Hide Caption Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – Mandela poses in boxing gloves in 1952. Hide Caption 4 of 31 Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – Mandela in the office of Mandela & Tambo, a law practice set up in Johannesburg by Mandela and Oliver Tambo to provide free or affordable legal representation to black South Africans. Hide Caption 5 of 31 Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – From left: Patrick Molaoa, Robert Resha and Mandela walk to the courtroom for their treason trial in Johannesburg. Hide Caption 6 of 31 Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – Mandela married his second wife, social worker Winnie Madikizela, in 1958. At the time, he was an active member of the African National Congress and had begun his lifelong commitment to ending segregation in South Africa. Hide Caption 7 of 31 Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – Nelson and Winnie Mandela raise their fists to salute a cheering crowd upon his 1990 release from Victor Verster Prison. He was still as upright and proud, he would say, as the day he walked into prison 27 years before. Hide Caption 8 of 31 Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – A jubilant South African holds up a newspaper announcing Mandela's release from prison at an ANC rally in Soweto on February 11, 1990. Two days later, more than 100,000 people attended a rally celebrating his release from jail. Hide Caption 9 of 31 Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – Mandela and Zambian President Kenneth Kaunda arrive at an ANC rally on March 3, 1990, in Lusaka, Zambia. Mandela was elected president of the ANC the next year. Hide Caption 10 of 31 Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – After his release in 1990, Mandela embarked on a world tour, meeting U.S. President George H.W. Bush at the White House in June. Hide Caption 11 of 31 Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – At his Soweto home on July 18, 1990, Mandela blows out the candles on his 72nd birthday cake. It was the first birthday he celebrated as a free man since the 1960s. Hide Caption 12 of 31 Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – Mandela and his wife react to supporters during a visit to Brazil at the governor's palace in Rio De Janeiro, on August 1, 1991. Hide Caption 13 of 31 Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – South African President Frederik de Klerk, right, and Mandela shared a Nobel Peace Prize in 1993 for their work to secure a peaceful transition from apartheid rule. Hide Caption 14 of 31 Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – Mandela votes for the first time in his life on March 26, 1994. Hide Caption 15 of 31 Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – On April 27, 1994, a long line of people snake toward a polling station in the black township of Soweto outside of Johannesburg in the nation's first all-race elections. Hide Caption 16 of 31 Photos: The evolution of Nelson Mandela The evolution of Nelson Mandela – Mandela in Mmabatho for an election rally on March 15, | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### quora_pairs * Dataset: [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) * Size: 675 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------|:--------------------------------------------------------------| | Does a transition matrix have to be square? | Does a transition matrix have to be square? Why? | | What is Triple Talaaq? | How does triple talaq work? | | What does it mean when your period is three days late? | My period is 5 days late, what do I do? | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 3, "last_layer_weight": 0.15, "prior_layers_weight": 2.5, "kl_div_weight": 0.75, "kl_temperature": 0.5 } ``` #### gooaq_pairs * Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 160 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | how to connect your airpods to your playstation 4? | ['Turn on your PS4.', 'Plug the bluetooth adapter into the USB port at the front of your PS4.', 'Put the adapter into pairing mode — how you do this will depend on what type of adapter you have. ... ', 'Press and hold the pairing button on the back of your AirPod charging case to put it into pairing mode as well.'] | | what is the difference between quickbooks iif and qbo? | QuickBooks accounting software is offered as QuickBooks Desktop (QBD) or QuickBooks Online. ... QBO and IIF format are different: QBO (Web Connect) is to import bank transactions, and IIF is more 'low level' import allowing to create various transactions between QuickBooks accounts. | | can you see who viewed your whatsapp status? | A view counter is placed at the bottom of your screen, showing you how many people have watched or looked at your status. You can swipe up on the screen to view a list of contact names who have viewed your Status. