| | --- |
| | language: |
| | - en |
| | library_name: sentence-transformers |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:314315 |
| | - loss:AdaptiveLayerLoss |
| | - loss:MultipleNegativesRankingLoss |
| | base_model: microsoft/deberta-v3-small |
| | datasets: |
| | - stanfordnlp/snli |
| | metrics: |
| | - 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 |
| | widget: |
| | - source_sentence: The pitcher is pitching the ball in a game of baseball. |
| | sentences: |
| | - the lady digs into the ground |
| | - A group of people are sitting at tables. |
| | - The pitcher throws the ball. |
| | - source_sentence: People are conversing at a dining table under a canopy. |
| | sentences: |
| | - A canine is using his legs. |
| | - The people are creative. |
| | - People at a party are seated for dinner on the lawn. |
| | - source_sentence: Two teenage girls conversing next to lockers. |
| | sentences: |
| | - Girls talking about their problems next to lockers. |
| | - A group of people play in the ocean. |
| | - The man is testing the bike. |
| | - source_sentence: A young boy in a hoodie climbs a red slide sitting on a red and |
| | green checkered background. |
| | sentences: |
| | - People are buying food from a street vendor. |
| | - A boy is playing. |
| | - A dog outside digging. |
| | - source_sentence: A professional swimmer spits water out after surfacing while grabbing |
| | the hand of someone helping him back to land. |
| | sentences: |
| | - A group of people wait in a line. |
| | - A tourist has his picture taken on Easter Island. |
| | - The swimmer almost drowned after being sucked under a fast current. |
| | pipeline_tag: sentence-similarity |
| | model-index: |
| | - name: SentenceTransformer based on microsoft/deberta-v3-small |
| | results: |
| | - task: |
| | type: binary-classification |
| | name: Binary Classification |
| | dataset: |
| | name: Unknown |
| | type: unknown |
| | metrics: |
| | - type: cosine_accuracy |
| | value: 0.6578209113655319 |
| | name: Cosine Accuracy |
| | - type: cosine_accuracy_threshold |
| | value: 0.7228835821151733 |
| | name: Cosine Accuracy Threshold |
| | - type: cosine_f1 |
| | value: 0.7058138858173776 |
| | name: Cosine F1 |
| | - type: cosine_f1_threshold |
| | value: 0.6018929481506348 |
| | name: Cosine F1 Threshold |
| | - type: cosine_precision |
| | value: 0.586687306501548 |
| | name: Cosine Precision |
| | - type: cosine_recall |
| | value: 0.8856433474514386 |
| | name: Cosine Recall |
| | - type: cosine_ap |
| | value: 0.6972177912771047 |
| | name: Cosine Ap |
| | - type: dot_accuracy |
| | value: 0.6157403897187049 |
| | name: Dot Accuracy |
| | - type: dot_accuracy_threshold |
| | value: 240.6935577392578 |
| | name: Dot Accuracy Threshold |
| | - type: dot_f1 |
| | value: 0.6994949494949494 |
| | name: Dot F1 |
| | - type: dot_f1_threshold |
| | value: 180.59024047851562 |
| | name: Dot F1 Threshold |
| | - type: dot_precision |
| | value: 0.5603834989884774 |
| | name: Dot Precision |
| | - type: dot_recall |
| | value: 0.9304805024098145 |
| | name: Dot Recall |
| | - type: dot_ap |
| | value: 0.6228322985998769 |
| | name: Dot Ap |
| | - type: manhattan_accuracy |
| | value: 0.6658579118962772 |
| | name: Manhattan Accuracy |
| | - type: manhattan_accuracy_threshold |
| | value: 281.63262939453125 |
| | name: Manhattan Accuracy Threshold |
| | - type: manhattan_f1 |
| | value: 0.7096774193548386 |
| | name: Manhattan F1 |
| | - type: manhattan_f1_threshold |
| | value: 315.9024658203125 |
| | name: Manhattan F1 Threshold |
| | - type: manhattan_precision |
| | value: 0.6168446026097272 |
| | name: Manhattan Precision |
| | - type: manhattan_recall |
| | value: 0.8354023659997079 |
| | name: Manhattan Recall |
