readme: ### Model
  **[`albert-xlarge-v2`](https://huggingface.co/albert-xlarge-v2)** 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
  ```bash
  BASE_MODEL=albert-xlarge-v2
  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 3 \
    --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 814 \
    --gradient_accumulation_steps 4 \
    --fp16 \
    --do_train
  ```
    
  ### Evaluation
  
  Evaluation on the dev set. I did not sweep for best threshold.
  
  |                   | val               |
  |-------------------|-------------------|
  | exact             | 84.41842836688285 |
  | f1                | 87.4628460501696  |
  | total             | 11873.0           |
  | HasAns_exact      | 80.68488529014844 |
  | HasAns_f1         | 86.78245127423482 |
  | HasAns_total      | 5928.0            |
  | NoAns_exact       | 88.1412952060555  |
  | NoAns_f1          | 88.1412952060555  |
  | NoAns_total       | 5945.0            |
  | best_exact        | 84.41842836688285 |
  | best_exact_thresh | 0.0               |
  | best_f1           | 87.46284605016956 |
  | best_f1_thresh    | 0.0               |
  
  
  ### Usage
  
  See [huggingface documentation](https://huggingface.co/transformers/model_doc/albert.html#albertforquestionanswering). 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
      
  ```
  
  