how to finetune based huggingface: run_glue.py
python transformers-master/examples/pytorch/text-classification/run_glue.py \
  --model_name_or_path chinese_bert-base \
  --train_file=transformers-master/dataset/class/train.csv \
  --validation_file=transformers-master/dataset/class/dev.csv \
  --do_train \
  --do_eval \
  --max_seq_length 128 \
  --per_device_train_batch_size 32 \
  --learning_rate 2e-5 \
  --num_train_epochs 3 \
  --output_dir output

When I use run_glue.py, and train.csv/dev.csv data format has two columns which contain “sentence” and “label”.
But It reports an error "pandas.errors. ParserError: Error tokenizing data. C error: Expected 1 fields in line 10, saw 2".
What is reason of this error?  It is the data format error?


Can you provide a Colab notebook to reproduce your issue?<|||||>what is the format of the classification data which used in run_glue.py?<|||||>The [README](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) mentions that "your own data in a csv or a JSON file (the script might need some tweaks in that case, refer to the comments inside for help)". Looking at the comments, it says:

    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
    # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
    # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
    # label if at least two columns are provided.
    #
    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
    # single column. You can easily tweak this behavior (see below)

A bit further down, the dataset is read as follows:
```
from datasets import load_dataset

data_files = {"train": data_args.train_file, "validation": data_args.validation_file}
raw_datasets = load_dataset("csv", data_files=data_files, cache_dir=model_args.cache_dir)
```

You can perhaps isolate the error by running the code above.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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