Create README.md

README.md

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+This model is the finetuned version of the pre-trained contriever model available here https://huggingface.co/facebook/contriever, following the approach described in [Towards Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). The associated GitHub repository is available here https://github.com/facebookresearch/contriever.
+
+## Usage (HuggingFace Transformers)
+Using the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding.
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained('facebook/contriever-msmarco')
+model = AutoModel.from_pretrained('facebook/contriever-msmarco')
+
+sentences = [
+    "Where was Marie Curie born?",
+    "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
+inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+outputs = model(**inputs)
+
+# Mean pooling
+def mean_pooling(token_embeddings, mask):
+    token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.)
+    sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None]
+    return sentence_embeddings
+embeddings = mean_pooling(outputs[0], inputs['attention_mask'])
+```