| | --- |
| | language: |
| | - en |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:5749 |
| | - loss:CosineSimilarityLoss |
| | base_model: google-bert/bert-base-uncased |
| | 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. |
| | datasets: |
| | - sentence-transformers/stsb |
| | pipeline_tag: sentence-similarity |
| | 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 |
| | model-index: |
| | - name: SentenceTransformer based on google-bert/bert-base-uncased |
| | results: |
| | - task: |
| | type: semantic-similarity |
| | name: Semantic Similarity |
| | dataset: |
| | name: sts dev |
| | type: sts-dev |
| | metrics: |
| | - type: pearson_cosine |
| | value: 0.8750639784456109 |
| | name: Pearson Cosine |
| | - type: spearman_cosine |
| | value: 0.8763732796351635 |
| | name: Spearman Cosine |
| | - type: pearson_manhattan |
| | value: 0.8500806390555404 |
| | name: Pearson Manhattan |
| | - type: spearman_manhattan |
| | value: 0.8544026288312274 |
| | name: Spearman Manhattan |
| | - type: pearson_euclidean |
| | value: 0.8509873124432761 |
| | name: Pearson Euclidean |
| | - type: spearman_euclidean |
| | value: 0.8552711165079961 |
| | name: Spearman Euclidean |
| | - type: pearson_dot |
| | value: 0.820163390731617 |
| | name: Pearson Dot |
| | - type: spearman_dot |
| | value: 0.8230126279079186 |
| | name: Spearman Dot |
| | - type: pearson_max |
| | value: 0.8750639784456109 |
| | name: Pearson Max |
| | - type: spearman_max |
| | value: 0.8763732796351635 |
| | name: Spearman Max |
| | - task: |
| | type: semantic-similarity |
| | name: Semantic Similarity |
| | dataset: |
| | name: sts test |
| | type: sts-test |
| | metrics: |
| | - type: pearson_cosine |
| | value: 0.8488910100773219 |
| | name: Pearson Cosine |
| | - type: spearman_cosine |
| | value: 0.8470522115508275 |
| | name: Spearman Cosine |
| | - type: pearson_manhattan |
| | value: 0.8346925106528352 |
| | name: Pearson Manhattan |
| | - type: spearman_manhattan |
| | value: 0.8347776246956976 |
| | name: Spearman Manhattan |
| | - type: pearson_euclidean |
| | value: 0.8352622451045902 |
| | name: Pearson Euclidean |
| | - type: spearman_euclidean |
| | value: 0.8351127906424753 |
| | name: Spearman Euclidean |
| | - type: pearson_dot |
| | value: 0.7832345853494516 |
| | name: Pearson Dot |
| | - type: spearman_dot |
| | value: 0.7761724556948709 |
| | name: Spearman Dot |
| | - type: pearson_max |
| | value: 0.8488910100773219 |
| | name: Pearson Max |
| | - type: spearman_max |
| | value: 0.8470522115508275 |
| | name: Spearman Max |
| | --- |
| | |
| | # SentenceTransformer based on google-bert/bert-base-uncased |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) 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:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 --> |
| | - **Maximum Sequence Length:** 512 tokens |
| | - **Output Dimensionality:** 768 tokens |
| | - **Similarity Function:** Cosine Similarity |
| | - **Training Dataset:** |
| | - [stsb](https://huggingface.co/datasets/sentence-transformers/stsb) |
| | - **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: BertModel |
| | (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("bingcheng9/bert-base-uncased-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, 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 |
| |
|
| | #### Semantic Similarity |
| | * Dataset: `sts-dev` |
| | * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
| |
|
| | | Metric | Value | |
| | |:--------------------|:-----------| |
| | | pearson_cosine | 0.8751 | |
| | | **spearman_cosine** | **0.8764** | |
| | | pearson_manhattan | 0.8501 | |
| | | spearman_manhattan | 0.8544 | |
| | | pearson_euclidean | 0.851 | |
| | | spearman_euclidean | 0.8553 | |
| | | pearson_dot | 0.8202 | |
| | | spearman_dot | 0.823 | |
| | | pearson_max | 0.8751 | |
| | | spearman_max | 0.8764 | |
| | |
| | #### Semantic Similarity |
| | * Dataset: `sts-test` |
| | * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
| | |
| | | Metric | Value | |
| | |:--------------------|:-----------| |
| | | pearson_cosine | 0.8489 | |
| | | **spearman_cosine** | **0.8471** | |
