Model
monologg/biobert_v1.1_pubmed fine-tuned on SQuAD V2 using run_squad.py
This model is cased.
Training Parameters
Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb
BASE_MODEL=monologg/biobert_v1.1_pubmed
python run_squad.py \
--version_2_with_negative \
--model_type albert \
--model_name_or_path $BASE_MODEL \
--output_dir $OUTPUT_MODEL \
--do_eval \
--do_lower_case \
--train_file $SQUAD_DIR/train-v2.0.json \
--predict_file $SQUAD_DIR/dev-v2.0.json \
--per_gpu_train_batch_size 18 \
--per_gpu_eval_batch_size 64 \
--learning_rate 3e-5 \
--num_train_epochs 3.0 \
--max_seq_length 384 \
--doc_stride 128 \
--save_steps 2000 \
--threads 24 \
--warmup_steps 550 \
--gradient_accumulation_steps 1 \
--fp16 \
--logging_steps 50 \
--do_train
Evaluation
Evaluation on the dev set. I did not sweep for best threshold.
| val | |
|---|---|
| exact | 75.97068980038743 |
| f1 | 79.37043950121722 |
| total | 11873.0 |
| HasAns_exact | 74.13967611336032 |
| HasAns_f1 | 80.94892513460755 |
| HasAns_total | 5928.0 |
| NoAns_exact | 77.79646761984861 |
| NoAns_f1 | 77.79646761984861 |
| NoAns_total | 5945.0 |
| best_exact | 75.97068980038743 |
| best_exact_thresh | 0.0 |
| best_f1 | 79.37043950121729 |
| best_f1_thresh | 0.0 |
Usage
See huggingface documentation. Training on SQuAD V2 allows the model to score if a paragraph contains an answer:
start_scores, end_scores = model(input_ids)
span_scores = start_scores.softmax(dim=1).log()[:,:,None] + end_scores.softmax(dim=1).log()[:,None,:]
ignore_score = span_scores[:,0,0] #no answer scores