--- base_model: FacebookAI/xlm-roberta-large datasets: - sentence-transformers/stsb 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 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:5749 - loss:MatryoshkaLoss - loss:CoSENTLoss widget: - source_sentence: A chef is preparing some food. sentences: - Five birds stand on the snow. - A chef prepared a meal. - There is no 'still' that is not relative to some other object. - source_sentence: A woman is adding oil on fishes. sentences: - Large cruise ship floating on the water. - It refers to the maximum f-stop (which is defined as the ratio of focal length to effective aperture diameter). - The woman is cutting potatoes. - source_sentence: The player shoots the winning points. sentences: - Minimum wage laws hurt the least skilled, least productive the most. - The basketball player is about to score points for his team. - Three televisions, on on the floor, the other two on a box. - source_sentence: Stars form in star-formation regions, which itself develop from molecular clouds. sentences: - Although I believe Searle is mistaken, I don't think you have found the problem. - It may be possible for a solar system like ours to exist outside of a galaxy. - A blond-haired child performing on the trumpet in front of a house while his younger brother watches. - source_sentence: While Queen may refer to both Queen regent (sovereign) or Queen consort, the King has always been the sovereign. sentences: - At first, I thought this is a bit of a tricky question. - A man plays the guitar. - There is a very good reason not to refer to the Queen's spouse as "King" - because they aren't the King. model-index: - name: SentenceTransformer based on FacebookAI/xlm-roberta-large results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev 768 type: sts-dev-768 metrics: - type: pearson_cosine value: .nan name: Pearson Cosine - type: spearman_cosine value: .nan name: Spearman Cosine - type: pearson_manhattan value: -0.038123417655342585 name: Pearson Manhattan - type: spearman_manhattan value: -0.030855987437062582 name: Spearman Manhattan - type: pearson_euclidean value: -0.0742298464837288 name: Pearson Euclidean - type: spearman_euclidean value: -0.016119009479880368 name: Spearman Euclidean - type: pearson_dot value: -0.053239384921975864 name: Pearson Dot - type: spearman_dot value: -0.03860610142560432 name: Spearman Dot - type: pearson_max value: .nan name: Pearson Max - type: spearman_max value: .nan name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev 512 type: sts-dev-512 metrics: - type: pearson_cosine value: .nan name: Pearson Cosine - type: spearman_cosine value: .nan name: Spearman Cosine - type: pearson_manhattan value: -0.040766255073950965 name: Pearson Manhattan - type: spearman_manhattan value: -0.028106086435826655 name: Spearman Manhattan - type: pearson_euclidean value: -0.076050553000047 name: Pearson Euclidean - type: spearman_euclidean value: -0.014573222092867504 name: Spearman Euclidean - type: pearson_dot value: -0.06110575151055097 name: Pearson Dot - type: spearman_dot value: -0.04818501881621991 name: Spearman Dot - type: pearson_max value: .nan name: Pearson Max - type: spearman_max value: .nan name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev 256 type: sts-dev-256 metrics: - type: pearson_cosine value: .nan name: Pearson Cosine - type: spearman_cosine value: .nan name: Spearman Cosine - type: pearson_manhattan value: -0.044210895435818166 name: Pearson Manhattan - type: spearman_manhattan value: -0.03253407490039325 name: Spearman Manhattan - type: pearson_euclidean value: -0.0529355152933442 name: Pearson Euclidean - type: spearman_euclidean value: -0.0338167301189937 name: Spearman Euclidean - type: pearson_dot value: 0.0887169006335579 name: Pearson Dot - type: spearman_dot value: 0.06886250477710897 name: Spearman Dot - type: pearson_max value: .nan name: Pearson Max - type: spearman_max value: .nan name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev 128 type: sts-dev-128 metrics: - type: pearson_cosine value: .nan name: Pearson Cosine - type: spearman_cosine value: .nan name: Spearman Cosine - type: pearson_manhattan value: -0.05321620243744594 name: Pearson Manhattan - type: spearman_manhattan value: -0.026531903856252148 name: Spearman Manhattan - type: pearson_euclidean value: -0.06064347235216407 name: Pearson Euclidean - type: spearman_euclidean value: -0.0270947004666721 name: Spearman Euclidean - type: pearson_dot value: 0.07199088437564892 name: Pearson Dot - type: spearman_dot value: 0.05552894816506978 name: Spearman Dot - type: pearson_max value: .nan name: Pearson Max - type: spearman_max value: .nan name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev 64 type: sts-dev-64 metrics: - type: pearson_cosine value: .nan name: Pearson Cosine - type: spearman_cosine value: .nan name: Spearman Cosine - type: pearson_manhattan value: -0.046922199302745354 name: Pearson Manhattan - type: spearman_manhattan value: -0.027530540631984835 name: Spearman Manhattan - type: pearson_euclidean value: -0.04930495975336398 name: Pearson Euclidean - type: spearman_euclidean value: -0.02287953412697089 name: Spearman Euclidean - type: pearson_dot value: 0.05851507366090909 name: Pearson Dot - type: spearman_dot value: 0.044913605667507114 name: Spearman Dot - type: pearson_max value: .nan name: Pearson Max - type: spearman_max value: .nan name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 768 type: sts-test-768 metrics: - type: pearson_cosine value: .nan name: Pearson Cosine - type: spearman_cosine value: .nan name: Spearman Cosine - type: pearson_manhattan value: 0.0005203243269627229 name: Pearson Manhattan - type: spearman_manhattan value: 0.007914891421418472 name: Spearman Manhattan - type: pearson_euclidean value: -0.008479099839233263 name: Pearson Euclidean - type: spearman_euclidean value: 0.0002449834909380018 name: Spearman Euclidean - type: pearson_dot value: 0.015253799995136243 name: Pearson Dot - type: spearman_dot value: -0.002544651953260673 name: Spearman Dot - type: pearson_max value: .nan name: Pearson Max - type: spearman_max value: .nan name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 512 type: sts-test-512 metrics: - type: pearson_cosine value: .nan name: Pearson Cosine - type: spearman_cosine value: .nan name: Spearman Cosine - type: pearson_manhattan value: -0.000985791968546407 name: Pearson Manhattan - type: spearman_manhattan value: 0.009210170664121263 name: Spearman Manhattan - type: pearson_euclidean value: -0.010968197464829785 name: Pearson Euclidean - type: spearman_euclidean value: 0.0006366521814203481 name: Spearman Euclidean - type: pearson_dot value: 0.030903954394043587 name: Pearson Dot - type: spearman_dot value: 0.0214169911509498 name: Spearman Dot - type: pearson_max value: .nan name: Pearson Max - type: spearman_max value: .nan name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 256 type: sts-test-256 metrics: - type: pearson_cosine value: .nan name: Pearson Cosine - type: spearman_cosine value: .nan name: Spearman Cosine - type: pearson_manhattan value: -0.008347426706014351 name: Pearson Manhattan - type: spearman_manhattan value: 0.008133437696668973 name: Spearman Manhattan - type: pearson_euclidean value: -0.01284332508912676 name: Pearson Euclidean - type: spearman_euclidean value: 0.006207692348050752 name: Spearman Euclidean - type: pearson_dot value: -0.10411841010392278 name: Pearson Dot - type: spearman_dot value: -0.10441611480429308 name: Spearman Dot - type: pearson_max value: .nan name: Pearson Max - type: spearman_max value: .nan name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 128 type: sts-test-128 metrics: - type: pearson_cosine value: .nan name: Pearson Cosine - type: spearman_cosine value: .nan name: Spearman Cosine - type: pearson_manhattan value: -0.007293947286825709 name: Pearson Manhattan - type: spearman_manhattan value: 0.012461130559236479 name: Spearman Manhattan - type: pearson_euclidean value: -0.013785631605643068 name: Pearson Euclidean - type: spearman_euclidean value: 0.008355374230034162 name: Spearman Euclidean - type: pearson_dot value: -0.07790382803601184 name: Pearson Dot - type: spearman_dot value: -0.08277939304968172 name: Spearman Dot - type: pearson_max value: .nan name: Pearson Max - type: spearman_max value: .nan name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test 64 type: sts-test-64 metrics: - type: pearson_cosine value: .nan name: Pearson Cosine - type: spearman_cosine