Token classification for a non-textual data
### System Info

- `transformers` version: 4.24.0
- Platform: Linux-5.13.0-37-generic-x86_64-with-glibc2.29
- Python version: 3.8.10
- Huggingface_hub version: 0.10.1
- PyTorch version (GPU?): 1.10.2+cu113 (True)

### Who can help?

_No response_

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

I'm looking for an implementation of an architecture that performs token classification, but the input is not an integer that represents the vocabulary but a vector of numbers.

Basically, each token in the input is represented by a vector. Each token is already an embeddings vector.

How can this be achieved?

Best,
Vitaly



### Expected behavior

Input vector of size 768 for each token. A sequence of such tokens of up to 512.

Maybe it is as simple as removing the layer
 (word_embeddings): Embedding(50265, 768, padding_idx=1)?
In any case a link to the solution would be most helpful.

Best,
Vitaly

You should probably ask this open question on the [forums](https://discuss.huggingface.co/).<|||||>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.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.