| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:44114 |
| - loss:ContrastiveLoss |
| widget: |
| - source_sentence: The Sadrist movement left the Alliance before the elections in |
| December 2005 , which also brought the Iraqi National Congress more firmly to |
| the Alliance . |
| sentences: |
| - The Iraqi National Congress left the Alliance before the December 2005 elections |
| , which also brought the Sadrist movement more to the Alliance . |
| - He pioneered important developments in the style of sculpting in wood , parallel |
| to those driven by Filippo Parodi in marble sculpture and Domenico Piola in painting |
| . |
| - The Mine South Deep is a large mine in the northern part of Gauteng in South Africa |
| . |
| - source_sentence: Mike Monroney was challenged by A.S. Thomas in the Democratic Prefix |
| in 1950 . |
| sentences: |
| - was challenged in 1950 by A.S. Mike Monroney in the Democratic Primary . |
| - The T helper cells then activate the B cells , which are also in the presence |
| of these antigens , causing the production of autoantibodies . |
| - Illinois Route 158 , or Washington Avenue , leads west to Columbia and east to |
| Belleville . |
| - source_sentence: Morrow can mean either the next day in particular or the future |
| in general . |
| sentences: |
| - Brockton is located approximately 25 miles northeast of Providence , Rhode Island |
| and 30 miles south of Boston . |
| - He had been in the state playing for Melbourne , but moved to Victoria in 1925 |
| and appointed New Town . |
| - Morrow can either mean the next day in general , or the future in particular . |
| - source_sentence: Fotbal Club Forex Braşov was a Romanian professional club from |
| Braşov , Romania , who was founded in October 2002 and was dissolved in 2011 . |
| sentences: |
| - Fotbal Club Forex Braşov was a Romanian professional club from Braşov , Romania |
| , which was dissolved in October 2002 and was founded in 2011 . |
| - Nate decides to struggle for Ricky and confirms his love for her . |
| - Ricardo Lingan Baccay was ordained a priest on April 10 , 1987 by Diosdado Aenlle |
| Talamayan . |
| - source_sentence: He was born in July 1973 in Petroupoli ( Athens ) . |
| sentences: |
| - Carmen Aub Romero ( born October 24 , 1989 in Mexico City , DF , Mexico ) is a |
| Mexican actress . |
| - He was born in Athens in July 1973 ( Petroupoli ) . |
| - At the age of nine , Garcia appeared in his first concert and since then has appeared |
| alone or with his aunt and his uncle in all parts of France . |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy |
| - cosine_accuracy_threshold |
| - cosine_f1 |
| - cosine_f1_threshold |
| - cosine_precision |
| - cosine_recall |
| - cosine_ap |
| - cosine_mcc |
| model-index: |
| - name: SentenceTransformer |
| results: |
| - task: |
| type: binary-classification |
| name: Binary Classification |
| dataset: |
| name: paws val deberta |
| type: paws-val-deberta |
| metrics: |
| - type: cosine_accuracy |
| value: 0.9121457489878543 |
| name: Cosine Accuracy |
| - type: cosine_accuracy_threshold |
| value: 0.8481842279434204 |
| name: Cosine Accuracy Threshold |
| - type: cosine_f1 |
| value: 0.9024280575539567 |
| name: Cosine F1 |
| - type: cosine_f1_threshold |
| value: 0.8432618379592896 |
| name: Cosine F1 Threshold |
| - type: cosine_precision |
| value: 0.8860927152317881 |
| name: Cosine Precision |
| - type: cosine_recall |
| value: 0.9193770041227668 |
| name: Cosine Recall |
| - type: cosine_ap |
| value: 0.9503471324249102 |
| name: Cosine Ap |
| - type: cosine_mcc |
| value: 0.8230430822451054 |
| name: Cosine Mcc |
| --- |
| |
| # SentenceTransformer |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model trained. 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:** [Unknown](https://huggingface.co/unknown) --> |
| - **Maximum Sequence Length:** 64 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/huggingface/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': 64, 'do_lower_case': False, 'architecture': 'DebertaV2Model'}) |
| (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("sentence_transformers_model_id") |
| # Run inference |
| sentences = [ |
| 'He was born in July 1973 in Petroupoli ( Athens ) .', |
| 'He was born in Athens in July 1973 ( Petroupoli ) .', |
| 'At the age of nine , Garcia appeared in his first concert and since then has appeared alone or with his aunt and his uncle in all parts of France .', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 768] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(embeddings, embeddings) |