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1.75, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### mrpc_pairs * Dataset: [mrpc_pairs](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) * Size: 160 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------| | Mr Annan also warned the US should not use the war on terror as an excuse to suppress " long-cherished freedoms " . | Annan warned that the dangers of extremism after September 11 should not be used as an excuse to suppress " long-cherished " freedoms . | | Citigroup Inc . C.N , the world 's largest financial services company , on Wednesday promoted Marjorie Magner to chairman and chief executive of its global consumer group . | Citigroup ( C ) on Wednesday named Marjorie Magner chairman and chief executive of its colossal global consumer business . | | They were among about 40 people attending the traditional Jewish ceremony colored by some non-traditional touches . | He said about 40 people attended the traditional Jewish ceremony colored by some nontraditional touches . | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 0.8, "kl_temperature": 0.75 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `learning_rate`: 3e-05 - `weight_decay`: 0.0001 - `num_train_epochs`: 5 - `lr_scheduler_type`: cosine_with_restarts - `lr_scheduler_kwargs`: {'num_cycles': 3} - `warmup_ratio`: 0.2 - `save_safetensors`: False - `fp16`: True - `push_to_hub`: True - `hub_model_id`: bobox/DeBERTa-ST-AllLayers-v3.1bis-checkpoints-tmp - `hub_strategy`: all_checkpoints - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 3e-05 - `weight_decay`: 0.0001 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: cosine_with_restarts - `lr_scheduler_kwargs`: {'num_cycles': 3} - `warmup_ratio`: 0.2 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: False - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: True - `resume_from_checkpoint`: None - `hub_model_id`: bobox/DeBERTa-ST-AllLayers-v3.1bis-checkpoints-tmp - `hub_strategy`: all_checkpoints - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | vitaminc-pairs loss | quora pairs loss | qnli-contrastive loss | qasc facts sym loss | xsum-pairs loss | nli-pairs loss | mrpc pairs loss | qasc pairs loss | sciq pairs loss | openbookqa pairs loss | gooaq pairs loss | nq pairs loss | scitail-pairs-qa loss | scitail-pairs-pos loss | msmarco pairs loss | trivia pairs loss | compression-pairs loss | StS-test_spearman_cosine | Vitaminc-test_max_ap | mrpc-test_max_ap | |:------:|:-----:|:-------------:|:-------------------:|:----------------:|:---------------------:|:-------------------:|:---------------:|:--------------:|:---------------:|:---------------:|:---------------:|:---------------------:|:----------------:|:-------------:|:---------------------:|:----------------------:|:------------------:|:-----------------:|:----------------------:|:------------------------:|:--------------------:|:----------------:| | 0 | 0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.8972 | 0.5594 | 0.8571 | | 0.0126 | 65 | 0.4577 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0252 | 130 | 0.4707 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0377 | 195 | 0.5259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0503 | 260 | 0.5501 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0629 | 325 | 0.5089 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0755 | 390 | 0.4816 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0881 | 455 | 0.5822 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1007 | 520 | 0.5686 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1132 | 585 | 0.5686 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1258 | 650 | 0.517 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1384 | 715 | 0.3615 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1510 | 780 | 0.5978 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1636 | 845 | 0.5153 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1762 | 910 | 0.5059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1887 | 975 | 0.5624 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2013 | 1040 | 0.5201 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2139 | 1105 | 0.6127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2265 | 1170 | 0.5333 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2391 | 1235 | 0.494 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2501 | 1292 | - | 5.7698 | 0.2283 | 0.1211 | 0.1645 | 0.3134 | 0.8093 | 0.0461 | 0.1987 | 0.2693 | 1.7182 | 0.4996 | 0.4005 | 0.0755 | 0.3979 | 0.4961 | 0.6545 | 0.0832 | 0.8943 | 0.5651 | 0.8563 | | 0.2516 | 1300 | 0.6236 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2642 | 1365 | 0.4947 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2768 | 1430 | 0.5595 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2894 | 1495 | 0.641 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3020 | 1560 | 0.5188 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3146 | 1625 | 0.4927 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3271 | 1690 | 0.657 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3397 | 1755 | 0.4665 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3523 | 1820 | 0.4645 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3649 | 1885 | 0.5887 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3775 | 1950 | 0.5308 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3901 | 2015 | 0.536 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4026 | 2080 | 0.4841 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4152 | 2145 | 0.6499 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4278 | 2210 | 0.5982 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4404 | 2275 | 0.5281 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4530 | 2340 | 0.6657 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4655 | 2405 | 0.5746 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4781 | 2470 | 0.5853 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4907 | 2535 | 0.5828 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5002 | 2584 | - | 5.8030 | 0.2231 | 0.1561 | 0.1525 | 0.3021 | 0.8056 | 0.0449 | 0.1990 | 0.2838 | 1.7069 | 0.4902 | 0.4163 | 0.0706 | 0.4010 | 0.5150 | 0.6237 | 0.0808 | 0.8965 | 0.5620 | 0.8490 | | 0.5033 | 2600 | 0.4889 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5159 | 2665 | 0.517 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5285 | 2730 | 0.5479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5410 | 2795 | 0.6409 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5536 | 2860 | 0.6137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5662 | 2925 | 0.5947 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5788 | 2990 | 0.514 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5914 | 3055 | 0.5256 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6039 | 3120 | 0.5574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6165 | 3185 | 0.5669 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6291 | 3250 | 0.5185 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6417 | 3315 | 0.4875 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6543 | 3380 | 0.5773 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6669 | 3445 | 0.6102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6794 | 3510 | 0.5882 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6920 | 3575 | 0.5888 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7046 | 3640 | 0.6088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7172 | 3705 | 0.688 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7298 | 3770 | 0.6541 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7424 | 3835 | 0.4526 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7503 | 3876 | - | 5.7060 | 0.2193 | 0.1424 | 0.1448 | 0.3261 | 0.8815 | 0.0442 | 0.2069 | 0.2930 | 1.7119 | 0.5001 | 0.4694 | 0.0674 | 0.3630 | 0.4975 | 0.6990 | 0.0862 | 0.8899 | 0.5696 | 0.8492 | | 0.7549 | 3900 | 0.4466 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7675 | 3965 | 0.6293 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7801 | 4030 | 0.5591 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7927 | 4095 | 0.5702 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8053 | 4160 | 0.4645 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8178 | 4225 | 0.5175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8304 | 4290 | 0.5188 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8430 | 4355 | 0.4908 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8556 | 4420 | 0.5075 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8682 | 4485 | 0.5625 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8808 | 4550 | 0.4952 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8933 | 4615 | 0.5818 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9059 | 4680 | 0.6346 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9185 | 4745 | 0.4971 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9311 | 4810 | 0.4834 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9437 | 4875 | 0.5992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9563 | 4940 | 0.5244 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9688 | 5005 | 0.6051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9814 | 5070 | 0.4323 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9940 | 5135 | 0.5347 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0004 | 5168 | - | 5.7486 | 0.2115 | 0.1502 | 0.1495 | 0.2985 | 0.8807 | 0.0477 | 0.2273 | 0.2317 | 1.7004 | 0.5320 | 0.4479 | 0.0687 | 0.3598 | 0.5557 | 0.7469 | 0.0826 | 0.8905 | 0.5604 | 0.8520 | | 1.0066 | 5200 | 0.4946 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0192 | 5265 | 0.4134 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0317 | 5330 | 0.4702 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0443 | 5395 | 0.5392 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0569 | 5460 | 0.4845 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0695 | 5525 | 0.4601 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0821 | 5590 | 0.5683 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0947 | 5655 | 0.4742 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1072 | 5720 | 0.5187 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1198 | 5785 | 0.4888 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1324 | 5850 | 0.3486 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1450 | 5915 | 0.4714 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1576 | 5980 | 0.5638 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1702 | 6045 | 0.457 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1827 | 6110 | 0.5419 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1953 | 6175 | 0.4478 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2079 | 6240 | 0.5012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2205 | 6305 | 