---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- language: es
---

# bertin-roberta-base-finetuning-esnli

This is a [sentence-transformers](https://www.SBERT.net) model trained on a collection of NLI tasks for Spanish. It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Based around the siamese networks approach from [this paper](https://arxiv.org/pdf/1908.10084.pdf).
<!--- Describe your model here -->

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
from sentence_transformers import SentenceTransformer
sentences = ["Este es un ejemplo", "Cada oración es transformada"]

model = SentenceTransformer('hackathon-pln-es/bertin-roberta-base-finetuning-esnli')
embeddings = model.encode(sentences)
print(embeddings)
```

## Evaluation Results

<!--- Describe how your model was evaluated -->
Our model was evaluated on the task of Semantic Textual Similarity using the [SemEval-2015 Task](https://alt.qcri.org/semeval2015/task2/) for [Spanish](http://alt.qcri.org/semeval2015/task2/data/uploads/sts2015-es-test.zip).

|                    | [BETO STS](https://huggingface.co/espejelomar/sentece-embeddings-BETO) | BERTIN STS (this model) | Relative improvement |
|-------------------:|---------:|-----------:|---------------------:|
|   cosine_pearson   | 0.609803 | 0.670862   | +10.01               |
|   cosine_spearman  | 0.528776 | 0.598593   | +13.20               |
|  euclidean_pearson | 0.590613 | 0.675257   | +14.33               |
| euclidean_spearman | 0.526529 | 0.604656   | +14.84               |
|  manhattan_pearson | 0.589108 | 0.676706   | +14.87               |
| manhattan_spearman | 0.525910 | 0.606461   | +15.32               |
|     dot_pearson    | 0.544078 | 0.586429   | +7.78                |
|    dot_spearman    | 0.460427 | 0.495614   | +7.64                |


## Training
The model was trained with the parameters:

**Dataset**

We used a collection of datasets of Natural Language Inference as training data:
 - [ESXNLI](https://raw.githubusercontent.com/artetxem/esxnli/master/esxnli.tsv), only the part in spanish
 - [SNLI](https://nlp.stanford.edu/projects/snli/), automatically translated
 - [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/), automatically translated

The whole dataset used is available [here](https://huggingface.co/hackathon-pln-es/coming-soon).

**DataLoader**:

`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 1127 with parameters:
```
{'batch_size': 64}
```

**Loss**:

`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
  ```
  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
  ```

Parameters of the fit()-Method:
```
{
    "epochs": 20,
    "evaluation_steps": 0,
    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 1127,
    "weight_decay": 0.01
}
```


## Full Model Architecture
```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 514, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (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})
  (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```

## Authors

Coming soon.

<!---[Anibal Pérez](https://huggingface.co/Anarpego) -->
<!---[Emilio Tomás Ariza](https://huggingface.co/medardodt) -->
<!---[Mauricio Mazuecos](https://huggingface.co/mmazuecos) -->