why the acc of chinese model(bert) is just 0.438
dataset: XNLI-1.0
i run the dataset of xnli-1.0, but the result is `acc = 0.43855421686746987`, and i run on google bert in tf, the result is `eval_accuracy = 0.7674699`. i use the same epochs and lr, i really don't know why.

i add the dataprocess of xnli ,the same with the version of tf bert:
```
class XnliProcessor(DataProcessor):
  """Processor for the XNLI data set."""

  def __init__(self):
    self.language = "zh"

  def get_train_examples(self, data_dir):
    """See base class."""
    lines = self._read_tsv(
        os.path.join(data_dir, "multinli",
                     "multinli.train.%s.tsv" % self.language))
    examples = []
    for (i, line) in enumerate(lines):
      if i == 0:
        continue
      guid = "train-%d" % (i)
      text_a = line[0]
      text_b = line[1]
      label = line[2]
      if label == "contradictory":
        label = "contradiction"
      examples.append(
          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
    return examples

  def get_dev_examples(self, data_dir):
    """See base class."""
    lines = self._read_tsv(os.path.join(data_dir, "xnli.dev.tsv"))
    examples = []
    for (i, line) in enumerate(lines):
      if i == 0:
        continue
      guid = "dev-%d" % (i)
      language = line[0]
      if language != self.language:
        continue
      text_a = line[6]
      text_b = line[7]
      label = line[1]
      examples.append(
          InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
    return examples

  def get_labels(self):
    """See base class."""
    return ["contradiction", "entailment", "neutral"]
```

 and my pytorch command is :
```
python run_glue.py --model_type bert --model_name_or_path bert-base-chinese --task_name XNLI --do_train --do_eval --do_lower_case --data_dir $XNLI_DIR --max_seq_length 128 --per_gpu_eval_batch_size=8 --per_gpu_train_batch_size=8 --learning_rate 5e-5 --num_train_epochs 2.0 --output_dir /tmp/MRPC4/ --overwrite_output_dir --save_steps=1000
```

and my tf common is:
```
python run_classifier.py   --task_name=XNLI   --do_train=true   --do_eval=true   --data_dir=$XNLI_DIR   --vocab_file=$BERT_BASE_DIR/vocab.txt   --bert_config_file=$BERT_BASE_DIR/bert_config.json   --init_checkpoint=$BERT_BASE_DIR/bert_model.ckpt   --max_seq_length=128   --train_batch_size=32   --learning_rate=5e-5   --num_train_epochs=2.0   --output_dir=/tmp/xnli_output
```

i have the same issue as you.  Do you have a good solution for it？<|||||>I met the same problem in multi-labels classification task, have no idea about the problem!<|||||>@zsk423200  Maybe you can try the "bert-base-multilingual-cased-pytorch_model" , it's performance seems better a lot  than the pure Chinese ver in my task, just a temporal solu.<|||||>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.