0.4505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2331 | 6370 | 0.4677 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2456 | 6435 | 0.478 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2505 | 6460 | - | 5.8135 | 0.2143 | 0.0903 | 0.1406 | 0.3172 | 0.8716 | 0.0403 | 0.1974 | 0.2574 | 1.6864 | 0.5355 | 0.4532 | 0.0638 | 0.3649 | 0.5885 | 0.8866 | 0.0814 | 0.8912 | 0.5682 | 0.8479 | | 1.2582 | 6500 | 0.5004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2708 | 6565 | 0.5453 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2834 | 6630 | 0.529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2960 | 6695 | 0.4343 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3086 | 6760 | 0.4235 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3211 | 6825 | 0.4493 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3337 | 6890 | 0.4349 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3463 | 6955 | 0.4016 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3589 | 7020 | 0.39 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3715 | 7085 | 0.5162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3840 | 7150 | 0.4186 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3966 | 7215 | 0.3867 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4092 | 7280 | 0.5076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4218 | 7345 | 0.4872 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4344 | 7410 | 0.4508 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4470 | 7475 | 0.4261 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4595 | 7540 | 0.4444 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4721 | 7605 | 0.4043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4847 | 7670 | 0.4451 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4973 | 7735 | 0.4312 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5006 | 7752 | - | 5.7624 | 0.1859 | 0.1609 | 0.1289 | 0.2724 | 0.7820 | 0.0376 | 0.1764 | 0.2523 | 1.6650 | 0.4796 | 0.4191 | 0.0553 | 0.3169 | 0.4905 | 0.6449 | 0.0707 | 0.8955 | 0.5636 | 0.8442 | | 1.5099 | 7800 | 0.3881 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5225 | 7865 | 0.3704 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5350 | 7930 | 0.451 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5476 | 7995 | 0.4553 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5602 | 8060 | 0.411 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5728 | 8125 | 0.446 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5854 | 8190 | 0.2831 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5979 | 8255 | 0.3546 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6105 | 8320 | 0.3713 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6231 | 8385 | 0.4221 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6357 | 8450 | 0.3154 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6483 | 8515 | 0.3379 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6609 | 8580 | 0.388 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6734 | 8645 | 0.3323 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6860 | 8710 | 0.4388 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6986 | 8775 | 0.3223 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7112 | 8840 | 0.3775 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7238 | 8905 | 0.4348 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7364 | 8970 | 0.3758 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7489 | 9035 | 0.2605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7507 | 9044 | - | 5.7163 | 0.1824 | 0.1385 | 0.1195 | 0.2820 | 0.7929 | 0.0338 | 0.1572 | 0.2519 | 1.6055 | 0.4615 | 0.3871 | 0.0498 | 0.2821 | 0.4612 | 0.6729 | 0.0652 | 0.8925 | 0.5657 | 0.8510 | | 1.7615 | 9100 | 0.325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7741 | 9165 | 0.2905 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7867 | 9230 | 0.3873 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7993 | 9295 | 0.2658 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8118 | 9360 | 0.3357 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8244 | 9425 | 0.2736 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8370 | 9490 | 0.2758 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8496 | 9555 | 0.2423 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8622 | 9620 | 0.2992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8748 | 9685 | 0.2551 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8873 | 9750 | 0.3565 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8999 | 9815 | 0.2705 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9125 | 9880 | 0.3363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9251 | 9945 | 0.2787 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9377 | 10010 | 0.273 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9503 | 10075 | 0.264 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9628 | 10140 | 0.3532 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9754 | 10205 | 0.221 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9880 | 10270 | 0.2625 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0006 | 10335 | 0.2808 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0008 | 10336 | - | 5.7618 | 0.1803 | 0.1177 | 0.1171 | 0.2668 | 0.7486 | 0.0301 | 0.1470 | 0.2111 | 1.6116 | 0.4440 | 0.3611 | 0.0442 | 0.3013 | 0.4583 | 0.5992 | 0.0598 | 0.8900 | 0.5627 | 0.8489 | | 2.0132 | 10400 | 0.2003 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0257 | 10465 | 0.216 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0383 | 10530 | 0.2704 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0509 | 10595 | 0.2788 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0635 | 10660 | 0.264 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0761 | 10725 | 0.2625 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0887 | 10790 | 0.3213 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1012 | 10855 | 0.2799 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1138 | 10920 | 0.2719 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1264 | 10985 | 0.2086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1390 | 11050 | 0.208 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1516 | 11115 | 0.3249 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1642 | 11180 | 0.2545 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1767 | 11245 | 0.274 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1893 | 11310 | 0.216 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2019 | 11375 | 0.2467 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2145 | 11440 | 0.2578 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2271 | 11505 | 0.2608 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2396 | 11570 | 0.2476 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2509 | 11628 | - | 5.7284 | 0.1979 | 0.1260 | 0.1164 | 0.2607 | 0.7366 | 0.0300 | 0.1397 | 0.2135 | 1.5971 | 0.4295 | 0.3480 | 0.0444 | 0.2841 | 0.4528 | 0.5698 | 0.0601 | 0.8920 | 0.5638 | 0.8501 | | 2.2522 | 11635 | 0.3296 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2648 | 11700 | 0.2261 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2774 | 11765 | 0.2999 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2900 | 11830 | 0.2888 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3026 | 11895 | 0.2253 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3151 | 11960 | 0.2343 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3277 | 12025 | 0.331 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3403 | 12090 | 0.2101 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3529 | 12155 | 0.2155 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3655 | 12220 | 0.2996 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3780 | 12285 | 0.2964 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3906 | 12350 | 0.2825 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4032 | 12415 | 0.2259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4158 | 12480 | 0.373 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4284 | 12545 | 0.2906 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4410 | 12610 | 0.295 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4535 | 12675 | 0.3156 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4661 | 12740 | 0.3068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4787 | 12805 | 0.3137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4913 | 12870 | 0.2985 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5010 | 12920 | - | 6.0874 | 0.1622 | 0.1799 | 0.1253 | 0.2761 | 0.8446 | 0.0332 | 0.1480 | 0.2507 | 1.6857 | 0.4601 | 0.4049 | 0.0485 | 0.3115 | 0.5118 | 0.6597 | 0.0625 | 0.8946 | 0.5675 | 0.8489 | | 2.5039 | 12935 | 0.2487 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5165 | 13000 | 0.2624 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5290 | 13065 | 0.2677 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5416 | 13130 | 0.3675 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5542 | 13195 | 0.2948 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5668 | 13260 | 0.2964 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5794 | 13325 | 0.2434 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5919 | 13390 | 0.2722 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6045 | 13455 | 0.3233 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6171 | 13520 | 0.2968 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6297 | 13585 | 0.3172 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6423 | 13650 | 0.2398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6549 | 13715 | 0.3085 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6674 | 13780 | 0.2809 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6800 | 13845 | 0.3496 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6926 | 13910 | 0.2281 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7052 | 13975 | 0.3075 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7178 | 14040 | 0.3335 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7304 | 14105 | 0.3423 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7429 | 14170 | 0.2579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7511 | 14212 | - | 5.7951 | 0.1943 | 0.1599 | 0.1175 | 0.2769 | 0.8535 | 0.0300 | 0.1549 | 0.2465 | 1.6479 | 0.4621 | 0.4197 | 0.0445 | 0.2758 | 0.4515 | 0.7053 | 0.0607 | 0.8947 | 0.5634 | 0.8448 | | 2.7555 | 14235 | 0.2323 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7681 | 14300 | 0.2952 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7807 | 14365 | 0.2626 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7933 | 14430 | 0.2741 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8058 | 14495 | 0.2424 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8184 | 14560 | 0.2544 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8310 | 14625 | 0.2889 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8436 | 14690 | 0.2222 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8562 | 14755 | 0.2335 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8688 | 14820 | 0.2741 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8813 | 14885 | 0.2814 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8939 | 14950 | 0.2493 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9065 | 15015 | 0.3201 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9191 | 15080 | 0.3087 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9317 | 15145 | 0.2326 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9443 | 15210 | 0.335 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9568 | 15275 | 0.2784 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9694 | 15340 | 0.2806 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9820 | 15405 | 0.185 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9946 | 15470 | 0.2096 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.0012 | 15504 | - | 5.9431 | 0.1751 | 0.1348 | 0.1181 | 0.2738 | 0.7936 | 0.0272 | 0.1360 | 0.2057 | 1.5949 | 0.4264 | 0.3879 | 0.0397 | 0.2736 | 0.4805 | 0.6310 | 0.0558 | 0.8911 | 0.5526 | 0.8483 | | 3.0072 | 15535 | 0.2311 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.0197 | 15600 | 0.1838 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.0323 | 15665 | 0.2076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.0449 | 15730 | 0.2866 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.0575 | 15795 | 0.2769 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.0701 | 15860 | 0.2398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.0827 | 15925 | 0.3044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.0952 | 15990 | 0.261 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.1078 | 16055 | 0.2806 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.1204 | 16120 | 0.2093 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.1330 | 16185 | 0.1835 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.1456 | 16250 | 0.2615 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.1581 | 16315 | 0.3112 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.1707 | 16380 | 0.2247 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.1833 | 16445 | 0.2808 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.1959 | 16510 | 0.2256 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.2085 | 16575 | 0.266 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.2211 | 16640 | 0.2396 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.2336 | 16705 | 0.2493 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.2462 | 16770 | 0.2642 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.2513 | 16796 | - | 6.0016 | 0.1697 | 0.1143 | 0.1166 | 0.2524 | 0.7502 | 0.0258 | 0.1279 | 0.2148 | 1.5522 | 0.3950 | 0.3408 | 0.0387 | 0.2691 | 0.4553 | 0.6150 | 0.0537 | 0.8965 | 0.5597 | 0.8471 | | 3.2588 | 16835 | 0.2925 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.2714 | 16900 | 0.2983 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.2840 | 16965 | 0.2797 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.2966 | 17030 | 0.2351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.3091 | 17095 | 0.2431 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.3217 | 17160 | 0.2406 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.3343 | 17225 | 0.2354 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.3469 | 17290 | 0.1614 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.3595 | 17355 | 0.1372 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.3720 | 17420 | 0.2192 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.3846 | 17485 | 0.1628 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.3972 | 17550 | 0.1496 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.4098 | 17615 | 0.1813 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.4224 | 17680 | 0.1914 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.4350 | 17745 | 0.196 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.4475 | 17810 | 0.1858 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.4601 | 17875 | 0.19 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.4727 | 17940 | 0.166 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.4853 | 18005 | 0.1681 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.4979 | 18070 | 0.1615 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.5014 | 18088 | - | 6.0385 | 0.1907 | 0.1368 | 0.1117 | 0.2433 | 0.7530 | 0.0241 | 0.1192 | 0.2145 | 1.5496 | 0.4085 | 0.3250 | 0.0359 | 0.2776 | 0.4487 | 0.5620 | 0.0519 | 0.8969 | 0.5599 | 0.8465 | | 3.5105 | 18135 | 0.136 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.5230 | 18200 | 0.1507 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.5356 | 18265 | 0.207 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.5482 | 18330 | 0.2192 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.5608 | 18395 | 0.1574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.5734 | 18460 | 0.1859 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.5859 | 18525 | 0.13 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.5985 | 18590 | 0.1555 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.6111 | 18655 | 0.2212 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.6237 | 18720 | 0.1647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.6363 | 18785 | 0.1529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.6489 | 18850 | 0.1512 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.6614 | 18915 | 0.1788 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.6740 | 18980 | 0.184 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.6866 | 19045 | 0.2365 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.6992 | 19110 | 0.1695 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.7118 | 19175 | 0.2396 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.7244 | 19240 | 0.232 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.7369 | 19305 | 0.2263 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.7495 | 19370 | 0.1583 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.7515 | 19380 | - | 6.0426 | 0.1694 | 0.1481 | 0.1098 | 0.2730 | 0.8451 | 0.0255 | 0.1368 | 0.2253 | 1.6146 | 0.4429 | 0.3488 | 0.0374 | 0.2775 | 0.5042 | 0.6838 | 0.0556 | 0.8881 | 0.5598 | 0.8454 | | 3.7621 | 19435 | 0.1775 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.7747 | 19500 | 0.179 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.7873 | 19565 | 0.225 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.7998 | 19630 | 0.168 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.8124 | 19695 | 0.2078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.8250 | 19760 | 0.1772 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.8376 | 19825 | 0.1729 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.8502 | 19890 | 0.1674 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.8628 | 19955 | 0.1964 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.8753 | 20020 | 0.1475 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.8879 | 20085 | 0.2376 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.9005 | 20150 | 0.1967 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.9131 | 20215 | 0.238 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.9257 | 20280 | 0.2088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.9383 | 20345 | 0.2234 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.9508 | 20410 | 0.2084 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.9634 | 20475 | 0.276 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.9760 | 20540 | 0.1641 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.9886 | 20605 | 0.1624 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.0012 | 20670 | 0.2049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.0015 | 20672 | - | 5.7577 | 0.1443 | 0.1427 | 0.1228 | 0.2700 | 0.8549 | 0.0258 | 0.1470 | 0.2269 | 1.6409 | 0.3750 | 0.4072 | 0.0371 | 0.2630 | 0.5083 | 0.6768 | 0.0505 | 0.8938 | 0.5538 | 0.8453 | | 4.0137 | 20735 | 0.1442 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.0263 | 20800 | 0.1301 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.0389 | 20865 | 0.2229 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.0515 | 20930 | 0.2103 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.0641 | 20995 | 0.2235 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.0767 | 21060 | 0.2242 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.0892 | 21125 | 0.2803 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.1018 | 21190 | 0.2094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.1144 | 21255 | 0.2196 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.1270 | 21320 | 0.1926 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.1396 | 21385 | 0.1866 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.1521 | 21450 | 0.2873 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.1647 | 21515 | 0.2281 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.1773 | 21580 | 0.2539 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.1899 | 21645 | 0.2232 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.2025 | 21710 | 0.2111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.2151 | 21775 | 0.2502 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.2276 | 21840 | 0.2432 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.2402 | 21905 | 0.2261 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.2516 | 21964 | - | 5.9666 | 0.1697 | 0.1052 | 0.1139 | 0.2659 | 0.7817 | 0.0236 | 0.1282 | 0.2212 | 1.6212 | 0.4113 | 0.3616 | 0.0358 | 0.2494 | 0.5530 | 0.6664 | 0.0520 | 0.8918 | 0.5569 | 0.8472 | | 4.2528 | 21970 | 0.2843 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.2654 | 22035 | 0.2221 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.2780 | 22100 | 0.3073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.2906 | 22165 | 0.2695 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.3031 | 22230 | 0.2286 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.3157 | 22295 | 0.2236 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.3283 | 22360 | 0.311 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.3409 | 22425 | 0.1523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.3535 | 22490 | 0.137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.3660 | 22555 | 0.1888 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.3786 | 22620 | 0.1886 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.3912 | 22685 | 0.1539 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.4038 | 22750 | 0.1141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.4164 | 22815 | 0.2414 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.4290 | 22880 | 0.1844 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.4415 | 22945 | 0.1882 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.4541 | 23010 | 0.2267 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.4667 | 23075 | 0.1586 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.4793 | 23140 | 0.1833 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.4919 | 23205 | 0.1505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.5017 | 23256 | - | 6.0877 | 0.1703 | 0.1650 | 0.1067 | 0.2550 | 0.7659 | 0.0215 | 0.1127 | 0.2174 | 1.5698 | 0.3639 | 0.3551 | 0.0312 | 0.2663 | 0.4660 | 0.5573 | 0.0467 | 0.8969 | 0.5594 | 0.8389 | | 4.5045 | 23270 | 0.1539 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.5170 | 23335 | 0.1661 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.5296 | 23400 | 0.1719 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.5422 | 23465 | 0.2357 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.5548 | 23530 | 0.1764 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.5674 | 23595 | 0.1741 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.5799 | 23660 | 0.1251 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.5925 | 23725 | 0.1734 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.6051 | 23790 | 0.1539 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.6177 | 23855 | 0.1804 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.6303 | 23920 | 0.1678 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.6429 | 23985 | 0.1526 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.6554 | 24050 | 0.1954 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.6680 | 24115 | 0.1876 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.6806 | 24180 | 0.1567 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.6932 | 24245 | 0.0897 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.7058 | 24310 | 0.1338 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.7184 | 24375 | 0.1611 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.7309 | 24440 | 0.1611 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.7435 | 24505 | 0.1218 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.7518 | 24548 | - | 6.0147 | 0.1762 | 0.1470 | 0.1034 | 0.2599 | 0.7503 | 0.0198 | 0.1106 | 0.2139 | 1.5396 | 0.3562 | 0.3512 | 0.0285 | 0.2434 | 0.4512 | 0.5726 | 0.0455 | 0.8950 | 0.5587 | 0.8428 | | 4.7561 | 24570 | 0.0839 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.7687 | 24635 | 0.1318 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.7813 | 24700 | 0.129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.7938 | 24765 | 0.1326 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.8064 | 24830 | 0.103 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.8190 | 24895 | 0.1251 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.8316 | 24960 | 0.115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.8442 | 25025 | 0.1023 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.8568 | 25090 | 0.1003 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.8693 | 25155 | 0.1094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.8819 | 25220 | 0.133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.8945 | 25285 | 0.1164 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.9071 | 25350 | 0.1303 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.9197 | 25415 | 0.1446 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.9322 | 25480 | 0.1189 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.9448 | 25545 | 0.1272 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.9574 | 25610 | 0.1283 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.9700 | 25675 | 0.1473 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.9826 | 25740 | 0.0909 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 4.9952 | 25805 | 0.1042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 5.0 | 25830 | - | 5.9783 | 0.1599 | 0.1411 | 0.1035 | 0.2591 | 0.7474 | 0.0195 | 0.1075 | 0.2129 | 1.5411 | 0.3557 | 0.3465 | 0.0282 | 0.2445 | 0.4601 | 0.5563 | 0.0440 | 0.8948 | 0.5583 | 0.8423 |
### Framework Versions - Python: 3.10.13 - Sentence Transformers: 3.0.1 - Transformers: 4.42.3 - PyTorch: 2.1.2 - Accelerate: 0.32.1 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### AdaptiveLayerLoss ```bibtex @misc{li20242d, title={2D Matryoshka Sentence Embeddings}, author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li}, year={2024}, eprint={2402.14776}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` #### GISTEmbedLoss ```bibtex @misc{solatorio2024gistembed, title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, author={Aivin V. Solatorio}, year={2024}, eprint={2402.16829}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```