Initial commit

README.md

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 ---
-license: cc-by-nc-4.0
+tags:
+- feature-extraction
+pipeline_tag: feature-extraction
 ---
+DRAGON-RoBERTa is a BERT-base sized dense retriever initialized from [RoBERTa](https://huggingface.co/roberta-base) and further trained on the data augmented from MS MARCO corpus, following the approach described in [How to Train Your DRAGON:
+Diverse Augmentation Towards Generalizable Dense Retrieval](\url). The associated GitHub repository is available here https://github.com/facebookresearch/dpr-scale/tree/dragon. We use asymmetric dual encoder, with two distinctly parameterized encoders. 
+The following models are also available:
+Model | Initialization | Query Encoder Path | Context Encoder Path
+|---|---|---
+DRAGON-RoBERTa | roberta-base | facebook/dragon-roberta-query-encoder | facebook/dragon-roberta-context-encoder
+
+## Usage (HuggingFace Transformers)
+Using the model directly available in HuggingFace transformers .
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained('facebook/dragon-roberta-query-encoder')
+query_encoder = AutoModel.from_pretrained('facebook/dragon-roberta-query-encoder')
+context_encoder = AutoModel.from_pretrained('facebook/dragon-roberta-context-encoder')
+
+# We use msmarco query and passages as an example
+query =  "Where was Marie Curie born?"
+contexts = [
+    "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.",
+    "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace."
+]
+# Apply tokenizer
+query_input = tokenizer(query, return_tensors='pt')
+ctx_input = tokenizer(contexts, padding=True, truncation=True, return_tensors='pt')
+# Compute embeddings: take the last-layer hidden state of the [CLS] token
+query_emb = query_encoder(**query_input).last_hidden_state[:, 0, :]
+ctx_emb = context_encoder(**ctx_input).last_hidden_state[:, 0, :]
+# Compute similarity scores using dot product
+score1 = query_emb @ ctx_emb[0]  # 385.1422
+score2 = query_emb @ ctx_emb[1]  # 383.6051
+```

config.json

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+{
+  "architectures": [
+    "RobertaForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "type_vocab_size": 1,
+  "vocab_size": 50265
+}

pytorch_model.bin

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