| | - type: manhattan_ap |
| | value: 0.7109579985461502 |
| | name: Manhattan Ap |
| | - type: euclidean_accuracy |
| | value: 0.6626734399878687 |
| | name: Euclidean Accuracy |
| | - type: euclidean_accuracy_threshold |
| | value: 14.194840431213379 |
| | name: Euclidean Accuracy Threshold |
| | - type: euclidean_f1 |
| | value: 0.7064288581751448 |
| | name: Euclidean F1 |
| | - type: euclidean_f1_threshold |
| | value: 17.004133224487305 |
| | name: Euclidean F1 Threshold |
| | - type: euclidean_precision |
| | value: 0.581586402266289 |
| | name: Euclidean Precision |
| | - type: euclidean_recall |
| | value: 0.8995180370965387 |
| | name: Euclidean Recall |
| | - type: euclidean_ap |
| | value: 0.7094433163219231 |
| | name: Euclidean Ap |
| | - type: max_accuracy |
| | value: 0.6658579118962772 |
| | name: Max Accuracy |
| | - type: max_accuracy_threshold |
| | value: 281.63262939453125 |
| | name: Max Accuracy Threshold |
| | - type: max_f1 |
| | value: 0.7096774193548386 |
| | name: Max F1 |
| | - type: max_f1_threshold |
| | value: 315.9024658203125 |
| | name: Max F1 Threshold |
| | - type: max_precision |
| | value: 0.6168446026097272 |
| | name: Max Precision |
| | - type: max_recall |
| | value: 0.9304805024098145 |
| | name: Max Recall |
| | - type: max_ap |
| | value: 0.7109579985461502 |
| | name: Max Ap |
| | --- |
| | |
| | # SentenceTransformer based on microsoft/deberta-v3-small |
| |
|
| | [n_layers_per_step = -1, last_layer_weight = 1 * (model_layers-1), prior_layers_weight= 0.85, kl_div_weight = 2, kl_temperature= 10, lr = 1e-6. batch = 42, schedule = cosine] |
| | |
| | |
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) 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:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 --> |
| | - **Maximum Sequence Length:** 512 tokens |
| | - **Output Dimensionality:** 768 tokens |
| | - **Similarity Function:** Cosine Similarity |
| | - **Training Dataset:** |
| | - [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) |
| | - **Language:** en |
| | <!-- - **License:** Unknown --> |
| | |
| | ### 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/DeBERTaV3-small-SenTra-AdaptiveLayerAllNorm") |
| | # Run inference |
| | sentences = [ |
| | 'A professional swimmer spits water out after surfacing while grabbing the hand of someone helping him back to land.', |
| | 'The swimmer almost drowned after being sucked under a fast current.', |
| | 'A group of people wait in a line.', |
| | ] |
| | 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] |
| | ``` |
| | |
| | <!-- |
| | ### Direct Usage (Transformers) |
| | |
| | <details><summary>Click to see the direct usage in Transformers</summary> |
| | |
| | </details> |
| | --> |
| | |
| | <!-- |
| | ### Downstream Usage (Sentence Transformers) |
| | |
| | You can finetune this model on your own dataset. |
| | |
| | <details><summary>Click to expand</summary> |
| | |
| | </details> |
| | --> |
| | |
| | <!-- |
| | ### Out-of-Scope Use |
| | |
| | *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| | --> |
| | |
| | ## Evaluation |
| | |
| | ### Metrics |
| | |
| | #### Binary Classification |
| | |
| | * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
| | |
| | | Metric | Value | |
| | |:-----------------------------|:----------| |
| | | cosine_accuracy | 0.6578 | |
| | | cosine_accuracy_threshold | 0.7229 | |
| | | cosine_f1 | 0.7058 | |
| | | cosine_f1_threshold | 0.6019 | |
| | | cosine_precision | 0.5867 | |
| | | cosine_recall | 0.8856 | |
| | | cosine_ap | 0.6972 | |
| | | dot_accuracy | 0.6157 | |
| | | dot_accuracy_threshold | 240.6936 | |
| | | dot_f1 | 0.6995 | |
| | | dot_f1_threshold | 180.5902 | |
| | | dot_precision | 0.5604 | |
| | | dot_recall | 0.9305 | |
