How to load the finetuned model for retraining from checkpoints in run_squad.py?
Because of bad internet connection and computational issues its hard for us to train a large number of epochs. We're trying to use the run_squad.py script for bangla QA system training. We have trained the model before and have the checkpoints. 

```
!python run_squad.py \
  --model_type distilbert \
  --model_name_or_path ../data/tmp/attempt_with_dataset_v3/checkpoint-5000/ \
  --do_train \
  --do_eval \
  --do_lower_case \
  --train_file ../data/dataset_v_3/train_bangla_samples.json \
  --predict_file ../data/dataset_v_3/valid_bangla_samples.json \
  --version_2_with_negative \
  --per_gpu_train_batch_size 12 \
  --learning_rate 5e-5 \
  --num_train_epochs 1.0 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --logging_steps 100 \
  --save_steps 100 \
  --fp16 \
  --evaluate_during_training \
  --output_dir ../data/mytrial
```
command produces this error : 

```
12/20/2019 14:06:45 - WARNING - __main__ -   Process rank: -1, device: cuda, n_gpu: 1, distributed training: False, 16-bits training: True
12/20/2019 14:06:47 - INFO - transformers.configuration_utils -   loading configuration file ../data/tmp/attempt_with_dataset_v3/checkpoint-5000/config.json
12/20/2019 14:06:47 - INFO - transformers.configuration_utils -   Model config {
  "activation": "gelu",
  "attention_dropout": 0.1,
  "dim": 768,
  "dropout": 0.1,
  "finetuning_task": null,
  "hidden_dim": 3072,
  "id2label": {
    "0": "LABEL_0",
    "1": "LABEL_1"
  },
  "initializer_range": 0.02,
  "is_decoder": false,
  "label2id": {
    "LABEL_0": 0,
    "LABEL_1": 1
  },
  "max_position_embeddings": 512,
  "n_heads": 12,
  "n_layers": 6,
  "num_labels": 2,
  "output_attentions": false,
  "output_hidden_states": false,
  "output_past": true,
  "pruned_heads": {},
  "qa_dropout": 0.1,
  "seq_classif_dropout": 0.2,
  "sinusoidal_pos_embds": false,
  "tie_weights_": true,
  "torchscript": false,
  "use_bfloat16": false,
  "vocab_size": 30522
}

12/20/2019 14:06:47 - INFO - transformers.tokenization_utils -   Model name '../data/tmp/attempt_with_dataset_v3/checkpoint-5000/' not found in model shortcut name list (distilbert-base-uncased, distilbert-base-uncased-distilled-squad, distilbert-base-german-cased, distilbert-base-multilingual-cased). Assuming '../data/tmp/attempt_with_dataset_v3/checkpoint-5000/' is a path or url to a directory containing tokenizer files.
12/20/2019 14:06:47 - INFO - transformers.tokenization_utils -   Didn't find file ../data/tmp/attempt_with_dataset_v3/checkpoint-5000/vocab.txt. We won't load it.
12/20/2019 14:06:47 - INFO - transformers.tokenization_utils -   Didn't find file ../data/tmp/attempt_with_dataset_v3/checkpoint-5000/added_tokens.json. We won't load it.
12/20/2019 14:06:47 - INFO - transformers.tokenization_utils -   Didn't find file ../data/tmp/attempt_with_dataset_v3/checkpoint-5000/special_tokens_map.json. We won't load it.
12/20/2019 14:06:47 - INFO - transformers.tokenization_utils -   Didn't find file ../data/tmp/attempt_with_dataset_v3/checkpoint-5000/tokenizer_config.json. We won't load it.
Traceback (most recent call last):
  File "run_squad.py", line 614, in <module>
    main()
  File "run_squad.py", line 528, in main
    cache_dir=args.cache_dir if args.cache_dir else None)
  File "/content/gdrive/My Drive/huggingfaceattempt/transformers/transformers/tokenization_utils.py", line 302, in from_pretrained
    return cls._from_pretrained(*inputs, **kwargs)
  File "/content/gdrive/My Drive/huggingfaceattempt/transformers/transformers/tokenization_utils.py", line 370, in _from_pretrained
    list(cls.vocab_files_names.values())))
OSError: Model name '../data/tmp/attempt_with_dataset_v3/checkpoint-5000/' was not found in tokenizers model name list (distilbert-base-uncased, distilbert-base-uncased-distilled-squad, distilbert-base-german-cased, distilbert-base-multilingual-cased). We assumed '../data/tmp/attempt_with_dataset_v3/checkpoint-5000/' was a path or url to a directory containing vocabulary files named ['vocab.txt'] but couldn't find such vocabulary files at this path or url.
```
but none of the checkpoint folders have any vocabulary stored. Are we supposed to pass the checkpoint folder path to the model_name_or_path for training again from that checkpoint?
Ah, turns out to run from a pretrained model we have to specify the output_dir as the previous checkpoint. I feel like its quite unintuitive. <|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
