No way around "Truncation was not explicitely activated..." error when using SingleSentenceClassificationProcessor.

[codygunton]

Environment info

• transformers version: 3.1.0
• Platform: Linux-4.19.112+-x86_64-with-Ubuntu-18.04-bionic
• Python version: 3.6.9
• PyTorch version (GPU?): 1.6.0+cu101 (True)
• Tensorflow version (GPU?): 2.3.0 (True)
• Using GPU in script?: Yes
• Using distributed or parallel set-up in script?:

Who can help

@LysandreJik, @thomwolf

Information

Model I am using: BERT.

The problem arises when using:

• [x] the official example scripts: (give details below)
• [ ] my own modified scripts: (give details below)

The tasks I am working on is:

• [ ] an official GLUE/SQUaD task: (give the name)
• [x] my own task or dataset: (give details below)

To reproduce

from transformers import *
processor = SingleSentenceClassificationProcessor()
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
processor.add_examples(["Thanks for cool stuff!"])
processor.get_features(tokenizer, max_length=3)

Truncation was not explicitely activated but `max_length` is provided a specific value, please use `truncation=True` to explicitely truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.

[InputFeatures(input_ids=[101, 4283, 102], attention_mask=[1, 1, 1], token_type_ids=None, label=0)]

Expected behavior

This is expected, but the problem is that there is no way to suppress that warning, because there is no way to pass truncation=True when tokenizer.encode is called within processor.get_features. Probably one should make truncation an argument to processor.get_features.

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