| print(similarities) |
| # tensor([[1.0000, 0.9386, 0.5843], |
| # [0.9386, 1.0000, 0.5614], |
| # [0.5843, 0.5614, 1.0000]]) |
| ``` |
|
|
| <!-- |
| ### 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 |
|
|
| #### Binary Classification |
|
|
| * Dataset: `paws-val-deberta` |
| * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------------|:-----------| |
| | cosine_accuracy | 0.9121 | |
| | cosine_accuracy_threshold | 0.8482 | |
| | cosine_f1 | 0.9024 | |
| | cosine_f1_threshold | 0.8433 | |
| | cosine_precision | 0.8861 | |
| | cosine_recall | 0.9194 | |
| | **cosine_ap** | **0.9503** | |
| | cosine_mcc | 0.823 | |
| |
| <!-- |
| ## 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: 44,114 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: 8 tokens</li><li>mean: 25.39 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 25.47 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence_0 | sentence_1 | label | |
| |:----------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| |
| | <code>The Song of Ceylon is a 1934 British documentary film produced by Basil Wright and directed by John Grierson for the Ceylon Tea Propaganda Board .</code> | <code>The Song of Ceylon is a British documentary film directed by Basil Wright by John Grierson for the Ceylon Tea Propaganda Board in 1934 .</code> | <code>0.0</code> | |
| | <code>The two leased aircraft were returned to the BAE Systems lessor on 9 November 2006 .</code> | <code>Centavia 's two leased aircraft were returned to the lessor , BAE Systems , on November 9 , 2006 .</code> | <code>1.0</code> | |
| | <code>When , in 1818 , Ortona was assigned to Lanciano , Campli was joined to the diocese of Teramo .</code> | <code>When Ortona was assigned to Lanciano in 1818 , Campli was connected to the Diocese of Teramo .</code> | <code>1.0</code> | |
| * Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: |
| ```json |
| { |
| "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", |
| "margin": 0.5, |
| "size_average": true |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `num_train_epochs`: 2 |
| - `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`: 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 |
| - `num_train_epochs`: 2 |
| - `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 |
| - `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} |
| - `parallelism_config`: None |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `adafactor`: False |
| - `group_by_length`: False |
| - `length_column_name`: length |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `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 |
| - `hub_revision`: None |
| - `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 |
| - `include_tokens_per_second`: False |
| - `include_num_input_tokens_seen`: no |
| - `neftune_noise_alpha`: None |
| - `optim_target_modules`: None |
| - `batch_eval_metrics`: False |
| - `eval_on_start`: False |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: True |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: round_robin |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | paws-val-deberta_cosine_ap | |
| |:------:|:----:|:-------------:|:--------------------------:| |
| | 0.1813 | 500 | 0.0314 | - | |
| | 0.3626 | 1000 | 0.023 | - | |
| | 0.5439 | 1500 | 0.0188 | - | |
| | 0.7252 | 2000 | 0.0161 | - | |
| | 0.9065 | 2500 | 0.0148 | - | |
| | 1.0 | 2758 | - | 0.9361 | |
| | 1.0877 | 3000 | 0.0121 | - | |
| | 1.2690 | 3500 | 0.0107 | - | |
| | 1.4503 | 4000 | 0.01 | - | |
| | 1.6316 | 4500 | 0.0098 | - | |
| | 1.8129 | 5000 | 0.0094 | - | |
| | 1.9942 | 5500 | 0.0091 | - | |
| | 2.0 | 5516 | - | 0.9503 | |
| |
| |
| ### Framework Versions |
| - Python: 3.12.12 |
| - Sentence Transformers: 5.2.0 |
| - Transformers: 4.57.3 |
| - PyTorch: 2.9.0+cu126 |
| - Accelerate: 1.12.0 |
| - Datasets: 4.0.0 |
| - Tokenizers: 0.22.1 |
| |
| ## 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", |
| } |
| ``` |
| |
| #### ContrastiveLoss |
| ```bibtex |
| @inproceedings{hadsell2006dimensionality, |
| author={Hadsell, R. and Chopra, S. and LeCun, Y.}, |
| booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, |
| title={Dimensionality Reduction by Learning an Invariant Mapping}, |
| year={2006}, |
| volume={2}, |
| number={}, |
| pages={1735-1742}, |
| doi={10.1109/CVPR.2006.100} |
| } |
| ``` |
| |
| <!-- |
| ## 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.* |
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