readme: ## Albert xxlarge version 1 language model fine-tuned on SQuAD2.0
  
  ###  (updated 30Sept2020) with the following results:
  
  ```
  exact: 86.11134506864315
  f1: 89.35371214945009
  total': 11873
  HasAns_exact': 83.56950067476383
  HasAns_f1': 90.06353312254078
  HasAns_total': 5928
  NoAns_exact': 88.64592094196804
  NoAns_f1': 88.64592094196804
  NoAns_total': 5945
  best_exact': 86.11134506864315
  best_exact_thresh': 0.0
  best_f1': 89.35371214944985
  best_f1_thresh': 0.0
  ```
  
  ### from script:
  
  ```
  python ${EXAMPLES}/run_squad.py \
    --model_type albert \
    --model_name_or_path albert-xxlarge-v1 \
    --do_train \
    --do_eval \
    --train_file ${SQUAD}/train-v2.0.json \
    --predict_file ${SQUAD}/dev-v2.0.json \
    --version_2_with_negative \
    --do_lower_case \
    --num_train_epochs 3 \
    --max_steps 8144 \
    --warmup_steps 814 \
    --learning_rate 3e-5 \
    --max_seq_length 512 \
    --doc_stride 128 \
    --per_gpu_train_batch_size 6 \
    --gradient_accumulation_steps 8 \
    --per_gpu_eval_batch_size 48 \
    --fp16 \
    --fp16_opt_level O1 \
    --threads 12 \
    --logging_steps 50 \
    --save_steps 3000 \
    --overwrite_output_dir \
    --output_dir ${MODEL_PATH}
  ```
  
  ### using the following software & system:
  
  ```
  Transformers: 3.1.0
  PyTorch: 1.6.0
  TensorFlow: 2.3.1
  Python: 3.8.1
  OS: Linux-5.4.0-48-generic-x86_64-with-glibc2.10
  CPU/GPU: Intel i9-9900K / NVIDIA Titan RTX 24GB
  ```
  