| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - loss:CosineSimilarityLoss |
| base_model: sentence-transformers/all-MiniLM-L6-v2 |
| widget: |
| - source_sentence: Oracle Cloud - Infrastructure and Platform Services for Enterprises |
| sentences: |
| - PulseAudio - Ubuntu Wiki |
| - Documentation page not found - Read the Docs |
| - Dwarf Fortress beginner tips - Video Games on Sports Illustrated |
| - source_sentence: Suggest opt in User Test - Google Slides |
| sentences: |
| - ReleaseEngineering/TryServer - MozillaWiki |
| - Dwarf Fortress beginner tips - Video Games on Sports Illustrated |
| - Tutanota - Private Mailbox with End-to-End Encryption and Calendar |
| - source_sentence: https://portal.naviabenefits.com/part/prioritytasks.aspx |
| sentences: |
| - What to Expect - Pregnancy and Parenting Tips, Week-by-Week Guides |
| - Parents.com - Articles, Recipes, and Ideas for Family Activities |
| - Pinterest - Boards for Collecting and Sharing Inspiration on Any Topic |
| - source_sentence: Tidal - High-Fidelity Music Streaming with Master Quality Audio |
| sentences: |
| - Walmart - Everyday Low Prices on Groceries, Electronics, and More |
| - Notion - Integrated Workspace for Notes, Tasks, Databases, and Wikis |
| - Ambient Dreams Playlist on Amazon Music |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - pearson_cosine |
| - spearman_cosine |
| model-index: |
| - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| metrics: |
| - type: pearson_cosine |
| value: 0.982180856269761 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.24020738836963906 |
| name: Spearman Cosine |
| --- |
| |
| # 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). 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) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 --> |
| - **Maximum Sequence Length:** 256 tokens |
| - **Output Dimensionality:** 384 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': 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("sentence_transformers_model_id") |
| # Run inference |
| sentences = [ |
| 'Tabletop Simulator Hub - Workshop Mods and Board Game Fans', |
| 'PC Gamer Club - Official Community for PC Gaming Enthusiasts', |
| 'Booking.com - Hotels, Homes, and Vacation Rentals Worldwide', |
| ] |
| 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] |
| ``` |
|
|
| <!-- |
| ### 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 |
|
|
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | pearson_cosine | 0.9822 | |
| | **spearman_cosine** | **0.2402** | |
| |
| <!-- |
| ## 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 |
| * Size: 49,800 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: 10 tokens</li><li>mean: 14.76 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 14.64 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.04</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | label | |
| |:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:-----------------| |
| | <code>TripAdvisor - Hotel Reviews, Photos, and Travel Forums</code> | <code>Docker Hub - Container Image Repository for DevOps Environments</code> | <code>0.0</code> | |
| | <code>Mastodon - Decentralized Social Media for Niche Communities</code> | <code>Allrecipes - User-Submitted Recipes, Reviews, and Cooking Tips</code> | <code>0.0</code> | |
| | <code>YouTube Music - Music Videos, Official Albums, and Live Performances</code> | <code>ESPN - Sports News, Live Scores, Stats, and Highlights</code> | <code>0.0</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 |
| |
| - `per_device_train_batch_size`: 32 |
| - `per_device_eval_batch_size`: 32 |
