Reset README.md.

README.md

@@ -0,0 +1,114 @@
+---
+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) -->
+