Paraphrase Spanish Distilroberta

🚀 paraphrase-spanish-distilroberta

This is a sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space. It can be used for tasks such as clustering or semantic search.

🚀 Quick Start

This model simplifies sentence and paragraph processing by converting them into a 768-dimensional dense vector space. It's highly useful for clustering and semantic search tasks.

✨ Features

• Teacher-Student Approach: Trained using a teacher-student transfer learning approach with parallel EN-ES sentence pairs.
• Multi-Language Compatibility: A bilingual Spanish-English model suitable for cross - language tasks.
• Versatile Applications: Can be used for clustering, semantic search, information retrieval, and sentence similarity tasks.

📦 Installation

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

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

model = SentenceTransformer('hackathon-pln-es/paraphrase-spanish-distilroberta')
embeddings = model.encode(sentences)
print(embeddings)

Advanced Usage

Without sentence-transformers, 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.

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('hackathon-pln-es/paraphrase-spanish-distilroberta')
model = AutoModel.from_pretrained('hackathon-pln-es/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)

📚 Documentation

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

ES - EN

Intended uses

Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs 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 and the documentation 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 developed this model during the Hackathon 2022 NLP - Spanish, 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)

👥 Authors

• Anibal Pérez
• Emilio Tomás Ariza
• Lautaro Gesuelli Pinto
• Mauricio Mazuecos