Issue testing run_squad.py example
## ❓ Questions & Help

<!-- A clear and concise description of the question. -->

I am trying to run the squad example here

https://github.com/huggingface/transformers/blob/master/examples/run_squad.py

Using the Squad data from here

https://rajpurkar.github.io/SQuAD-explorer/

But I get this error

>ValueError: For training, each question should have exactly 1 answer.

Here is my code

```
!pip install transformers
import urllib.request

url = 'https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json'
urllib.request.urlretrieve(url, 'train-v2.0.json')

url = 'https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json'
urllib.request.urlretrieve(url, 'dev-v2.0.json')

!wget 'https://raw.githubusercontent.com/huggingface/transformers/master/examples/run_squad.py'
!wget 'https://raw.githubusercontent.com/huggingface/pytorch-transformers/master/examples/utils_squad.py'
!wget 'https://raw.githubusercontent.com/huggingface/pytorch-transformers/master/examples/utils_squad_evaluate.py'

# $SQUAD_DIR/train-v1.1.json

SQUAD_Train = '/content/train-v2.0.json'
SQUAD_Dev = '/content/dev-v2.0.json'

!python run_squad.py \
  --model_type bert \
  --model_name_or_path bert-base-cased \
  --do_train \
  --do_eval \
  --do_lower_case \
  --train_file '$SQUAD_Train' \
  --predict_file '$SQUAD_Dev' \
  --per_gpu_train_batch_size 12 \
  --learning_rate 3e-5 \
  --num_train_epochs 2.0 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir /tmp/debug_squad/

```

Here is the error message

```
11/14/2019 05:01:20 - WARNING - __main__ -   Process rank: -1, device: cuda, n_gpu: 1, distributed training: False, 16-bits training: False
11/14/2019 05:01:20 - INFO - transformers.configuration_utils -   loading configuration file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-config.json from cache at /root/.cache/torch/transformers/b945b69218e98b3e2c95acf911789741307dec43c698d35fad11c1ae28bda352.d7a3af18ce3a2ab7c0f48f04dc8daff45ed9a3ed333b9e9a79d012a0dedf87a6
11/14/2019 05:01:20 - INFO - transformers.configuration_utils -   Model config {
  "attention_probs_dropout_prob": 0.1,
  "finetuning_task": null,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "num_labels": 2,
  "output_attentions": false,
  "output_hidden_states": false,
  "output_past": true,
  "pruned_heads": {},
  "torchscript": false,
  "type_vocab_size": 2,
  "use_bfloat16": false,
  "vocab_size": 28996
}

11/14/2019 05:01:21 - INFO - transformers.tokenization_utils -   loading file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt from cache at /root/.cache/torch/transformers/5e8a2b4893d13790ed4150ca1906be5f7a03d6c4ddf62296c383f6db42814db2.e13dbb970cb325137104fb2e5f36fe865f27746c6b526f6352861b1980eb80b1
11/14/2019 05:01:21 - INFO - transformers.modeling_utils -   loading weights file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-pytorch_model.bin from cache at /root/.cache/torch/transformers/35d8b9d36faaf46728a0192d82bf7d00137490cd6074e8500778afed552a67e5.3fadbea36527ae472139fe84cddaa65454d7429f12d543d80bfc3ad70de55ac2
11/14/2019 05:01:24 - INFO - transformers.modeling_utils -   Weights of BertForQuestionAnswering not initialized from pretrained model: ['qa_outputs.weight', 'qa_outputs.bias']
11/14/2019 05:01:24 - INFO - transformers.modeling_utils -   Weights from pretrained model not used in BertForQuestionAnswering: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']
11/14/2019 05:01:27 - INFO - __main__ -   Training/evaluation parameters Namespace(adam_epsilon=1e-08, cache_dir='', config_name='', device=device(type='cuda'), do_eval=True, do_lower_case=True, do_train=True, doc_stride=128, eval_all_checkpoints=False, evaluate_during_training=False, fp16=False, fp16_opt_level='O1', gradient_accumulation_steps=1, learning_rate=3e-05, local_rank=-1, logging_steps=50, max_answer_length=30, max_grad_norm=1.0, max_query_length=64, max_seq_length=384, max_steps=-1, model_name_or_path='bert-base-cased', model_type='bert', n_best_size=20, n_gpu=1, no_cuda=False, null_score_diff_threshold=0.0, num_train_epochs=2.0, output_dir='/tmp/debug_squad/', overwrite_cache=False, overwrite_output_dir=False, per_gpu_eval_batch_size=8, per_gpu_train_batch_size=12, predict_file='/content/dev-v2.0.json', save_steps=50, seed=42, server_ip='', server_port='', tokenizer_name='', train_file='/content/train-v2.0.json', verbose_logging=False, version_2_with_negative=False, warmup_steps=0, weight_decay=0.0)
11/14/2019 05:01:27 - INFO - __main__ -   Creating features from dataset file at /content/train-v2.0.json
Traceback (most recent call last):
  File "run_squad.py", line 569, in <module>
    main()
  File "run_squad.py", line 514, in main
    train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False)
  File "run_squad.py", line 307, in load_and_cache_examples
    version_2_with_negative=args.version_2_with_negative)
  File "/content/utils_squad.py", line 152, in read_squad_examples
    "For training, each question should have exactly 1 answer.")
ValueError: For training, each question should have exactly 1 answer.
```

For convenience, here's a colab notebook with the code that you can run 

https://colab.research.google.com/drive/1tNisXX5siuNnkuEQ-X_XdEdDTtdJ0qeL
Try adding `--version_2_with_negative \` to your `run_squad.py `script.<|||||>That fixed it, thanks!