| | --- |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - generated_from_trainer |
| | - dataset_size:5749 |
| | - loss:CosineSimilarityLoss |
| | base_model: sentence-transformers/all-mpnet-base-v2 |
| | widget: |
| | - source_sentence: Young woman in riding gear on top of horse. |
| | sentences: |
| | - Italy’s centre-left splinters in presidential vote |
| | - The woman is riding on the brown horse. |
| | - Mali's Interim President Sworn Into Office |
| | - source_sentence: Sony reports record annual loss |
| | sentences: |
| | - A woman is playing a flute. |
| | - A man and a woman kiss. |
| | - Sony forecasts record annual loss of $6.4bn |
| | - source_sentence: A clear plastic chair in front of a bookcase. |
| | sentences: |
| | - Allen defends self against Farrow's abuse claims |
| | - Ehud Olmert sentenced to six years in Israel |
| | - a clear plastic chair in front of book shelves. |
| | - source_sentence: KLCI Futures traded mixed at mid-day |
| | sentences: |
| | - KL shares mixed at mid-day |
| | - NATO helicopter makes hard landing in E. Afghanistan |
| | - Sewol ferry crew faces trial |
| | - source_sentence: We in Britain think differently to Americans. |
| | sentences: |
| | - south korea has had a bullet train system since the 1980s. |
| | - Originally Posted by zaf We in Britain think differently to Americans. |
| | - Car bombings kill 13 civilians in Iraqi capital |
| | pipeline_tag: sentence-similarity |
| | library_name: sentence-transformers |
| | metrics: |
| | - pearson_cosine |
| | - spearman_cosine |
| | model-index: |
| | - name: SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 |
| | results: |
| | - task: |
| | type: semantic-similarity |
| | name: Semantic Similarity |
| | dataset: |
| | name: Unknown |
| | type: unknown |
| | metrics: |
| | - type: pearson_cosine |
| | value: 0.9075334661878893 |
| | name: Pearson Cosine |
| | - type: spearman_cosine |
| | value: 0.9060484206473507 |
| | name: Spearman Cosine |
| | - task: |
| | type: semantic-similarity |
| | name: Semantic Similarity |
| | dataset: |
| | name: sts dev |
| | type: sts-dev |
| | metrics: |
| | - type: pearson_cosine |
| | value: 0.9075334589342524 |
| | name: Pearson Cosine |
| | - type: spearman_cosine |
| | value: 0.9060484206473507 |
| | name: Spearman Cosine |
| | --- |
| | |
| | # SentenceTransformer based on sentence-transformers/all-mpnet-base-v2 |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 9a3225965996d404b775526de6dbfe85d3368642 --> |
| | - **Maximum Sequence Length:** 384 tokens |
| | - **Output Dimensionality:** 768 dimensions |
| | - **Similarity Function:** Cosine Similarity |
| | <!-- - **Training Dataset:** Unknown --> |
| | <!-- - **Language:** Unknown --> |
| | <!-- - **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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel |
| | (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}) |
| | (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("sentence_transformers_model_id") |
| | # Run inference |
| | sentences = [ |
| | 'We in Britain think differently to Americans.', |
| | 'Originally Posted by zaf We in Britain think differently to Americans.', |
| | 'south korea has had a bullet train system since the 1980s.', |
| | ] |
| | 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 |
| |
|
| | * Datasets: `` and `sts-dev` |
| | * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
| |
|
| | | Metric | | sts-dev | |
| | |:--------------------|:----------|:----------| |
