| | --- |
| | 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/) |