value: .nan name: Spearman Cosine - type: pearson_manhattan value: -0.012731573411777072 name: Pearson Manhattan - type: spearman_manhattan value: 0.003453137865023755 name: Spearman Manhattan - type: pearson_euclidean value: -0.013710254571378023 name: Pearson Euclidean - type: spearman_euclidean value: 0.0028389826642085166 name: Spearman Euclidean - type: pearson_dot value: -0.04900795414419644 name: Pearson Dot - type: spearman_dot value: -0.05520642056907742 name: Spearman Dot - type: pearson_max value: .nan name: Pearson Max - type: spearman_max value: .nan name: Spearman Max --- # SentenceTransformer based on FacebookAI/xlm-roberta-large This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) dataset. It maps sentences & paragraphs to a 1024-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:** [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) - **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: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, '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("dipteshkanojia/xlm-roberta-large-sts-matryoshka") # Run inference sentences = [ 'While Queen may refer to both Queen regent (sovereign) or Queen consort, the King has always been the sovereign.', 'There is a very good reason not to refer to the Queen\'s spouse as "King" - because they aren\'t the King.', 'A man plays the guitar.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev-768` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:--------| | pearson_cosine | nan | | **spearman_cosine** | **nan** | | pearson_manhattan | -0.0381 | | spearman_manhattan | -0.0309 | | pearson_euclidean | -0.0742 | | spearman_euclidean | -0.0161 | | pearson_dot | -0.0532 | | spearman_dot | -0.0386 | | pearson_max | nan | | spearman_max | nan | #### Semantic Similarity * Dataset: `sts-dev-512` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:--------| | pearson_cosine | nan | | **spearman_cosine** | **nan** | | pearson_manhattan | -0.0408 | | spearman_manhattan | -0.0281 | | pearson_euclidean | -0.0761 | | spearman_euclidean | -0.0146 | | pearson_dot | -0.0611 | | spearman_dot | -0.0482 | | pearson_max | nan | | spearman_max | nan | #### Semantic Similarity * Dataset: `sts-dev-256` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:--------| | pearson_cosine | nan | | **spearman_cosine** | **nan** | | pearson_manhattan | -0.0442 | | spearman_manhattan | -0.0325 | | pearson_euclidean | -0.0529 | | spearman_euclidean | -0.0338 | | pearson_dot | 0.0887 | | spearman_dot | 0.0689 | | pearson_max | nan | | spearman_max | nan | #### Semantic Similarity * Dataset: `sts-dev-128` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:--------| | pearson_cosine | nan | | **spearman_cosine** | **nan** | | pearson_manhattan | -0.0532 | | spearman_manhattan | -0.0265 | | pearson_euclidean | -0.0606 | | spearman_euclidean | -0.0271 | | pearson_dot | 0.072 | | spearman_dot | 0.0555 | | pearson_max | nan | | spearman_max | nan | #### Semantic Similarity * Dataset: `sts-dev-64` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:--------| | pearson_cosine | nan | | **spearman_cosine** | **nan** | | pearson_manhattan | -0.0469 | | spearman_manhattan | -0.0275 | | pearson_euclidean | -0.0493 | | spearman_euclidean | -0.0229 | | pearson_dot | 0.0585 | | spearman_dot | 0.0449 | | pearson_max | nan | | spearman_max | nan | #### Semantic Similarity * Dataset: `sts-test-768` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:--------| | pearson_cosine | nan | | **spearman_cosine** | **nan** | | pearson_manhattan | 0.0005 | | spearman_manhattan | 0.0079 | | pearson_euclidean | -0.0085 | | spearman_euclidean | 0.0002 | | pearson_dot | 0.0153 | | spearman_dot | -0.0025 | | pearson_max | nan | | spearman_max | nan | #### Semantic Similarity * Dataset: `sts-test-512` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:--------| | pearson_cosine | nan | | **spearman_cosine** | **nan** | | pearson_manhattan | -0.001 | | spearman_manhattan | 0.0092 | | pearson_euclidean | -0.011 | | spearman_euclidean | 0.0006 | | pearson_dot | 0.0309 | | spearman_dot | 0.0214 | | pearson_max | nan | | spearman_max | nan | #### Semantic Similarity * Dataset: `sts-test-256` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:--------| | pearson_cosine | nan | | **spearman_cosine** | **nan** | | pearson_manhattan | -0.0083 | | spearman_manhattan | 0.0081 | | pearson_euclidean | -0.0128 | | spearman_euclidean | 0.0062 | | pearson_dot | -0.1041 | | spearman_dot | -0.1044 | | pearson_max | nan | | spearman_max | nan | #### Semantic Similarity * Dataset: `sts-test-128` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:--------| | pearson_cosine | nan | | **spearman_cosine** | **nan** | | pearson_manhattan | -0.0073 | | spearman_manhattan | 0.0125 | | pearson_euclidean | -0.0138 | | spearman_euclidean | 0.0084 | | pearson_dot | -0.0779 | | spearman_dot | -0.0828 | | pearson_max | nan | | spearman_max | nan | #### Semantic Similarity * Dataset: `sts-test-64` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:--------| | pearson_cosine | nan | | **spearman_cosine** | **nan** | | pearson_manhattan | -0.0127 | | spearman_manhattan | 0.0035 | | pearson_euclidean | -0.0137 | | spearman_euclidean | 0.0028 | | pearson_dot | -0.049 | | spearman_dot | -0.0552 | | pearson_max | nan | | spearman_max | nan | ## Training Details ### Training Dataset #### sentence-transformers/stsb * Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308) * Size: 5,749 training samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------| | A plane is taking off. | An air plane is taking off. | 1.0 | | A man is playing a large flute. | A man is playing a flute. | 0.76 | | A man is spreading shreded cheese on a pizza. | A man is spreading shredded cheese on an uncooked pizza. | 0.76 | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "CoSENTLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### sentence-transformers/stsb * Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308) * Size: 1,500 evaluation samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:--------------------------------------------------|:------------------------------------------------------|:------------------| | A man with a hard hat is dancing. | A man wearing a hard hat is dancing. | 1.0 | | A young child is riding a horse. | A child is riding a horse. | 0.95 | | A man is feeding a mouse to a snake. | The man is feeding a mouse to the snake. | 1.0 | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "CoSENTLoss", "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`: steps - `per_device_train_batch_size`: 6 - `per_device_eval_batch_size`: 6 - `num_train_epochs`: 8 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 6 - `per_device_eval_batch_size`: 6 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-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`: 8 - `max_steps`: -1 - `lr_scheduler_type`: linear - `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`: 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`: 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`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | sts-dev-128_spearman_cosine | sts-dev-256_spearman_cosine | sts-dev-512_spearman_cosine | sts-dev-64_spearman_cosine | sts-dev-768_spearman_cosine | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-512_spearman_cosine | sts-test-64_spearman_cosine | sts-test-768_spearman_cosine | |:------:|:----:|:-------------:|:-------:|:---------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:----------------------------:| | 1.0417 | 500 | 21.1353 | 20.8565 | nan | nan | nan | nan | nan | - | - | - | - | - | | 2.0833 | 1000 | 20.7941 | 20.8565 | nan | nan | nan | nan | nan | - | - | - | - | - | | 3.125 | 1500 | 20.7823 | 20.8565 | nan | nan | nan | nan | nan | - | - | - | - | - | | 4.1667 | 2000 | 20.781 | 20.8565 | nan | nan | nan | nan | nan | - | - | - | - | - | | 5.2083 | 2500 | 20.7707 | 20.8565 | nan | nan | nan | nan | nan | - | - | - | - | - | | 6.25 | 3000 | 20.7661 | 20.8565 | nan | nan | nan | nan | nan | - | - | - | - | - | | 7.2917 | 3500 | 20.7719 | 20.8565 | nan | nan | nan | nan | nan | - | - | - | - | - | | 8.0 | 3840 | - | - | - | - | - | - | - | nan | nan | nan | nan | nan | ### Framework Versions - Python: 3.9.19 - Sentence Transformers: 3.1.0.dev0 - Transformers: 4.44.2 - PyTorch: 2.4.1+cu121 - Accelerate: 0.34.2 - 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} } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ```