fill-mask target for full words not enabled?
### System Info

```shell
- `transformers` version: 4.19.2
- 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 (False)
- Tensorflow version (GPU?): 2.8.0 (False)
- 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>
```


### Who can help?

@Narsil and @LysandreJik (?)
How can one use Roberta for  fill-mask to get the **full** word candidate and its "full" score for Roberta-large? Open to workaround solutions.

My example: 
`sentence = f"Nitzsch argues against the doctrine of the annihilation of the wicked, regards the teaching of Scripture about eternal {nlp.tokenizer.mask_token} as hypothetical."`  
Notebook [here](https://colab.research.google.com/drive/12QrU5SC7kHsM0gekzjLXDJXptAkdSnuq?usp=sharing).

Using pipeline, the output I get is:
`The specified target token ` damnation` does not exist in the model vocabulary. Replacing with `Ġdamn`.`

Thanks.

### Information

- [X] The official example scripts
- [X] 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

See notebook above.

### Expected behavior

```shell
I expect to see "damnation" with its score.
```

hi @i-am-neo ,

Fill-mask works at a token level, not words, so you cannot use targets which are multi token. Since `damnation` seems to no exist directly in your vocabulary, it uses the closes `1-token` element it finds `damn`. You cannot unfortunately have `fill-mask` work with varying number of holes/tokens. You could use 2 masks instead of one for instance, but then you will need to logic "fuse" those two tokens which might not correspond to a single word.<|||||>Thanks @Narsil .  I had thought so. No plans to allow full words and regex in your roadmap?<|||||>It's not something that fits the current `pipeline` model (at least in the default settings).

`pipeline` is aimed to make ML model usable without any ML specific knowledge, BUT never hiding any complexities it induces.

In this particular part, `fill-mask` model, do work on a token level, and trying to do `word-level` really requires some custom strategies (how many tokens is your word? Do you want to handle multiple size  of tokens ?). How to resolve in case of multi tokens (since multi tokens will give you independent token probabilities, and not grouped probabilities). 

Since it is a non trivial problem, we decide to not do it on behalf of users and give an output that is much closer to what the original model does. If simple strategies can be implemented maybe we can add them as opt-in parameters, but so far nothing is being worked on as far as I know. PRs are more than welcome.

If you want more background for instance, this PR might be valuable to read (and the linked PRs too); https://github.com/huggingface/transformers/pull/10222

I would like to point out `zero-shot-classification` which although not being the same pipeline we have seen being used in a similar fashion, which might suit your needs.

side note: An easy start solution for regexp is to fetch all tokens in the vocabulary that start with your prefix and use them as targets `targets=[word for word in tokenizer.get_vocab() if word.startswith("X")]` for instance. It's not all possible english words, but at least all possible elements of the vocabulary that will work.<|||||>I hear you @Narsil, it sure is non-trivial.

In my case, I would like a large-enough LM (for example, Roberta-large) to generate word candidates to start with, given some regex as hints/constraints, _without knowing in advance what the best candidates are, except for those hints_.  My thinking is that the candidates the LM generates would more or less already fit into the context given to the model.  Multiple candidates would be ranked post-fill by their scores.

Re `zero-shot-classification`, the trouble is without knowing in advance what the correct/best candidates are, it's more difficult to work it in.<|||||>> In my case, I would like a large-enough LM (for example, Roberta-large) to generate word candidates to start with, given some regex as hints/constraints, without knowing in advance what the best candidates are, except for those hints. 

I think there would be a **lot of value** to be able to do that, but AFAIK there's no simple way to do that with bert-like models. I think the biggest culprit is that models are trained to give independant probabilities, and not joint ones. Solving it might require an entire new training objective.

`This house is <mask> and <mask>`:

Disjoint probabilities: (big: 50%, red: 50) (big: 50%, red: 50%)
Joint probabilities: ( (big, red, 50%) , (red, big, 50%) ) . (Btu then (big, big = 0% for instance, which is allowed in disjoint probabilities)<|||||>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.