--- base_model: Alibaba-NLP/gte-base-en-v1.5 datasets: [] 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: Custom Embedding Test - Anudit Nagar results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.7683027145599123 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8755141211955032 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9097888675623801 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9465313956676721 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7683027145599123 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.29183804039850103 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18195777351247602 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09465313956676721 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7683027145599123 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8755141211955032 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9097888675623801 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9465313956676721 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8566925927271383 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8279207524340517 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8302321946792381 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.762818755141212 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8700301617768028 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9062242939402249 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.946257197696737 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.762818755141212 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2900100539256009 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18124485878804497 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09462571976967371 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.762818755141212 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8700301617768028 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9062242939402249 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.946257197696737 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8529743473843932 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8231949721667308 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.825407004380477 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.762818755141212 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8683849739511927 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9015629284343296 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9418700301617768 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.762818755141212 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28946165798373086 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18031258568686592 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09418700301617768 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.762818755141212 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8683849739511927 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9015629284343296 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9418700301617768 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.850685453111757 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8215859088357048 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8239714751253995 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.7573347957225116 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8634494104743625 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8952563751028242 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9347408829174664 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7573347957225116 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2878164701581208 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17905127502056484 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09347408829174664 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7573347957225116 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8634494104743625 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8952563751028242 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9347408829174664 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8445055968214926 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8157123053956075 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8184088689781863 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.7419797093501508 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8530298875788319 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8859336440910337 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9284343295859611 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7419797093501508 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28434329585961066 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17718672881820677 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09284343295859611 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7419797093501508 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8530298875788319 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8859336440910337 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9284343295859611 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8334906130922063 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8032139919307455 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8057146368194794 name: Cosine Map@100 --- # Custom Embedding Test - 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). 