--- base_model: Alibaba-NLP/gte-base-en-v1.5 language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:32833 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: Anonymity in online interactions can lead to a disinhibition effect, where individuals feel free to express hostile or aggressive opinions they might otherwise suppress. sentences: - What are the implications of anonymity in online interactions? - How does creativity function as a form of costly signalling in personal expressions such as invitations? - Why is conflict considered essential in a creative organization? - source_sentence: The author decides to release their novel into the world despite its imperfections, and finds that this allows them to move on to new projects and experiences, and to focus on the value of the work itself rather than its flaws. sentences: - How does the author's experience with their novel illustrate the concept of 'embracing imperfection' in creative work? - What does the author mean by 'ambitious programmers are better off doing their own thing'? - What is the role of 'show me' in the design process? - source_sentence: Tokens become more valuable as more users adopt them, creating a positive feedback loop that enhances their utility and encourages further adoption across various applications. sentences: - In what ways do tokens exhibit network effects? - What can sometimes be found when considering a startup with a lame-sounding idea? - How do social norms influence decision-making in the context of airport choices? - source_sentence: Philosophers are often viewed as the guardians of critical thinking; however, their reliance on bureaucratic structures and abstract discussions can become problematic. Instead of fostering open-mindedness, they may perpetuate dogmatic thinking and limit the exploration of diverse perspectives, thereby failing to fulfill their duty of promoting genuine critical engagement. sentences: - In what ways can the role of philosophers be seen as essential or problematic within the context of critical thinking? - How does the evolution of pair-bonding facilitate cultural exchange between groups? - What is the role of autonomy in the success of acquired startups? - source_sentence: Society tends to admire those who despair when others hope, viewing them as sages or wise figures. sentences: - What is often the societal perception of those who express pessimism about the future? - How did the realization about user engagement influence the app development strategy? - What lessons can be learned from the historical context of employee relations in large corporations? model-index: - name: Alchemy Embedding - Anudit Nagar results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.782012613106663 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8889498217713189 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9248697559638058 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9520153550863724 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.782012613106663 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.29631660725710623 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1849739511927612 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09520153550863725 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.782012613106663 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8889498217713189 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9248697559638058 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9520153550863724 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.867555587052628 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8402608580220322 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8422322227138224 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.780367425281053 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8848368522072937 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9221277762544557 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9514669591445023 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.780367425281053 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2949456174024312 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1844255552508912 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09514669591445023 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.780367425281053 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8848368522072937 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9221277762544557 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9514669591445023 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8661558392165704 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.838656038231032 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8405372438205077 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.7754318618042226 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8804496846723334 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9169180148066904 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9468055936386071 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7754318618042226 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2934832282241111 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18338360296133807 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09468055936386072 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7754318618042226 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8804496846723334 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9169180148066904 