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--- |
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tags: |
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- feature-extraction |
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pipeline_tag: feature-extraction |
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--- |
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DRAGON+ is a BERT-base sized dense retriever initialized from [RetroMAE](https://huggingface.co/Shitao/RetroMAE) and further trained on the data augmented from MS MARCO corpus, following the approach described in [How to Train Your DRAGON: |
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Diverse Augmentation Towards Generalizable Dense Retrieval](https://arxiv.org/abs/2302.07452). |
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<p align="center"> |
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<img src="https://raw.githubusercontent.com/facebookresearch/dpr-scale/main/dragon/images/teaser.png" width="600"> |
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</p> |
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The associated GitHub repository is available here https://github.com/facebookresearch/dpr-scale/tree/main/dragon. We use asymmetric dual encoder, with two distinctly parameterized encoders. The following models are also available: |
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Model | Initialization | MARCO Dev | BEIR | Query Encoder Path | Context Encoder Path |
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DRAGON+ | Shitao/RetroMAE| 39.0 | 47.4 | [facebook/dragon-plus-query-encoder](https://huggingface.co/facebook/dragon-plus-query-encoder) | [facebook/dragon-plus-context-encoder](https://huggingface.co/facebook/dragon-plus-context-encoder) |
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DRAGON-RoBERTa | RoBERTa-base | 39.4 | 47.2 | [facebook/dragon-roberta-query-encoder](https://huggingface.co/facebook/dragon-roberta-query-encoder) | [facebook/dragon-roberta-context-encoder](https://huggingface.co/facebook/dragon-roberta-context-encoder) |
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## Usage (HuggingFace Transformers) |
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Using the model directly available in HuggingFace transformers . |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained('facebook/dragon-plus-query-encoder') |
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query_encoder = AutoModel.from_pretrained('facebook/dragon-plus-query-encoder') |
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context_encoder = AutoModel.from_pretrained('facebook/dragon-plus-context-encoder') |
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# We use msmarco query and passages as an example |
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query = "Where was Marie Curie born?" |
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contexts = [ |
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"Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.", |
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"Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace." |
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] |
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# Apply tokenizer |
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query_input = tokenizer(query, return_tensors='pt') |
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ctx_input = tokenizer(contexts, padding=True, truncation=True, return_tensors='pt') |
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# Compute embeddings: take the last-layer hidden state of the [CLS] token |
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query_emb = query_encoder(**query_input).last_hidden_state[:, 0, :] |
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ctx_emb = context_encoder(**ctx_input).last_hidden_state[:, 0, :] |
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# Compute similarity scores using dot product |
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score1 = query_emb @ ctx_emb[0] # 396.5625 |
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score2 = query_emb @ ctx_emb[1] # 393.8340 |
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``` |