| - `num_train_epochs`: 6 |
| - `multi_dataset_batch_sampler`: round_robin |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: no |
| - `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`: 6 |
| - `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 |
| | Epoch | Step | Training Loss | spearman_cosine | |
| |:------:|:-----:|:-------------:|:---------------:| |
| | 0.0754 | 500 | 0.0216 | - | |
| | 0.1509 | 1000 | 0.0178 | - | |
| | 0.2263 | 1500 | 0.016 | - | |
| | 0.3018 | 2000 | 0.015 | - | |
| | 0.3772 | 2500 | 0.0144 | - | |
| | 0.4526 | 3000 | 0.013 | - | |
| | 0.5281 | 3500 | 0.0123 | - | |
| | 0.6035 | 4000 | 0.0119 | - | |
| | 0.6789 | 4500 | 0.0116 | - | |
| | 0.7544 | 5000 | 0.0102 | - | |
| | 0.8298 | 5500 | 0.0092 | - | |
| | 0.9053 | 6000 | 0.0087 | - | |
| | 0.9807 | 6500 | 0.0076 | - | |
| | 1.0561 | 7000 | 0.0068 | - | |
| | 1.1316 | 7500 | 0.0063 | - | |
| | 1.2070 | 8000 | 0.0061 | - | |
| | 1.2824 | 8500 | 0.0059 | - | |
| | 1.3579 | 9000 | 0.0055 | - | |
| | 1.4333 | 9500 | 0.0056 | - | |
| | 1.5088 | 10000 | 0.0045 | - | |
| | 1.5842 | 10500 | 0.004 | - | |
| | 1.6596 | 11000 | 0.0045 | - | |
| | 1.7351 | 11500 | 0.0039 | - | |
| | 1.8105 | 12000 | 0.0044 | - | |
| | 1.8859 | 12500 | 0.0036 | - | |
| | 1.9614 | 13000 | 0.0032 | - | |
| | 2.0368 | 13500 | 0.0034 | - | |
| | 2.1123 | 14000 | 0.0028 | - | |
| | 2.1877 | 14500 | 0.0029 | - | |
| | 2.2631 | 15000 | 0.0031 | - | |
| | 2.3386 | 15500 | 0.0026 | - | |
| | 2.4140 | 16000 | 0.0026 | - | |
| | 2.4894 | 16500 | 0.003 | - | |
| | 2.5649 | 17000 | 0.0027 | - | |
| | 2.6403 | 17500 | 0.0026 | - | |
| | 2.7158 | 18000 | 0.0024 | - | |
| | 2.7912 | 18500 | 0.0025 | - | |
| | 2.8666 | 19000 | 0.002 | - | |
| | 2.9421 | 19500 | 0.0022 | - | |
| | 3.0175 | 20000 | 0.0021 | - | |
| | 3.0929 | 20500 | 0.0021 | - | |
| | 3.1684 | 21000 | 0.0019 | - | |
| | 3.2438 | 21500 | 0.0021 | - | |
| | 3.3193 | 22000 | 0.002 | - | |
| | 3.3947 | 22500 | 0.0018 | - | |
| | 3.4701 | 23000 | 0.0018 | - | |
| | 3.5456 | 23500 | 0.0019 | - | |
| | 3.6210 | 24000 | 0.0017 | - | |
| | 3.6964 | 24500 | 0.0017 | - | |
| | 3.7719 | 25000 | 0.0016 | - | |
| | 3.8473 | 25500 | 0.0016 | - | |
| | 3.9228 | 26000 | 0.0015 | - | |
| | 3.9982 | 26500 | 0.0019 | - | |
| | 4.0736 | 27000 | 0.0016 | - | |
| | 4.1491 | 27500 | 0.0016 | - | |
| | 4.2245 | 28000 | 0.0015 | - | |
| | 4.2999 | 28500 | 0.0015 | - | |
| | 4.3754 | 29000 | 0.0016 | - | |
| | 4.4508 | 29500 | 0.0014 | - | |
| | 4.5263 | 30000 | 0.0015 | - | |
| | 4.6017 | 30500 | 0.0014 | - | |
| | 4.6771 | 31000 | 0.0017 | - | |
| | 4.7526 | 31500 | 0.0014 | - | |
| | 4.8280 | 32000 | 0.0016 | - | |
| | 4.9034 | 32500 | 0.0015 | - | |
| | 4.9789 | 33000 | 0.0014 | - | |
| | 5.0543 | 33500 | 0.0014 | - | |
| | 5.1298 | 34000 | 0.0013 | - | |
| | 5.2052 | 34500 | 0.0014 | - | |
| | 5.2806 | 35000 | 0.0014 | - | |
| | 5.3561 | 35500 | 0.0016 | - | |
| | 5.4315 | 36000 | 0.0013 | - | |
| | 5.5069 | 36500 | 0.0015 | - | |
| | 5.5824 | 37000 | 0.0013 | - | |
| | 5.6578 | 37500 | 0.0016 | - | |
| | 5.7333 | 38000 | 0.0015 | - | |
| | 5.8087 | 38500 | 0.0014 | - | |
| | 5.8841 | 39000 | 0.0015 | - | |
| | 5.9596 | 39500 | 0.0014 | - | |
| | -1 | -1 | - | 0.2402 | |
| |
| |
| ### Framework Versions |
| - Python: 3.11.11 |
| - Sentence Transformers: 3.4.1 |
| - Transformers: 4.48.2 |
| - PyTorch: 2.5.1+cu124 |
| - Accelerate: 1.3.0 |
| - 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.* |
| --> |
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| <!-- |
| ## Model Card Contact |
|
|
| *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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