--- base_model: sentence-transformers/all-MiniLM-L6-v2 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:CosineSimilarityLoss widget: - source_sentence: The man talked to a girl over the internet camera. sentences: - A group of elderly people pose around a dining table. - A teenager talks to a girl over a webcam. - There is no 'still' that is not relative to some other object. - source_sentence: A woman is writing something. sentences: - Two eagles are perched on a branch. - It refers to the maximum f-stop (which is defined as the ratio of focal length to effective aperture diameter). - A woman is chopping green onions. - 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. - Sheep are grazing in the field in front of a line of trees. - 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 sitting on the floor in a room is strumming a 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 sentence-transformers/all-MiniLM-L6-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.8937895757423139 name: Pearson Cosine - type: spearman_cosine value: 0.8933408335166381 name: Spearman Cosine - type: pearson_manhattan value: 0.8893270459753304 name: Pearson Manhattan - type: spearman_manhattan value: 0.8931680438618355 name: Spearman Manhattan - type: pearson_euclidean value: 0.8894951039580792 name: Pearson Euclidean - type: spearman_euclidean value: 0.8933408335166381 name: Spearman Euclidean - type: pearson_dot value: 0.8937895714961118 name: Pearson Dot - type: spearman_dot value: 0.8933411165328404 name: Spearman Dot - type: pearson_max value: 0.8937895757423139 name: Pearson Max - type: spearman_max value: 0.8933411165328404 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.8567692080604863 name: Pearson Cosine - type: spearman_cosine value: 0.8581039412647984 name: Spearman Cosine - type: pearson_manhattan value: 0.8539129662613905 name: Pearson Manhattan - type: spearman_manhattan value: 0.8559325366695306 name: Spearman Manhattan - type: pearson_euclidean value: 0.8559600700692871 name: Pearson Euclidean - type: spearman_euclidean value: 0.8581039412647984 name: Spearman Euclidean - type: pearson_dot value: 0.8567692052012096 name: Pearson Dot - type: spearman_dot value: 0.8581039412647984 name: Spearman Dot - type: pearson_max value: 0.8567692080604863 name: Pearson Max - type: spearman_max value: 0.8581039412647984 name: Spearman Max --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) dataset. It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 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': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, '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}) (2): Normalize() ) ``` ## 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("praveenku32k/all-MiniLM-L6-v2-sts") # 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 sitting on the floor in a room is strumming a guitar.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8938 | | **spearman_cosine** | **0.8933** | | pearson_manhattan | 0.8893 | | spearman_manhattan | 0.8932 | | pearson_euclidean | 0.8895 | | spearman_euclidean | 0.8933 | | pearson_dot | 0.8938 | | spearman_dot | 0.8933 | | pearson_max | 0.8938 | | spearman_max | 0.8933 | #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8568 | | **spearman_cosine** | **0.8581** | | pearson_manhattan | 0.8539 | | spearman_manhattan | 0.8559 | | pearson_euclidean | 0.856 | | spearman_euclidean | 0.8581 | | pearson_dot | 0.8568 | | spearman_dot | 0.8581 | | pearson_max | 0.8568 | | spearman_max | 0.8581 | ## 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: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### 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: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 4 - `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`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_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`: 4 - `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 - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:| | 0.2778 | 100 | 0.0258 | 0.0231 | 0.8859 | - | | 0.5556 | 200 | 0.0229 | 0.0214 | 0.8916 | - | | 0.8333 | 300 | 0.0222 | 0.0203 | 0.8924 | - | | 1.1111 | 400 | 0.0178 | 0.0213 | 0.8927 | - | | 1.3889 | 500 | 0.0135 | 0.0211 | 0.8924 | - | | 1.6667 | 600 | 0.0123 | 0.0215 | 0.8921 | - | | 1.9444 | 700 | 0.0128 | 0.0208 | 0.8910 | - | | 2.2222 | 800 | 0.009 | 0.0207 | 0.8941 | - | | 2.5 | 900 | 0.008 | 0.0208 | 0.8943 | - | | 2.7778 | 1000 | 0.0075 | 0.0209 | 0.8943 | - | | 3.0556 | 1100 | 0.0081 | 0.0215 | 0.8934 | - | | 3.3333 | 1200 | 0.0063 | 0.0211 | 0.8932 | - | | 3.6111 | 1300 | 0.0061 | 0.0213 | 0.8933 | - | | 3.8889 | 1400 | 0.0059 | 0.0213 | 0.8933 | - | | 4.0 | 1440 | - | - | - | 0.8581 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.3.0+cu121 - Accelerate: 0.32.1 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ```