```glue_convert_examples_to_features``` for sequence labeling tasks
## 🚀 Feature

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I would like a function like ```glue_convert_examples_to_features``` for sequence labelling tasks.

## Motivation

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The motivation is that I need much better flexibility for  sequence labelling tasks. Its not enough to have a final, and decided for me model, for this task. I want just the features (a sequence of features/embeddings I guess).

## Additional context
This can be generalized to any dataset with a specific format.

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Do you mean the one already into Transformers in the [glue.py](https://github.com/huggingface/transformers/blob/d46147294852694d1dc701c72b9053ff2e726265/transformers/data/processors/glue.py) at line 30 or a different function? 

> glue_convert_examples_to_features<|||||>A different one.  Does this proposal makes sense?<|||||>> A different one. Does this proposal makes sense?

Different in which way? Describe to us please the goal and an high-level implementation.<|||||>Thanks for the reply! First of all I just want to clarify that I am not sure that my suggestion makes indeed sense. I will try to clarify: 1) This implementation ``` def glue_convert_examples_to_features(examples, tokenizer,``` is for glue datasets, so it does not cover what I suggest 2) in line 112 we see that it can only support "classification" and "regression". The classification is in sentence level. I want classification in tokens level.

So to my understanding, this function will give back one ```features``` tensor that is 1-1 correspondence with the one label for this sentence. In my case we would like n  ```features``` tensors, that will have 1-1 correspondence with the labels for this sentence, where n the number of tokens of the sentence.<|||||>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.
