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README.md

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+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+language:
+- es
+dataset:
+- hackathon-pln-es/parallel-sentences
+widget:
+- text: "A ver si nos tenemos que poner todos en huelga hasta cobrar lo que queramos."
+- text: "La huelga es el método de lucha más eficaz para conseguir mejoras en el salario."
+- text: "Tendremos que optar por hacer una huelga para cobrar lo que queremos."
+- text: "Queda descartada la huelga aunque no cobremos lo que queramos."
+---
+
+
+# paraphrase-spanish-distilroberta
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+We follow a **teacher-student** transfer learning approach to train an `bertin-roberta-base-spanish` model using parallel EN-ES sentence pairs.
+
+## 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 = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('sentence-transformers/paraphrase-spanish-distilroberta')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+import torch.nn.functional as F
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['Este es un ejemplo", "Cada oración es transformada']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('paraphrase-spanish-distilroberta')
+model = AutoModel.from_pretrained('paraphrase-spanish-distilroberta')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+# Normalize embeddings
+sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+  (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})
+)
+```
+
+## Evaluation Results
+
+Similarity Evaluation on STS-2017.es-en.txt and STS-2017.es-es.txt (translated manually for evaluation purposes)
+
+We measure the semantic textual similarity (STS) between sentence pairs in different languages:
+
+### ES-ES
+| cosine_pearson | cosine_spearman | manhattan_pearson | manhattan_spearman | euclidean_pearson | euclidean_spearman | dot_pearson | dot_spearman |
+| ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
+0.8495 | 0.8579 | 0.8675 | 0.8474 | 0.8676 | 0.8478 | 0.8277 | 0.8258 |
+
+### ES-EN
+| cosine_pearson | cosine_spearman | manhattan_pearson | manhattan_spearman | euclidean_pearson | euclidean_spearman | dot_pearson | dot_spearman |
+| ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
+0.8344 | 0.8448 | 0.8279 | 0.8168 | 0.8282 | 0.8159 | 0.8083 | 0.8145 |
+
+------
+
+
+## Intended uses
+
+Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures 
+the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
+
+## Background
+
+This model is a bilingual Spanish-English model trained according to instructions in the paper [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://arxiv.org/pdf/2004.09813.pdf) and the [documentation](https://www.sbert.net/examples/training/multilingual/README.html) accompanying its companion python package. We have used the strongest available pretrained English Bi-Encoder ([paraphrase-mpnet-base-v2](https://www.sbert.net/docs/pretrained_models.html#sentence-embedding-models)) as a teacher model, and the pretrained Spanish [BERTIN](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) as the student model.
+
+
+We developped this model during the 
+[Hackathon 2022 NLP - Spanish](https://somosnlp.org/hackathon), 
+organized by hackathon-pln-es Organization.
+
+### Training data
+
+We use the concatenation from multiple datasets with sentence pairs (EN-ES).
+We could check out the dataset that was used during training: [parallel-sentences](https://huggingface.co/datasets/hackathon-pln-es/parallel-sentences)
+
+| Dataset                                                  |
+|--------------------------------------------------------|
+| AllNLI - ES (SNLI + MultiNLI)|
+| EuroParl |
+| JW300 |
+| News Commentary |
+| Open Subtitles |
+| TED 2020 |
+| Tatoeba |
+| WikiMatrix |
+
+## Authors
+
+[Anibal Pérez](https://huggingface.co/Anarpego),	
+[Emilio Tomás Ariza](https://huggingface.co/medardodt),
+[Lautaro Gesuelli](https://huggingface.co/lautaro) y
+[Mauricio Mazuecos](https://huggingface.co/mmazuecos).