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 - **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 | 0.7683 | | cosine_accuracy@3 | 0.8755 | | cosine_accuracy@5 | 0.9098 | | cosine_accuracy@10 | 0.9465 | | cosine_precision@1 | 0.7683 | | cosine_precision@3 | 0.2918 | | cosine_precision@5 | 0.182 | | cosine_precision@10 | 0.0947 | | cosine_recall@1 | 0.7683 | | cosine_recall@3 | 0.8755 | | cosine_recall@5 | 0.9098 | | cosine_recall@10 | 0.9465 | | cosine_ndcg@10 | 0.8567 | | cosine_mrr@10 | 0.8279 | | **cosine_map@100** | **0.8302** | #### 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 | 0.7628 | | cosine_accuracy@3 | 0.87 | | cosine_accuracy@5 | 0.9062 | | cosine_accuracy@10 | 0.9463 | | cosine_precision@1 | 0.7628 | | cosine_precision@3 | 0.29 | | cosine_precision@5 | 0.1812 | | cosine_precision@10 | 0.0946 | | cosine_recall@1 | 0.7628 | | cosine_recall@3 | 0.87 | | cosine_recall@5 | 0.9062 | | cosine_recall@10 | 0.9463 | | cosine_ndcg@10 | 0.853 | | cosine_mrr@10 | 0.8232 | | **cosine_map@100** | **0.8254** | #### 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 | 0.7628 | | cosine_accuracy@3 | 0.8684 | | cosine_accuracy@5 | 0.9016 | | cosine_accuracy@10 | 0.9419 | | cosine_precision@1 | 0.7628 | | cosine_precision@3 | 0.2895 | | cosine_precision@5 | 0.1803 | | cosine_precision@10 | 0.0942 | | cosine_recall@1 | 0.7628 | | cosine_recall@3 | 0.8684 | | cosine_recall@5 | 0.9016 | | cosine_recall@10 | 0.9419 | | cosine_ndcg@10 | 0.8507 | | cosine_mrr@10 | 0.8216 | | **cosine_map@100** | **0.824** | #### 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 | 0.7573 | | cosine_accuracy@3 | 0.8634 | | cosine_accuracy@5 | 0.8953 | | cosine_accuracy@10 | 0.9347 | | cosine_precision@1 | 0.7573 | | cosine_precision@3 | 0.2878 | | cosine_precision@5 | 0.1791 | | cosine_precision@10 | 0.0935 | | cosine_recall@1 | 0.7573 | | cosine_recall@3 | 0.8634 | | cosine_recall@5 | 0.8953 | | cosine_recall@10 | 0.9347 | | cosine_ndcg@10 | 0.8445 | | cosine_mrr@10 | 0.8157 | | **cosine_map@100** | **0.8184** | #### 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 | 0.742 | | cosine_accuracy@3 | 0.853 | | cosine_accuracy@5 | 0.8859 | | cosine_accuracy@10 | 0.9284 | | cosine_precision@1 | 0.742 | | cosine_precision@3 | 0.2843 | | cosine_precision@5 | 0.1772 | | cosine_precision@10 | 0.0928 | | cosine_recall@1 | 0.742 | | cosine_recall@3 | 0.853 | | cosine_recall@5 | 0.8859 | | cosine_recall@10 | 0.9284 | | cosine_ndcg@10 | 0.8335 | | cosine_mrr@10 | 0.8032 | | **cosine_map@100** | **0.8057** | ## Training Details ### Training Dataset #### Unnamed Dataset * 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`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 0.0002 - `num_train_epochs`: 5 - `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`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.0002 - `weight_decay`: 0.0 - `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 - `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 | |:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| | 0.1558 | 10 | 0.7195 | - | - | - | - | - | | 0.3116 | 20 | 0.324 | - | - | - | - | - | | 0.4674 | 30 | 0.238 | - | - | - | - | - | | 0.6232 | 40 | 0.2265 | - | - | - | - | - | | 0.7790 | 50 | 0.1825 | - | - | - | - | - | | 0.9348 | 60 | 0.1938 | - | - | - | - | - | | **0.9971** | **64** | **-** | **0.8054** | **0.8198** | **0.8276** | **0.7796** | **0.8329** | | 1.0906 | 70 | 0.1397 | - | - | - | - | - | | 1.2463 | 80 | 0.0611 | - | - | - | - | - | | 1.4021 | 90 | 0.0506 | - | - | - | - | - | | 1.5579 | 100 | 0.047 | - | - | - | - | - | | 1.7137 | 110 | 0.0327 | - | - | - | - | - | | 1.8695 | 120 | 0.034 | - | - | - | - | - | | 1.9942 | 128 | - | 0.8036 | 0.8135 | 0.8187 | 0.7861 | 0.8243 | | 2.0253 | 130 | 0.0319 | - | - | - | - | - | | 2.1811 | 140 | 0.0347 | - | - | - | - | - | | 2.3369 | 150 | 0.021 | - | - | - | - | - | | 2.4927 | 160 | 0.0169 | - | - | - | - | - | | 2.6485 | 170 | 0.0135 | - | - | - | - | - | | 2.8043 | 180 | 0.0123 | - | - | - | - | - | | 2.9601 | 190 | 0.0111 | - | - | - | - | - | | 2.9912 | 192 | - | 0.8109 | 0.8179 | 0.8213 | 0.7973 | 0.8264 | | 3.1159 | 200 | 0.0083 | - | - | - | - | - | | 3.2717 | 210 | 0.0088 | - | - | - | - | - | | 3.4275 | 220 | 0.005 | - | - | - | - | - | | 3.5833 | 230 | 0.005 | - | - | - | - | - | | 3.7390 | 240 | 0.0043 | - | - | - | - | - | | 3.8948 | 250 | 0.0058 | - | - | - | - | - | | 3.9883 | 256 | - | 0.8163 | 0.8244 | 0.8260 | 0.8045 | 0.8287 | | 4.0506 | 260 | 0.0057 | - | - | - | - | - | | 4.2064 | 270 | 0.0035 | - | - | - | - | - | | 4.3622 | 280 | 0.0033 | - | - | - | - | - | | 4.5180 | 290 | 0.0032 | - | - | - | - | - | | 4.6738 | 300 | 0.0031 | - | - | - | - | - | | 4.8296 | 310 | 0.0038 | - | - | - | - | - | | 4.9854 | 320 | 0.0042 | 0.8184 | 0.8240 | 0.8254 | 0.8057 | 0.8302 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.12.5 - Sentence Transformers: 3.0.1 - Transformers: 4.44.2 - PyTorch: 2.4.0 - 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} } ```