Different sentiment class probabilities for sequential processing vs batch processing
### System Info

platform: windows
python: 3.7
transformers: latest
Model: finetuned BERT (from cardiffnlp/twitter-roberta-base-sentiment)

I have posted the issue here [https://discuss.huggingface.co/t/different-sentiments-when-texts-processed-in-batches-vs-singles/19462](url), but didn't receive any answer. The behavior is not really explainable and might look like a bug.

Cheers

@LysandreJik 

### Who can help?

_No response_

### Information

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

### Tasks

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

### Reproduction

see description under the above link.

### Expected behavior

I would expect the class probabilities to be equal for a given text, no matter if the classification is done sequentially or in batches.
Hi @Kayne88 -- could you please share a complete script for reproducibility? In your [original script](https://discuss.huggingface.co/t/different-sentiments-when-texts-processed-in-batches-vs-singles/19462) you were missing the definition of `tokenizer` and `model` :)<|||||>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.