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9468055936386071 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8613819477350178 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8338379881703168 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8360735900013385 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.7617219632574719 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.871675349602413 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9117082533589251 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9418700301617768 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7617219632574719 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2905584498674709 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18234165067178504 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09418700301617768 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7617219632574719 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.871675349602413 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9117082533589251 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9418700301617768 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.851649908463093 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8225671458602635 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8248455884524328 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.7408829174664108 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.853852481491637 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8936111872772141 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9292569234987661 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7408829174664108 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28461749383054563 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17872223745544283 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0929256923498766 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7408829174664108 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.853852481491637 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8936111872772141 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9292569234987661 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8338956659320366 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8033378162525404 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8057702637208689 name: Cosine Map@100 --- # Alchemy Embedding - Anudit Nagar This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) on the json dataset. 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:** [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json - **Language:** en - **License:** apache-2.0 ### 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': 8192, 'do_lower_case': False}) with Transformer model: NewModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("sentence_transformers_model_id") # Run inference sentences = [ 'Society tends to admire those who despair when others hope, viewing them as sages or wise figures.', 'What is often the societal perception of those who express pessimism about the future?', 'How did the realization about user engagement influence the app development strategy?', ] 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 #### Information Retrieval * Dataset: `dim_768` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.782 | | cosine_accuracy@3 | 0.8889 | | cosine_accuracy@5 | 0.9249 | | cosine_accuracy@10 | 0.952 | | cosine_precision@1 | 0.782 | | cosine_precision@3 | 0.2963 | | cosine_precision@5 | 0.185 | | cosine_precision@10 | 0.0952 | | cosine_recall@1 | 0.782 | | cosine_recall@3 | 0.8889 | | cosine_recall@5 | 0.9249 | | cosine_recall@10 | 0.952 | | cosine_ndcg@10 | 0.8676 | | cosine_mrr@10 | 0.8403 | | **cosine_map@100** | **0.8422** | #### Information Retrieval * Dataset: `dim_512` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.7804 | | cosine_accuracy@3 | 0.8848 | | cosine_accuracy@5 | 0.9221 | | cosine_accuracy@10 | 0.9515 | | cosine_precision@1 | 0.7804 | | cosine_precision@3 | 0.2949 | | cosine_precision@5 | 0.1844 | | cosine_precision@10 | 0.0951 | | cosine_recall@1 | 0.7804 | | cosine_recall@3 | 0.8848 | | cosine_recall@5 | 0.9221 | | cosine_recall@10 | 0.9515 | | cosine_ndcg@10 | 0.8662 | | cosine_mrr@10 | 0.8387 | | **cosine_map@100** | **0.8405** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.7754 | | cosine_accuracy@3 | 0.8804 | | cosine_accuracy@5 | 0.9169 | | cosine_accuracy@10 | 0.9468 | | cosine_precision@1 | 0.7754 | | cosine_precision@3 | 0.2935 | | cosine_precision@5 | 0.1834 | | cosine_precision@10 | 0.0947 | | cosine_recall@1 | 0.7754 | | cosine_recall@3 | 0.8804 | | cosine_recall@5 | 0.9169 | | cosine_recall@10 | 0.9468 | | cosine_ndcg@10 | 0.8614 | | cosine_mrr@10 | 0.8338 | | **cosine_map@100** | **0.8361** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.7617 | | cosine_accuracy@3 | 0.8717 | | cosine_accuracy@5 | 0.9117 | | cosine_accuracy@10 | 0.9419 | | cosine_precision@1 | 0.7617 | | cosine_precision@3 | 0.2906 | | cosine_precision@5 | 0.1823 | | cosine_precision@10 | 0.0942 | | cosine_recall@1 | 0.7617 | | cosine_recall@3 | 0.8717 | | cosine_recall@5 | 0.9117 | | cosine_recall@10 | 0.9419 | | cosine_ndcg@10 | 0.8516 | | cosine_mrr@10 | 0.8226 | | **cosine_map@100** | **0.8248** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.7409 | | cosine_accuracy@3 | 0.8539 | | cosine_accuracy@5 | 0.8936 | | cosine_accuracy@10 | 0.9293 | | cosine_precision@1 | 0.7409 | | cosine_precision@3 | 0.2846 | | cosine_precision@5 | 0.1787 | | cosine_precision@10 | 0.0929 | | cosine_recall@1 | 0.7409 | | cosine_recall@3 | 0.8539 | | cosine_recall@5 | 0.8936 | | cosine_recall@10 | 0.9293 | | cosine_ndcg@10 | 0.8339 | | cosine_mrr@10 | 0.8033 | | **cosine_map@100** | **0.8058** | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 32,833 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------| | The author saw taking risks as a necessary part of the creative process, and was willing to take risks in order to explore new ideas and themes. | What was the author's perspective on the importance of taking risks in creative work? | | Recognizing that older users are less likely to invite new users led to a strategic focus on younger demographics, prompting a shift in development efforts toward creating products that resonate with teens. | How did the realization about user engagement influence the app development strategy? | | The phrase emphasizes the fragility of Earth and our collective responsibility to protect it and ensure sustainable resource management for future generations. | What is the significance of the phrase 'pale blue dot' in relation to environmental responsibility? | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 24 - `per_device_eval_batch_size`: 24 - `gradient_accumulation_steps`: 8 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 24 - `per_device_eval_batch_size`: 24 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 8 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `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`: True - `fp16`: False - `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`: True - `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`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `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 - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |:----------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.0584 | 10 | 0.8567 | - | - | - | - | - | | 0.1169 | 20 | 0.6549 | - | - | - | - | - | | 0.1753 | 30 | 0.5407 | - | - | - | - | - | | 0.2337 | 40 | 0.4586 | - | - | - | - | - | | 0.2922 | 50 | 0.3914 | - | - | - | - | - | | 0.3506 | 60 | 0.4104 | - | - | - | - | - | | 0.4091 | 70 | 0.299 | - | - | - | - | - | | 0.4675 | 80 | 0.2444 | - | - | - | - | - | | 0.5259 | 90 | 0.2367 | - | - | - | - | - | | 0.5844 | 100 | 0.2302 | - | - | - | - | - | | 0.6428 | 110 | 0.2356 | - | - | - | - | - | | 0.7012 | 120 | 0.1537 | - | - | - | - | - | | 0.7597 | 130 | 0.2043 | - | - | - | - | - | | 0.8181 | 140 | 0.1606 | - | - | - | - | - | | 0.8766 | 150 | 0.1896 | - | - | - | - | - | | 0.9350 | 160 | 0.1766 | - | - | - | - | - | | 0.9934 | 170 | 0.1259 | - | - | - | - | - | | 0.9993 | 171 | - | 0.8115 | 0.8233 | 0.8321 | 0.7829 | 0.8340 | | 1.0519 | 180 | 0.1661 | - | - | - | - | - | | 1.1103 | 190 | 0.1632 | - | - | - | - | - | | 1.1687 | 200 | 0.1032 | - | - | - | - | - | | 1.2272 | 210 | 0.1037 | - | - | - | - | - | | 1.2856 | 220 | 0.0708 | - | - | - | - | - | | 1.3440 | 230 | 0.0827 | - | - | - | - | - | | 1.4025 | 240 | 0.0505 | - | - | - | - | - | | 1.4609 | 250 | 0.0468 | - | - | - | - | - | | 1.5194 | 260 | 0.0371 | - | - | - | - | - | | 1.5778 | 270 | 0.049 | - | - | - | - | - | | 1.6362 | 280 | 0.0527 | - | - | - | - | - | | 1.6947 | 290 | 0.0316 | - | - | - | - | - | | 1.7531 | 300 | 0.052 | - | - | - | - | - | | 1.8115 | 310 | 0.0298 | - | - | - | - | - | | 1.8700 | 320 | 0.0334 | - | - | - | - | - | | 1.9284 | 330 | 0.0431 | - | - | - | - | - | | 1.9869 | 340 | 0.0316 | - | - | - | - | - | | 1.9985 | 342 | - | 0.8216 | 0.8342 | 0.8397 | 0.8006 | 0.8408 | | 2.0453 | 350 | 0.0275 | - | - | - | - | - | | 2.1037 | 360 | 0.0461 | - | - | - | - | - | | 2.1622 | 370 | 0.0341 | - | - | - | - | - | | 2.2206 | 380 | 0.0323 | - | - | - | - | - | | 2.2790 | 390 | 0.0205 | - | - | - | - | - | | 2.3375 | 400 | 0.0223 | - | - | - | - | - | | 2.3959 | 410 | 0.0189 | - | - | - | - | - | | 2.4543 | 420 | 0.0181 | - | - | - | - | - | | 2.5128 | 430 | 0.0144 | - | - | - | - | - | | 2.5712 | 440 | 0.0179 | - | - | - | - | - | | 2.6297 | 450 | 0.0217 | - | - | - | - | - | | 2.6881 | 460 | 0.016 | - | - | - | - | - | | 2.7465 | 470 | 0.0143 | - | - | - | - | - | | 2.8050 | 480 | 0.0193 | - | - | - | - | - | | 2.8634 | 490 | 0.0183 | - | - | - | - | - | | 2.9218 | 500 | 0.0171 | - | - | - | - | - | | 2.9803 | 510 | 0.0195 | - | - | - | - | - | | 2.9978 | 513 | - | 0.8242 | 0.8350 | 0.8409 | 0.8051 | 0.8413 | | 3.0387 | 520 | 0.0127 | - | - | - | - | - | | 3.0972 | 530 | 0.0261 | - | - | - | - | - | | 3.1556 | 540 | 0.017 | - | - | - | - | - | | 3.2140 | 550 | 0.0198 | - | - | - | - | - | | 3.2725 | 560 | 0.0131 | - | - | - | - | - | | 3.3309 | 570 | 0.0156 | - | - | - | - | - | | 3.3893 | 580 | 0.0107 | - | - | - | - | - | | 3.4478 | 590 | 0.0123 | - | - | - | - | - | | 3.5062 | 600 | 0.0111 | - | - | - | - | - | | 3.5646 | 610 | 0.0112 | - | - | - | - | - | | 3.6231 | 620 | 0.0143 | - | - | - | - | - | | 3.6815 | 630 | 0.013 | - | - | - | - | - | | 3.7400 | 640 | 0.0105 | - | - | - | - | - | | 3.7984 | 650 | 0.0126 | - | - | - | - | - | | 3.8568 | 660 | 0.0118 | - | - | - | - | - | | 3.9153 | 670 | 0.0163 | - | - | - | - | - | | 3.9737 | 680 | 0.0187 | - | - | - | - | - | | **3.9971** | **684** | **-** | **0.8248** | **0.8361** | **0.8405** | **0.8058** | **0.8422** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.12.5 - Sentence Transformers: 3.1.1 - Transformers: 4.44.2 - PyTorch: 2.4.1 - Accelerate: 0.33.0 - Datasets: 2.21.0 - 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", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, 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} } ```