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---
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]
```
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## 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 |
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## 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},
}
```
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