automatically generated code
Author: yunyicheng
Pull request: yes

# What does this PR do?

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Adding a new transformers model MassFormer based on Graphormer for mass spectrum prediction.


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR. 

- original implementation by : @adamoyoung
- graph models: @clefourrier

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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @pacman100

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc and @younesbelkada

Documentation: @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

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Comments:
Hi @yunyicheng ,
Thank you for wanting to contribute! I think this model should be provided/used as custom code rather than be added directly into `transformers`.


Hello @clefourrier , thank you for your reply! Even though Massformer is partially built upon Graphormer, it has many differences in architecture. Thus I think it is better to add a new model to transformers. Could you tell me your reasons for making it a custom model rather than a new model? I would like to hear them before making a decision on how I should proceed.

Hi @yunyicheng ,
As adding models to the core of transformers creates a lot of maintenance work for the team, as well as significant time and effort for contributors to match the general lib design, we only add popular and important models for the community, which we see through how used the models are. 
It would therefore be better to first add your model as custom code on the hub, then see how much it is used by the general community. It will also be easier to integrate it! Here is a [tutorial](https://huggingface.co/docs/transformers/custom_models).

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.