| | | dot_ap | 0.6228 | |
| | | manhattan_accuracy | 0.6659 | |
| | | manhattan_accuracy_threshold | 281.6326 | |
| | | manhattan_f1 | 0.7097 | |
| | | manhattan_f1_threshold | 315.9025 | |
| | | manhattan_precision | 0.6168 | |
| | | manhattan_recall | 0.8354 | |
| | | manhattan_ap | 0.711 | |
| | | euclidean_accuracy | 0.6627 | |
| | | euclidean_accuracy_threshold | 14.1948 | |
| | | euclidean_f1 | 0.7064 | |
| | | euclidean_f1_threshold | 17.0041 | |
| | | euclidean_precision | 0.5816 | |
| | | euclidean_recall | 0.8995 | |
| | | euclidean_ap | 0.7094 | |
| | | max_accuracy | 0.6659 | |
| | | max_accuracy_threshold | 281.6326 | |
| | | max_f1 | 0.7097 | |
| | | max_f1_threshold | 315.9025 | |
| | | max_precision | 0.6168 | |
| | | max_recall | 0.9305 | |
| | | **max_ap** | **0.711** | |
| | |
| | <!-- |
| | ## Bias, Risks and Limitations |
| | |
| | *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| | --> |
| | |
| | <!-- |
| | ### Recommendations |
| | |
| | *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| | --> |
| | |
| | ## Training Details |
| | |
| | ### Training Dataset |
| | |
| | #### stanfordnlp/snli |
| | |
| | * Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b) |
| | * Size: 314,315 training samples |
| | * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | sentence1 | sentence2 | label | |
| | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------| |
| | | type | string | string | int | |
| | | details | <ul><li>min: 5 tokens</li><li>mean: 16.62 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.46 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> | |
| | * Samples: |
| | | sentence1 | sentence2 | label | |
| | |:---------------------------------------------------------------------------|:-------------------------------------------------|:---------------| |
| | | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>0</code> | |
| | | <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>0</code> | |
| | | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>0</code> | |
| | * Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
| | ```json |
| | { |
| | "loss": "MultipleNegativesRankingLoss", |
| | "n_layers_per_step": -1, |
| | "last_layer_weight": 6, |
| | "prior_layers_weight": 0.85, |
| | "kl_div_weight": 2, |
| | "kl_temperature": 10 |
| | } |
| | ``` |
| | |
| | ### Evaluation Dataset |
| |
|
| | #### stanfordnlp/snli |
| |
|
| | * Dataset: [stanfordnlp/snli](https://huggingface.co/datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co/datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b) |
| | * Size: 13,189 evaluation samples |
| | * Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | premise | hypothesis | label | |
| | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| |
| | | type | string | string | int | |
| | | details | <ul><li>min: 6 tokens</li><li>mean: 17.28 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.53 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>0: ~48.70%</li><li>1: ~51.30%</li></ul> | |
| | * Samples: |
| | | premise | hypothesis | label | |
| | |:--------------------------------------------------------------------------------------------------------|:---------------------------------------------------|:---------------| |
| | | <code>This church choir sings to the masses as they sing joyous songs from the book at a church.</code> | <code>The church has cracks in the ceiling.</code> | <code>0</code> | |
| | | <code>This church choir sings to the masses as they sing joyous songs from the book at a church.</code> | <code>The church is filled with song.</code> | <code>1</code> | |
| | | <code>A woman with a green headscarf, blue shirt and a very big grin.</code> | <code>The woman is young.</code> | <code>0</code> | |
| | * Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: |
| | ```json |
| | { |
| | "loss": "MultipleNegativesRankingLoss", |
| | "n_layers_per_step": -1, |
| | "last_layer_weight": 6, |
| | "prior_layers_weight": 0.85, |
| | "kl_div_weight": 2, |
| | "kl_temperature": 10 |
| | } |
| | ``` |
| |
|
| | ### Training Hyperparameters |
| | #### Non-Default Hyperparameters |
| |
|
| | - `eval_strategy`: steps |
| | - `per_device_train_batch_size`: 42 |
| | - `per_device_eval_batch_size`: 32 |
| | - `learning_rate`: 1e-06 |
| | - `weight_decay`: 1e-08 |
| | - `num_train_epochs`: 1 |
| | - `lr_scheduler_type`: cosine |
| | - `warmup_ratio`: 0.2 |
| | - `save_safetensors`: False |
| | - `fp16`: True |
| | - `hub_model_id`: bobox/DeBERTaV3-small-SenTra-AdaptiveLayerAllNorm-tmp |
| | - `hub_strategy`: checkpoint |
| | - `batch_sampler`: no_duplicates |
| | |
| | #### All Hyperparameters |
| | <details><summary>Click to expand</summary> |
| | |
| | - `overwrite_output_dir`: False |
| | - `do_predict`: False |
| | - `eval_strategy`: steps |
| | - `prediction_loss_only`: True |
| | - `per_device_train_batch_size`: 42 |
| | - `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`: 1e-06 |
| | - `weight_decay`: 1e-08 |
| | - `adam_beta1`: 0.9 |
| | - `adam_beta2`: 0.999 |
| | - `adam_epsilon`: 1e-08 |
| | - `max_grad_norm`: 1.0 |
| | - `num_train_epochs`: 1 |
| | - `max_steps`: -1 |
| | - `lr_scheduler_type`: cosine |
| | - `lr_scheduler_kwargs`: {} |
| | - `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`: False |
| | - `resume_from_checkpoint`: None |
| | - `hub_model_id`: bobox/DeBERTaV3-small-SenTra-AdaptiveLayerAllNorm-tmp |
| | - `hub_strategy`: checkpoint |
| | - `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 |
| | - `batch_sampler`: no_duplicates |
| | - `multi_dataset_batch_sampler`: proportional |
| |
|
| | </details> |
| |
|
| | ### Training Logs |
| | | Epoch | Step | Training Loss | loss | max_ap | |
| | |:------:|:----:|:-------------:|:-------:|:------:| |
| | | 0.0501 | 375 | 23.8735 | 21.0352 | 0.6131 | |
| | | 0.1002 | 750 | 22.4091 | 19.6992 | 0.6353 | |
| | | 0.1503 | 1125 | 19.4663 | 16.2104 | 0.6580 | |
| | | 0.2004 | 1500 | 15.348 | 13.2038 | 0.6732 | |
| | | 0.2505 | 1875 | 12.5377 | 11.6357 | 0.6815 | |
| | | 0.3006 | 2250 | 11.4576 | 10.7570 | 0.6862 | |
| | | 0.3507 | 2625 | 10.7446 | 10.1819 | 0.6891 | |
| | | 0.4009 | 3000 | 10.2323 | 9.7470 | 0.6904 | |
| | | 0.4510 | 3375 | 9.9825 | 9.4256 | 0.6914 | |
| | | 0.5011 | 3750 | 9.6954 | 9.2200 | 0.6923 | |
| | | 0.5512 | 4125 | 9.6359 | 9.0367 | 0.6923 | |
| | | 0.6013 | 4500 | 8.3103 | 7.8258 | 0.7026 | |
| | | 0.6514 | 4875 | 4.4845 | 7.4044 | 0.7073 | |
| | | 0.7015 | 5250 | 3.8303 | 7.2647 | 0.7092 | |
| | | 0.7516 | 5625 | 3.5617 | 7.2020 | 0.7098 | |
| | | 0.8017 | 6000 | 3.4088 | 7.1684 | 0.7103 | |
| | | 0.8518 | 6375 | 3.347 | 7.1531 | 0.7108 | |
| | | 0.9019 | 6750 | 3.2064 | 7.1451 | 0.7109 | |
| | | 0.9520 | 7125 | 3.3096 | 7.1427 | 0.7110 | |
| | |
| | |
| | ### Framework Versions |
| | - Python: 3.10.13 |
| | - Sentence Transformers: 3.0.1 |
| | - Transformers: 4.41.2 |
| | - PyTorch: 2.1.2 |
| | - Accelerate: 0.30.1 |
| | - Datasets: 2.19.2 |
| | - 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} |
| | } |
| | ``` |
| | |
| | #### 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} |
| | } |
| | ``` |
| | |
| | <!-- |
| | ## Glossary |
| | |
| | *Clearly define terms in order to be accessible across audiences.* |
| | --> |
| | |
| | <!-- |
| | ## Model Card Authors |
| | |
| | *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| | --> |
| | |
| | <!-- |
| | ## Model Card Contact |
| | |
| | *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| | --> |
