🚀 Add missing tokenizer test files

Several tokenizers currently have no associated tests. I think that adding the test file for one of these tokenizers could be a very good way to make a first contribution to transformers.

Tokenizers concerned

not yet claimed

• LED

• RemBert

• Splinter

• MobileBert

• ConvBert

• RetriBert

claimed

• Flaub

From paper, it mentioned

Instead, the training data generator chooses 15% of tokens at random, e.g., in the sentence my
dog is hairy it chooses hairy.

It means that 15% of token will be choose for sure.

From https://github.com/codertimo/BERT-pytorch/blob/master/bert_pytorch/dataset/dataset.py#L68,
for every single token, it has 15% of chance that go though the followup procedure.

Problem
Currently FARMReader will ask users to raise max_seq_length every time some samples are longer than the value set to it. However, this can be confusing if max_seq_length is already set to the maximum value allowed by the model, because raising it further will cause hard-to-read CUDA errors.

See #2177.

Solution
We should find a way to query the model for the maximum va

目前的多音字使用 pypinyin 或者 g2pM，精度有限，想做一个基于 BERT (或者 ERNIE) 多音字预测模型，简单来说就是假设某语言有 100 个多音字，每个多音字最多有 3 个发音，那么可以在 BERT 后面接 100 个 3 分类器（简单的 fc 层即可），在预测时，找到对应的分类器进行分类即可。
参考论文：
tencent_polyphone.pdf

数据可以用 https://github.com/kakaobrain/g2pM 提供的数据

进阶：多任务的 BERT
![image](https://user-images.githubusercontent.com/24568452

Describe the bug
Setting "text-gen-type": "interactive" results in an IndexError: : shape mismatch: indexing tensors could not be broadcast together with shapes [4], [3]. Other generation types work.

To Reproduce
Steps to reproduce the behavior:

1. Install, adapt 20B to local environment, add "text-gen-type": "interactive" config
2. Run inference
3. Enter arbitrary prompt when

Issue to track tutorial requests:

• Deep Learning with PyTorch: A 60 Minute Blitz - #69
• Sentence Classification - #79

I've been chatting with some others interested in training CLIP for different domain tasks. They expressed interest in a simple way to use a pre-trained text transformer.

Some basic support for Hugging Face or generic classes of transformers shouldn't be too crazy of an extension to what is already fleshed out.