readme: python run_squad.py
  --model_name_or_path google/canine-s
  --do_train
  --do_eval
  --per_gpu_train_batch_size 1
  --per_gpu_eval_batch_size 1
  --gradient_accumulation_steps 128
  --learning_rate 3e-5
  --num_train_epochs 3
  --max_seq_length 1024
  --doc_stride 128
  --max_answer_length 240
  --output_dir canine-s-squad
  --model_type bert
  
  {
    "_name_or_path": "google/canine-s",
    "architectures": [
      "CanineForQuestionAnswering"
    ],
    "attention_probs_dropout_prob": 0.1,
    "bos_token_id": 57344,
    "downsampling_rate": 4,
    "eos_token_id": 57345,
    "hidden_act": "gelu",
    "hidden_dropout_prob": 0.1,
    "hidden_size": 768,
    "initializer_range": 0.02,
    "intermediate_size": 3072,
    "layer_norm_eps": 1e-12,
    "local_transformer_stride": 128,
    "max_position_embeddings": 16384,
    "model_type": "canine",
    "num_attention_heads": 12,
    "num_hash_buckets": 16384,
    "num_hash_functions": 8,
    "num_hidden_layers": 12,
    "pad_token_id": 0,
    "torch_dtype": "float32",
    "transformers_version": "4.19.0.dev0",
    "type_vocab_size": 16,
    "upsampling_kernel_size": 4,
    "use_cache": true
  }
  
  {'exact': 64.70198675496688, 'f1': 76.57594921776277}