Dragon Plus Context Encoder

🚀 nthakur/dragon-plus-context-encoder

This project is a port of the facebook/dragon-plus-context-encoder model to the sentence-transformers framework. It maps sentences and paragraphs into a 768-dimensional dense vector space, which can be used for tasks such as clustering or semantic search.

🚀 Quick Start

✨ Features

• Maps sentences and paragraphs to a 768-dimensional dense vector space.
• Suitable for clustering and semantic search tasks.

📦 Installation

To use this model, you need to install sentence-transformers:

pip install -U sentence-transformers

💻 Usage Examples

🔍 Basic Usage with Sentence-Transformers

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('nthakur/dragon-plus-context-encoder')
embeddings = model.encode(sentences)
print(embeddings)

🔍 Advanced Usage with HuggingFace Transformers

Without sentence-transformers, you can use the model as follows:

from transformers import AutoTokenizer, AutoModel
import torch


def cls_pooling(model_output, attention_mask):
    return model_output[0][:,0]


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('nthakur/dragon-plus-context-encoder')
model = AutoModel.from_pretrained('nthakur/dragon-plus-context-encoder')

# 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. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

📚 Documentation

🔍 Evaluation Results

For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net

🔍 Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

📄 License

This section is not provided in the original document.

📖 Citing & Authors

Have a look at DRAGON for more information.

📋 Information Table