Error in HuggingFace Course "Fine-tuning a pretrained model"
Author: grantdelozier
Pull request: no

New to huggingface and just going through your newly posted course.

## To reproduce

Open a google collab notebook.

Run

```
!pip install transformers[sentencepiece]
!pip install datasets
```

Then follow the steps in this chapter of the huggingface course https://huggingface.co/course/chapter3/3?fw=pt

At the step where you are told to call `trainer.train()` you see this error 

```
***** Running training *****
  Num examples = 3668
  Num Epochs = 3
  Instantaneous batch size per device = 8
  Total train batch size (w. parallel, distributed & accumulation) = 8
  Gradient Accumulation steps = 1
  Total optimization steps = 1377
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py in convert_to_tensors(self, tensor_type, prepend_batch_axis)
    698                 if not is_tensor(value):
--> 699                     tensor = as_tensor(value)
    700 

ValueError: too many dimensions 'str'

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
8 frames
<ipython-input-50-3435b262f1ae> in <module>()
----> 1 trainer.train()

/usr/local/lib/python3.7/dist-packages/transformers/trainer.py in train(self, resume_from_checkpoint, trial, **kwargs)
   1241             self.control = self.callback_handler.on_epoch_begin(args, self.state, self.control)
   1242 
-> 1243             for step, inputs in enumerate(epoch_iterator):
   1244 
   1245                 # Skip past any already trained steps if resuming training

/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py in __next__(self)
    519             if self._sampler_iter is None:
    520                 self._reset()
--> 521             data = self._next_data()
    522             self._num_yielded += 1
    523             if self._dataset_kind == _DatasetKind.Iterable and \

/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py in _next_data(self)
    559     def _next_data(self):
    560         index = self._next_index()  # may raise StopIteration
--> 561         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    562         if self._pin_memory:
    563             data = _utils.pin_memory.pin_memory(data)

/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
     45         else:
     46             data = self.dataset[possibly_batched_index]
---> 47         return self.collate_fn(data)

/usr/local/lib/python3.7/dist-packages/transformers/data/data_collator.py in __call__(self, features)
    121             max_length=self.max_length,
    122             pad_to_multiple_of=self.pad_to_multiple_of,
--> 123             return_tensors="pt",
    124         )
    125         if "label" in batch:

/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py in pad(self, encoded_inputs, padding, max_length, pad_to_multiple_of, return_attention_mask, return_tensors, verbose)
   2700                 batch_outputs[key].append(value)
   2701 
-> 2702         return BatchEncoding(batch_outputs, tensor_type=return_tensors)
   2703 
   2704     def create_token_type_ids_from_sequences(

/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py in __init__(self, data, encoding, tensor_type, prepend_batch_axis, n_sequences)
    202         self._n_sequences = n_sequences
    203 
--> 204         self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
    205 
    206     @property

/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py in convert_to_tensors(self, tensor_type, prepend_batch_axis)
    714                     )
    715                 raise ValueError(
--> 716                     "Unable to create tensor, you should probably activate truncation and/or padding "
    717                     "with 'padding=True' 'truncation=True' to have batched tensors with the same length."
    718                 )

ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.
```

## Expected behavior

I guess I expected it to start training? The error message seems incorrect since padding and truncation are already set to True.


Comments:
cc @sgugger 

I am unable to reproduce. Are you sure you don't have an old version of transformers in your Colab runtime for some reason? Could you run
``` 
! transformers-cli env
```
in a cell and paste the output here?

My apologies for the spurious issue. I did a factory reset of my runtime today and was unable to replicate the error. Thanks for the awesome library!

I get the same error described above. Doing a factory reset does not help.
The tensorflow version works fine though.

> 
>  `transformers` version: 4.16.0
> - Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
> - Python version: 3.7.12
> - PyTorch version (GPU?): 1.10.0+cu111 (False)
> - Tensorflow version (GPU?): 2.7.0 (False)
> - Flax version (CPU?/GPU?/TPU?): not installed (NA)
> - Jax version: not installed
> - JaxLib version: not installed
> - Using GPU in script?: Yes
> - Using distributed or parallel set-up in script?: No

> I am unable to reproduce. Are you sure you don't have an old version of transformers in your Colab runtime for some reason? Could you run
> 
> ```
> ! transformers-cli env
> ```
> 
> in a cell and paste the output here?

@sgugger 

This is the ouput
```
- `transformers` version: 4.18.0
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- Huggingface_hub version: 0.6.0
- PyTorch version (GPU?): 1.11.0+cu113 (True)
- Tensorflow version (GPU?): 2.8.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
```
i am trying to use 'bert-base-cased' for text classificationn, and i got the same error as OP