| --- |
| language: |
| - en |
| - ca |
| license: apache-2.0 |
| library_name: sentence-transformers |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dataset_size:1K<n<10K |
| - loss:CoSENTLoss |
| base_model: microsoft/mpnet-base |
| metrics: |
| - pearson_cosine |
| - spearman_cosine |
| - pearson_manhattan |
| - spearman_manhattan |
| - pearson_euclidean |
| - spearman_euclidean |
| - pearson_dot |
| - spearman_dot |
| - pearson_max |
| - spearman_max |
| widget: |
| - source_sentence: Dia Internacional del Nen Prematur |
| sentences: |
| - Premiats a les comarques de Barcelona |
| - Les concordances són adjectiu / substantiu o verb / substantiu. |
| - Els Mossos en busquen un altre, que va aconseguir fugir en ser enxampats 'in fraganti' |
| - source_sentence: Vulneració del dret a la llibertat |
| sentences: |
| - Vulneració del dret a un jutge imparcial |
| - Detenen un home a Malgrat de Mar per apallissar un escombriaire |
| - La víctima ha rebut un cop de puny i ha caigut a terra inconscient |
| - source_sentence: Agafem un taxi i ens plantem allà. |
| sentences: |
| - És una activitat gratuïta oberta al públic general. |
| - El líder del PSC, Miquel Iceta, serà el nou president del Senat |
| - El PSOE ja no descarta l’aplicació de l’article 155 de la Constitució a Catalunya |
| - source_sentence: No ho entenc, però és el que hi ha. |
| sentences: |
| - és dels plats que a casa ens encanten! |
| - El Punt d'Informació Juvenil és el servei més actiu del centre. |
| - Puigdemont reunirà dimecres a Bèlgica els diputats de JxCat |
| - source_sentence: Però que hi ha de cert en tot això? |
| sentences: |
| - Però, què hi ha de veritat en tot això? |
| - Els camioners dissolen la marxa lenta a les rondes de Barcelona |
| - El 112 atén 747.730 trucades durant el primer semestre, un 9,6% més que l'any |
| passat |
| pipeline_tag: sentence-similarity |
| model-index: |
| - name: MPNet base trained on semantic text similarity |
| results: |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: Unknown |
| type: unknown |
| metrics: |
| - type: pearson_cosine |
| value: 0.9369799393019737 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.991833254558149 |
| name: Spearman Cosine |
| - type: pearson_manhattan |
| value: 0.9582116235734125 |
| name: Pearson Manhattan |
| - type: spearman_manhattan |
| value: 0.9876060961452328 |
| name: Spearman Manhattan |
| - type: pearson_euclidean |
| value: 0.9594231143506534 |
| name: Pearson Euclidean |
| - type: spearman_euclidean |
| value: 0.9887559900790531 |
| name: Spearman Euclidean |
| - type: pearson_dot |
| value: 0.9469313911363318 |
| name: Pearson Dot |
| - type: spearman_dot |
| value: 0.9834282009396937 |
| name: Spearman Dot |
| - type: pearson_max |
| value: 0.9594231143506534 |
| name: Pearson Max |
| - type: spearman_max |
| value: 0.991833254558149 |
| name: Spearman Max |
| - type: pearson_cosine |
| value: 0.5855972037779524 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.5854785473306573 |
| name: Spearman Cosine |
| - type: pearson_manhattan |
| value: 0.5881281979560977 |
| name: Pearson Manhattan |
| - type: spearman_manhattan |
| value: 0.578667646485271 |
| name: Spearman Manhattan |
| - type: pearson_euclidean |
| value: 0.5851079475768374 |
| name: Pearson Euclidean |
| - type: spearman_euclidean |
| value: 0.5754637407144132 |
| name: Spearman Euclidean |
| - type: pearson_dot |
| value: 0.5612927132777441 |
| name: Pearson Dot |
| - type: spearman_dot |
| value: 0.5630862098985 |
| name: Spearman Dot |
| - type: pearson_max |
| value: 0.5881281979560977 |
| name: Pearson Max |
| - type: spearman_max |
| value: 0.5854785473306573 |
| name: Spearman Max |
| - type: pearson_cosine |
| value: 0.6501162382185041 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.6819594226888658 |
