Create README.md

README.md

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+---
+language: en
+datasets:
+- vblagoje/lfqa
+license: mit
+---
+
+## Introduction
+The context/passage encoder model based on [DPRContextEncoder](https://huggingface.co/docs/transformers/master/en/model_doc/dpr#transformers.DPRContextEncoder) architecture. It uses the transformer's pooler outputs as context/passage representations. 
+
+## Training
+We trained vblagoje/dpr-ctx_encoder-single-lfqa-base using FAIR's dpr-scale starting with PAQ based pretrained checkpoint and fine-tuned the retriever on the question-answer pairs from the LFQA dataset. As dpr-scale requires DPR formatted training set input with positive, negative, and hard negative samples - we created a training file with an answer being positive, negatives being question unrelated answers, while hard negative samples were chosen from answers on questions between 0.55 and 0.65 of cosine similarity. 
+
+## Performance
+LFQA DPR-based retriever (vblagoje/dpr-question_encoder-single-lfqa-base and vblagoje/dpr-ctx_encoder-single-lfqa-base) had a score of 6.69 for R-precision and 14.5 for Recall@5 on KILT benchmark. 
+
+## Usage
+
+
+```python
+from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
+model = DPRQuestionEncoder.from_pretrained("vblagoje/dpr-question_encoder-single-lfqa-base").to(device)
+tokenizer = AutoTokenizer.from_pretrained("vblagoje/dpr-question_encoder-single-lfqa-base")
+input_ids = tokenizer("Why do airplanes leave contrails in the sky?", return_tensors="pt")["input_ids"]
+embeddings = model(input_ids).pooler_output
+```
+
+## Author
+- Vladimir Blagojevic: `dovlex [at] gmail.com` [Twitter](https://twitter.com/vladblagoje) | [LinkedIn](https://www.linkedin.com/in/blagojevicvladimir/)