Use `transformers` models as Spark estimators 
# 🚀 Feature request

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It would be great to have the possibility to use `transformers` models and pipelines for inference on massive Spark `DataFrames`.

Given a model trained the standard way, one would have a syntax of this kind (or any other):

```python
from transformers.spark import SparkWrapper
from transformers import BertForSequenceClassification
model = BertForSequenceClassification(...)
estimator = SparkWrapper(model)
estimator.fit(trainingData)
```

## Motivation

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`huggingface/transformers` models **and** tokenizers are great, but it seems they are not suitable for making inference on Big Data (example use case: I have a classifier I wanna use to make predictions on millions of sentences).

## Your contribution

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[Gluon NLP based on Apache MXNet](https://nlp.gluon.ai/) and [Spark NLP](https://nlp.johnsnowlabs.com/) seem to be the only NLP libraries that run natively on Spark. The export from `huggingface/transformers` models/tokenizers to them is not a one-liner, when it's possible. Maybe a solution for my problem would be to write conversion scripts, but I find it interesting to have the spark logic built in `transformers` anyway.

@sgugger @LysandreJik @patrickvonplaten 
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.

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