| | | pearson_manhattan | 0.8347 | |
| | | spearman_manhattan | 0.8348 | |
| | | pearson_euclidean | 0.8353 | |
| | | spearman_euclidean | 0.8351 | |
| | | pearson_dot | 0.7832 | |
| | | spearman_dot | 0.7762 | |
| | | pearson_max | 0.8489 | |
| | | spearman_max | 0.8471 | |
| | |
| | <!-- |
| | ## 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 |
| | |
| | #### stsb |
| | |
| | * Dataset: [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: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | sentence1 | sentence2 | score | |
| | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | | type | string | string | float | |
| | | details | <ul><li>min: 6 tokens</li><li>mean: 10.0 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.95 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> | |
| | * Samples: |
| | | sentence1 | sentence2 | score | |
| | |:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------| |
| | | <code>A plane is taking off.</code> | <code>An air plane is taking off.</code> | <code>1.0</code> | |
| | | <code>A man is playing a large flute.</code> | <code>A man is playing a flute.</code> | <code>0.76</code> | |
| | | <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</code> | |
| | * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: |
| | ```json |
| | { |
| | "loss_fct": "torch.nn.modules.loss.MSELoss" |
| | } |
| | ``` |
| | |
| | ### Evaluation Dataset |
| | |
| | #### stsb |
| | |
| | * Dataset: [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: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | sentence1 | sentence2 | score | |
| | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | | type | string | string | float | |
| | | details | <ul><li>min: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> | |
| | * Samples: |
| | | sentence1 | sentence2 | score | |
| | |:--------------------------------------------------|:------------------------------------------------------|:------------------| |
| | | <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> | |
| | | <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> | |
| | | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> | |
| | * Loss: [<code>CosineSimilarityLoss</code>](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 |
| | |
| | #### 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`: 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 |
| | - `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`: 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`: 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`: 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 |
| | - `use_liger_kernel`: False |
| | - `eval_use_gather_object`: False |
| | - `batch_sampler`: batch_sampler |
| | - `multi_dataset_batch_sampler`: proportional |
| | |
| | </details> |
| | |
| | ### Training Logs |
| | | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |
| | |:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:| |
| | | 0.2778 | 100 | 0.0608 | 0.0409 | 0.8190 | - | |
| | | 0.5556 | 200 | 0.0338 | 0.0308 | 0.8457 | - | |
| | | 0.8333 | 300 | 0.0286 | 0.0261 | 0.8605 | - | |
| | | 1.1111 | 400 | 0.0215 | 0.0299 | 0.8639 | - | |
| | | 1.3889 | 500 | 0.0144 | 0.0284 | 0.8714 | - | |
| | | 1.6667 | 600 | 0.0131 | 0.0261 | 0.8670 | - | |
| | | 1.9444 | 700 | 0.0133 | 0.0261 | 0.8714 | - | |
| | | 2.2222 | 800 | 0.0082 | 0.0266 | 0.8727 | - | |
| | | 2.5 | 900 | 0.0069 | 0.0257 | 0.8722 | - | |
| | | 2.7778 | 1000 | 0.0064 | 0.0256 | 0.8731 | - | |
| | | 3.0556 | 1100 | 0.006 | 0.0273 | 0.8746 | - | |
| | | 3.3333 | 1200 | 0.0046 | 0.0262 | 0.8757 | - | |
| | | 3.6111 | 1300 | 0.0042 | 0.0260 | 0.8760 | - | |
| | | 3.8889 | 1400 | 0.0039 | 0.0257 | 0.8764 | - | |
| | | 4.0 | 1440 | - | - | - | 0.8471 | |
| | |
| | |
| | ### Framework Versions |
| | - Python: 3.12.4 |
| | - Sentence Transformers: 3.1.1 |
| | - Transformers: 4.45.2 |
| | - PyTorch: 2.2.2 |
| | - Accelerate: 0.26.0 |
| | - Datasets: 3.0.2 |
| | - Tokenizers: 0.20.1 |
| | |
| | ## 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", |
| | } |
| | ``` |
| | |
| | <!-- |
| | ## 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.* |
| | --> |
