| ### 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/run_squad.py)** | |
| ### Training Parameters | |
| Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb | |
| ```bash | |
| 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_train | |
| ``` | |
| ### Evaluation | |
| Evaluation on the dev set. I did not sweep for best threshold. | |
| | | val | | |
| |-------------------|-------------------| | |
| | exact | 75.07790785816559 | | |
| | f1 | 78.47735207283013 | | |
| | total | 11873.0 | | |
| | HasAns_exact | 70.76585695006747 | | |
| | HasAns_f1 | 77.57449412292718 | | |
| | HasAns_total | 5928.0 | | |
| | NoAns_exact | 79.37762825904122 | | |
| | NoAns_f1 | 79.37762825904122 | | |
| | NoAns_total | 5945.0 | | |
| | best_exact | 75.08633032931863 | | |
| | best_exact_thresh | 0.0 | | |
| | best_f1 | 78.48577454398324 | | |
| | best_f1_thresh | 0.0 | | |
| ### Usage | |
| See [huggingface documentation](https://huggingface.co/transformers/model_doc/bert.html#bertforquestionanswering). Training on `SQuAD V2` allows the model to score if a paragraph contains an answer: | |
| ```python | |
| 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 | |
| ``` | |