| name: Spearman Cosine |
| - type: pearson_manhattan |
| value: 0.6517756634326819 |
| name: Pearson Manhattan |
| - type: spearman_manhattan |
| value: 0.6701084565797553 |
| name: Spearman Manhattan |
| - type: pearson_euclidean |
| value: 0.6553647425414415 |
| name: Pearson Euclidean |
| - type: spearman_euclidean |
| value: 0.675292747578234 |
| name: Spearman Euclidean |
| - type: pearson_dot |
| value: 0.6350099608995957 |
| name: Pearson Dot |
| - type: spearman_dot |
| value: 0.6458150666120989 |
| name: Spearman Dot |
| - type: pearson_max |
| value: 0.6553647425414415 |
| name: Pearson Max |
| - type: spearman_max |
| value: 0.6819594226888658 |
| name: Spearman Max |
| --- |
| |
| # MPNet base trained on semantic text similarity |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [projecte-aina/sts-ca](https://huggingface.co/datasets/projecte-aina/sts-ca) 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Output Dimensionality:** 768 tokens |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - [projecte-aina/sts-ca](https://huggingface.co/datasets/projecte-aina/sts-ca) |
| - **Languages:** en, ca |
| - **License:** apache-2.0 |
|
|
| ### 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: 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}) |
| ) |
| ``` |
|
|
| ## 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("pauhidalgoo/finetuned-sts-ca-mpnet-base") |
| # Run inference |
| sentences = [ |
| 'Però que hi ha de cert en tot això?', |
| 'Però, què hi ha de veritat en tot això?', |
| 'Els camioners dissolen la marxa lenta a les rondes de Barcelona', |
| ] |
| 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 |
|
|
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | pearson_cosine | 0.937 | |
| | **spearman_cosine** | **0.9918** | |
| | pearson_manhattan | 0.9582 | |
| | spearman_manhattan | 0.9876 | |
| | pearson_euclidean | 0.9594 | |
| | spearman_euclidean | 0.9888 | |
| | pearson_dot | 0.9469 | |
| | spearman_dot | 0.9834 | |
| | pearson_max | 0.9594 | |
| | spearman_max | 0.9918 | |
| |
| #### 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 | 0.5856 | |
| | **spearman_cosine** | **0.5855** | |
| | pearson_manhattan | 0.5881 | |
| | spearman_manhattan | 0.5787 | |
| | pearson_euclidean | 0.5851 | |
| | spearman_euclidean | 0.5755 | |
| | pearson_dot | 0.5613 | |
| | spearman_dot | 0.5631 | |
| | pearson_max | 0.5881 | |
| | spearman_max | 0.5855 | |
| |
| #### 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 | 0.6501 | |
| | **spearman_cosine** | **0.682** | |
| | pearson_manhattan | 0.6518 | |
| | spearman_manhattan | 0.6701 | |
| | pearson_euclidean | 0.6554 | |
| | spearman_euclidean | 0.6753 | |
| | pearson_dot | 0.635 | |
| | spearman_dot | 0.6458 | |
| | pearson_max | 0.6554 | |
| | spearman_max | 0.682 | |
| |
| <!-- |
| ## 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 |
| |
| #### projecte-aina/sts-ca |
| |
| * Dataset: [projecte-aina/sts-ca](https://huggingface.co/datasets/projecte-aina/sts-ca) |
| * Size: 2,073 training samples |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence1 | sentence2 | label | |
| |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 10 tokens</li><li>mean: 32.36 tokens</li><li>max: 82 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 30.57 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.56</li><li>max: 5.0</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | label | |
| |:-------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------| |
| | <code>Atorga per primer cop les mencions Encarna Sanahuja a la inclusió de la perspectiva de gènere en docència Universitària</code> | <code>Creen la menció M. Encarna Sanahuja a la inclusió de la perspectiva de gènere en docència universitària</code> | <code>3.5</code> | |
