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README.md

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----
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
----
-
-# 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
-
-[Anibal Pérez](https://huggingface.co/Anarpego),
-[Emilio Tomás Ariza](https://huggingface.co/medardodt),
-[Lautaro Gesuelli](https://huggingface.co/Lgesuelli) y
-[Mauricio Mazuecos](https://huggingface.co/mmazuecos).
-