ktrapeznikov/scibert_scivocab_uncased_squad_v2
076
Model
[`allenai/scibert_scivocab_uncased`](https://huggingface.co/allenai/scibert_scivocab_uncased) fine-tuned on [`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/) using [`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)
Training Parameters
Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb
BASE_MODEL=allenai/scibert_scivocab_uncased
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_trainEvaluation
Evaluation on the dev set. I did not sweep for best threshold.
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