Saving model errors
I am trying to fine-tune distillbert for a multilabel task using a V100 gpu and latest transformers from pip. When i try to save the model i get this error: 
```
Traceback (most recent call last):
  File "script/fine_tune_distillbert.py", line 51, in <module>
    model.save_pretrained(ROOT_DIR)
  File "/usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py", line 534, in save_pretrained
    self.save_weights(output_model_file)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py", line 2085, in save_weights
    hdf5_format.save_weights_to_hdf5_group(f, self.layers)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/saving/hdf5_format.py", line 640, in save_weights_to_hdf5_group
    param_dset = g.create_dataset(name, val.shape, dtype=val.dtype)
  File "/usr/local/lib/python3.7/dist-packages/h5py/_hl/group.py", line 143, in create_dataset
    if '/' in name:
TypeError: a bytes-like object is required, not 'str'
```

The model init:

```
model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels= max_lab)
optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)
model.compile(optimizer=optimizer, loss=model.compute_loss, metrics=['accuracy']) # can also use any keras loss fn

model.fit(train_dataset.shuffle(1000).batch(256), epochs=3, batch_size=256,
          validation_data=val_dataset.shuffle(1000).batch(256))
```

The way i am saving after sucessfuly training the model is this : 

```
import os
ROOT_DIR = os.path.abspath(os.curdir)
ROOT_DIR = ROOT_DIR + "/model"

tokenizer.save_pretrained(ROOT_DIR)
model.save_pretrained(ROOT_DIR)
```

Tokenizer is saved perfectly. I have tried to run the same code in a colab notebook ( with much less data) and it saves perfectly but when i use a service like `spell.ml` i get this error.

@LysandreJik 
Hi, I'm sorry but I don't know spell.ml or how it works; if it works in a colab notebook and saves correctly, it seems the issue comes from spell.ml rather than transformers.<|||||>This issue has been automatically marked as stale and been closed because it has not had recent activity. Thank you for your contributions.

If you think this still needs to be addressed please comment on this thread.