| | <code>Finalment, afegiu-hi els bolets que haureu saltat en una paella amb oli i deixeu-ho coure tot junt durant 5 minuts.</code> | <code>Finalment, poseu-hi les minipastanagues tallades a dauets, els pèsols, rectifiqueu-ho de sal i deixeu-ho coure tot junt durant un parell de minuts més.</code> | <code>1.25</code> | |
| | <code>El TC suspèn el pla d'acció exterior i de relacions amb la UE de la Generalitat</code> | <code>El Constitucional manté la suspensió del pla estratègic d'acció exterior i relacions amb la UE</code> | <code>3.6700000762939453</code> | |
| * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "pairwise_cos_sim" |
| } |
| ``` |
| |
| ### Evaluation Dataset |
| |
| #### projecte-aina/sts-ca |
| |
| * Dataset: [projecte-aina/sts-ca](https://huggingface.co/datasets/projecte-aina/sts-ca) |
| * Size: 500 evaluation samples |
| * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence1 | sentence2 | label | |
| |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 10 tokens</li><li>mean: 32.94 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 31.42 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.58</li><li>max: 5.0</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | label | |
| |:---------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|:--------------------------------| |
| | <code>L'euríbor puja una centèsima fins el -0,189% al gener després de setze mesos de caigudes</code> | <code>La morositat de bancs i caixes repunta moderadament fins el 9,44%, després d'onze mesos de caigudes</code> | <code>1.6699999570846558</code> | |
| | <code>Demanen 3 anys de presó a l'ex treballador d'una farmàcia de Lleida per robar més de 550 unitats de Viagra i Cialis</code> | <code>L'extreballador d'una farmàcia de Lleida accepta sis mesos de presó per robar més de 500 unitats de Viagra i Cialis</code> | <code>2.0</code> | |
| | <code>Es tracta d'un jove de 20 anys que ha estat denunciat als Mossos d'Esquadra</code> | <code>Es tracta d'un jove de 21 anys que ha estat denunciat penalment pels Mossos</code> | <code>3.0</code> | |
| * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "pairwise_cos_sim" |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `per_device_train_batch_size`: 16 |
| - `per_device_eval_batch_size`: 16 |
| - `num_train_epochs`: 40 |
| - `warmup_ratio`: 0.1 |
| - `fp16`: True |
|
|
| #### 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 |
| - `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`: 40 |
| - `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 |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | spearman_cosine | |
| |:-------:|:----:|:-------------:|:---------------:| |
| | 3.8462 | 500 | 4.5209 | - | |
| | 7.6923 | 1000 | 4.1445 | - | |
| | 11.5385 | 1500 | 3.9291 | - | |
| | 15.3846 | 2000 | 3.6952 | - | |
| | 19.2308 | 2500 | 3.5393 | - | |
| | 23.0769 | 3000 | 3.3778 | - | |
| | 26.9231 | 3500 | 3.1712 | - | |
| | 30.7692 | 4000 | 2.8265 | - | |
| | 34.6154 | 4500 | 2.6265 | - | |
| | 38.4615 | 5000 | 2.3259 | - | |
| | 40.0 | 5200 | - | 0.6820 | |
| |
| |
| ### Framework Versions |
| - Python: 3.10.12 |
| - Sentence Transformers: 3.0.0 |
| - Transformers: 4.41.1 |
| - PyTorch: 2.3.0+cu121 |
| - Accelerate: 0.30.1 |
| - Datasets: 2.19.2 |
| - 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", |
| } |
| ``` |
| |
| #### CoSENTLoss |
| ```bibtex |
| @online{kexuefm-8847, |
| title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, |
| author={Su Jianlin}, |
| year={2022}, |
| month={Jan}, |
| url={https://kexue.fm/archives/8847}, |
| } |
| ``` |
| |
| <!-- |
| ## 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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