Training TFBertForSequenceClassification with custom X and Y data
I am working on a TextClassification problem, for which I am trying to traing my model on TFBertForSequenceClassification given in huggingface-transformers library.

I followed the example given on their github page, I am able to run the sample code with given sample data using tensorflow_datasets.load('glue/mrpc'). However, I am unable to find an example on how to load my own custom data and pass it in  model.fit(train_dataset, epochs=2, steps_per_epoch=115, validation_data=valid_dataset, validation_steps=7).

How can I define my own X, do tokenization of my X and prepare train_dataset with my X and Y. Where X represents my input text and Y represents classification category of given X.

Sample Training dataframe :
```
    text    category_index
0   Assorted Print Joggers - Pack of 2 ,/ Gray Pri...   0
1   "Buckle" ( Matt ) for 35 mm Width Belt  0
2   (Gagam 07) Barcelona Football Jersey Home 17 1...   2
3   (Pack of 3 Pair) Flocklined Reusable Rubber Ha...   1
4   (Summer special Offer)Firststep new born baby ...   0
```
```
Question already asked on SO : 
https://stackoverflow.com/questions/60463829/training-tfbertforsequenceclassification-with-custom-x-and-y-data
```
Maybe this is a little late but you could take a look in both `examples/run_tf_glue.py` and [this function](https://github.com/huggingface/transformers/blob/master/src/transformers/data/processors/glue.py#L31-L168) from`src/transformers/data/processors/glue.py` and write a custom training script based from those.<|||||>To make things a little more concrete, I've written and annotated [an end-to-end example](https://gist.github.com/papapabi/124c6ac406e6bbd1f28df732e953ac6d) of how to fine-tune a `bert-base-cased` model from your `DataFrame`'s spec. Do comment if it helps you out!<|||||>@papapabi Thank you for your inputs. I will check this out.
<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