| | | pearson_cosine | 0.9075 | 0.9075 | |
| | | **spearman_cosine** | **0.906** | **0.906** | |
| | |
| | <!-- |
| | ## 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 |
| | |
| | #### Unnamed Dataset |
| | |
| | |
| | * Size: 5,749 training samples |
| | * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | sentence_0 | sentence_1 | label | |
| | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | | type | string | string | float | |
| | | details | <ul><li>min: 6 tokens</li><li>mean: 14.16 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.18 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> | |
| | * Samples: |
| | | sentence_0 | sentence_1 | label | |
| | |:----------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:--------------------------------| |
| | | <code>US Senate to vote on fiscal cliff deal as deadline nears</code> | <code>Fiscal cliff: House delays vote on fiscal cliff deal - live</code> | <code>0.5599999904632569</code> | |
| | | <code>This is America, my friends, and it should not happen here," he said to loud applause.</code> | <code>"This is America, my friends, and it should not happen here."</code> | <code>0.65</code> | |
| | | <code>Books To Help Kids Talk About Boston Marathon News</code> | <code>Report of two explosions at finish line of Boston Marathon</code> | <code>0.1600000023841858</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`: 32 |
| | - `per_device_eval_batch_size`: 32 |
| | - `num_train_epochs`: 10 |
| | - `multi_dataset_batch_sampler`: round_robin |
| | |
| | #### 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`: 32 |
| | - `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 |
| | - `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 |
| | - `num_train_epochs`: 10 |
| | - `max_steps`: -1 |
| | - `lr_scheduler_type`: linear |
| | - `lr_scheduler_kwargs`: {} |
| | - `warmup_ratio`: 0.0 |
| | - `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`: None |
| | - `hub_always_push`: False |
| | - `gradient_checkpointing`: False |
| | - `gradient_checkpointing_kwargs`: None |
| | - `include_inputs_for_metrics`: False |
| | - `include_for_metrics`: [] |
| | - `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 |
| | - `average_tokens_across_devices`: False |
| | - `prompts`: None |
| | - `batch_sampler`: batch_sampler |
| | - `multi_dataset_batch_sampler`: round_robin |
| | |
| | </details> |
| | |
| | ### Training Logs |
| | <details><summary>Click to expand</summary> |
| | |
| | | Epoch | Step | Training Loss | spearman_cosine | sts-dev_spearman_cosine | |
| | |:------:|:----:|:-------------:|:---------------:|:-----------------------:| |
| | | 0 | 0 | - | 0.8811 | - | |
| | | 0.1 | 18 | - | - | 0.8816 | |
| | | 0.2 | 36 | - | - | 0.8834 | |
| | | 0.3 | 54 | - | - | 0.8847 | |
| | | 0.4 | 72 | - | - | 0.8894 | |
| | | 0.5 | 90 | - | - | 0.8933 | |
| | | 0.6 | 108 | - | - | 0.8966 | |
| | | 0.7 | 126 | - | - | 0.9005 | |
| | | 0.8 | 144 | - | - | 0.9020 | |
| | | 0.9 | 162 | - | - | 0.9010 | |
| | | 1.0 | 180 | - | - | 0.9001 | |
| | | 1.1 | 198 | - | - | 0.9022 | |
| | | 1.2 | 216 | - | - | 0.9018 | |
| | | 1.3 | 234 | - | - | 0.9015 | |
| | | 1.4 | 252 | - | - | 0.9029 | |
| | | 1.5 | 270 | - | - | 0.9044 | |
| | | 1.6 | 288 | - | - | 0.9049 | |
| | | 1.7 | 306 | - | - | 0.9051 | |
| | | 1.8 | 324 | - | - | 0.9033 | |
| | | 1.9 | 342 | - | - | 0.9039 | |
| | | 2.0 | 360 | - | - | 0.9050 | |
| | | 2.1 | 378 | - | - | 0.9042 | |
| | | 2.2 | 396 | - | - | 0.9041 | |
| | | 2.3 | 414 | - | - | 0.9040 | |
