- This is the Information Retrieval HW6.
- Using BM25 and transformer-based language model to compute the relation between given querys and documents.
- All file have argument parser, please use
python3 FILENAME -h
to get more.
- Use transformer-based doing multiple choice task.
[CLS]
query[SEP]
document[SEP]
- Token length truncate to 512 by tokenizer.
- Pretrained lenguage model: bert-base-uncased
- Optimizer: AdamW
- Learning rate = 1e-5
- Split 20 documents of training queries to grid search optimal 𝜶 for BERT
- Batch size = 3
- Num. epochs = 2
- Num. of negative documents = 3
- 𝜶 from greedy search = 1.13