| | | 2.4 | 432 | - | - | 0.9048 | |
| | | 2.5 | 450 | - | - | 0.9045 | |
| | | 2.6 | 468 | - | - | 0.9046 | |
| | | 2.7 | 486 | - | - | 0.9047 | |
| | | 2.7778 | 500 | 0.0153 | - | - | |
| | | 2.8 | 504 | - | - | 0.9057 | |
| | | 2.9 | 522 | - | - | 0.9065 | |
| | | 3.0 | 540 | - | - | 0.9074 | |
| | | 3.1 | 558 | - | - | 0.9073 | |
| | | 3.2 | 576 | - | - | 0.9065 | |
| | | 3.3 | 594 | - | - | 0.9046 | |
| | | 3.4 | 612 | - | - | 0.9057 | |
| | | 3.5 | 630 | - | - | 0.9069 | |
| | | 3.6 | 648 | - | - | 0.9062 | |
| | | 3.7 | 666 | - | - | 0.9061 | |
| | | 3.8 | 684 | - | - | 0.9050 | |
| | | 3.9 | 702 | - | - | 0.9050 | |
| | | 4.0 | 720 | - | - | 0.9048 | |
| | | 4.1 | 738 | - | - | 0.9052 | |
| | | 4.2 | 756 | - | - | 0.9055 | |
| | | 4.3 | 774 | - | - | 0.9060 | |
| | | 4.4 | 792 | - | - | 0.9059 | |
| | | 4.5 | 810 | - | - | 0.9064 | |
| | | 4.6 | 828 | - | - | 0.9063 | |
| | | 4.7 | 846 | - | - | 0.9063 | |
| | | 4.8 | 864 | - | - | 0.9067 | |
| | | 4.9 | 882 | - | - | 0.9059 | |
| | | 5.0 | 900 | - | - | 0.9052 | |
| | | 5.1 | 918 | - | - | 0.9061 | |
| | | 5.2 | 936 | - | - | 0.9057 | |
| | | 5.3 | 954 | - | - | 0.9053 | |
| | | 5.4 | 972 | - | - | 0.9060 | |
| | | 5.5 | 990 | - | - | 0.9050 | |
| | | 5.5556 | 1000 | 0.0051 | - | - | |
| | | 5.6 | 1008 | - | - | 0.9053 | |
| | | 5.7 | 1026 | - | - | 0.9052 | |
| | | 5.8 | 1044 | - | - | 0.9056 | |
| | | 5.9 | 1062 | - | - | 0.9062 | |
| | | 6.0 | 1080 | - | - | 0.9056 | |
| | | 6.1 | 1098 | - | - | 0.9054 | |
| | | 6.2 | 1116 | - | - | 0.9058 | |
| | | 6.3 | 1134 | - | - | 0.9058 | |
| | | 6.4 | 1152 | - | - | 0.9056 | |
| | | 6.5 | 1170 | - | - | 0.9057 | |
| | | 6.6 | 1188 | - | - | 0.9055 | |
| | | 6.7 | 1206 | - | - | 0.9055 | |
| | | 6.8 | 1224 | - | - | 0.9053 | |
| | | 6.9 | 1242 | - | - | 0.9053 | |
| | | 7.0 | 1260 | - | - | 0.9053 | |
| | | 7.1 | 1278 | - | - | 0.9057 | |
| | | 7.2 | 1296 | - | - | 0.9055 | |
| | | 7.3 | 1314 | - | - | 0.9053 | |
| | | 7.4 | 1332 | - | - | 0.9056 | |
| | | 7.5 | 1350 | - | - | 0.9059 | |
| | | 7.6 | 1368 | - | - | 0.9060 | |
| | | 7.7 | 1386 | - | - | 0.9057 | |
| | | 7.8 | 1404 | - | - | 0.9058 | |
| | | 7.9 | 1422 | - | - | 0.9057 | |
| | | 8.0 | 1440 | - | - | 0.9058 | |
| | | 8.1 | 1458 | - | - | 0.9059 | |
| | | 8.2 | 1476 | - | - | 0.9060 | |
| | | 8.3 | 1494 | - | - | 0.9056 | |
| | | 8.3333 | 1500 | 0.0031 | - | - | |
| | | 8.4 | 1512 | - | - | 0.9057 | |
| | | 8.5 | 1530 | - | - | 0.9060 | |
| | | 8.6 | 1548 | - | - | 0.9058 | |
| | | 8.7 | 1566 | - | - | 0.9060 | |
| | | 8.8 | 1584 | - | - | 0.9062 | |
| | | 8.9 | 1602 | - | - | 0.9061 | |
| | | 9.0 | 1620 | - | - | 0.9061 | |
| | | 9.1 | 1638 | - | - | 0.9061 | |
| | | 9.2 | 1656 | - | - | 0.9059 | |
| | | 9.3 | 1674 | - | - | 0.9060 | |
| | | 9.4 | 1692 | - | - | 0.9061 | |
| | | 9.5 | 1710 | - | - | 0.9061 | |
| | | 9.6 | 1728 | - | - | 0.9061 | |
| | | 9.7 | 1746 | - | - | 0.9060 | |
| | | 9.8 | 1764 | - | - | 0.9061 | |
| | | 9.9 | 1782 | - | - | 0.9061 | |
| | | 10.0 | 1800 | - | 0.9060 | 0.9060 | |
| | |
| | </details> |
| | |
| | ### Framework Versions |
| | - Python: 3.10.12 |
| | - Sentence Transformers: 3.3.1 |
| | - Transformers: 4.47.1 |
| | - PyTorch: 2.5.1+cu121 |
| | - Accelerate: 1.2.1 |
| | - Datasets: 3.2.0 |
| | - Tokenizers: 0.21.0 |
| | |
| | ## 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 |
| |
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| | *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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