No max_length set on huawei-noah/TinyBERT_General_4L_312D/config.json
## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->

- `transformers` version: 3.0.2
- Platform: Darwin-20.3.0-x86_64-i386-64bit
- Python version: 3.6.10
- PyTorch version (GPU?): 1.7.1 (False)
- Tensorflow version (GPU?): 2.4.1 (False)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No


### Who can help
@patrickvonplaten @JetRunner 


## Information

Model I am using: huawei-noah/TinyBERT_General_4L_312D

The problem arises when using:
* [x] my own modified scripts: (give details below)
```{python}
import json

import pandas as pd
import gzip

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('huawei-noah/TinyBERT_General_4L_312D')


def parse(path):
    g = gzip.open(path, 'rb')
    for l in g:
        yield json.loads(l)

def getDF(path):
    i = 0
    df = {}
    for d in parse(path):
        df[i] = d
        i += 1
    return pd.DataFrame.from_dict(df, orient='index')

local_path_to_review_data = "/Users/alexandrecombessie/Downloads/Software_5.json.gz"  # See download link below
df = getDF(local_path_to_review_data)

df["review_text_full_embeddings"] = [
    json.dumps(x.tolist()) for x in model.encode(df["reviewText"].astype(str))
]
```


The tasks I am working on is:
* [x] my own task or dataset: (give details below)
- Amazon review dataset sample (http://deepyeti.ucsd.edu/jianmo/amazon/categoryFilesSmall/Software_5.json.gz)

## To reproduce

Steps to reproduce the behavior:

See script above

## Expected behavior

A `max_length` should be set in the model `config.json` for the tokenizer to apply truncation (which is my expected behavior).
See https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D/blob/main/config.json

I could do it myself, but I am not able to understand what is the right length to set.



Hi @patrickvonplaten @JetRunner,

Apologies for following up, I know it's a busy time.

Would you have some time to look into this issue?

Thanks,

Alex <|||||>Hi Alex, I think the right thing to do is to look up `max_len` from the TinyBERT paper. Do you know what is that setting? <|||||>> Hi Alex, I think the right thing to do is to look up `max_len` from the TinyBERT paper. Do you know what is that setting?

Yeah, you are right. The paper seems to indicate 128 for the general distillation.

![Screenshot 2021-06-11 at 11 12 49](https://user-images.githubusercontent.com/4739848/121662849-1758a780-caa6-11eb-8d4b-9b7bb91e0c6a.png)

I will reach out to the authors because they mention another length of 64 for task-specific distillation. I just want to be sure which one is used by the model hosted on Huggingface.

As a side-note, it would be really useful (at least to me) to have some automated checks and/or feedback system on the model hub.
<|||||>Hi @JetRunner,

I got the following answer from the author (Xiaoqi Jiao)

> The max_len of TinyBERT is 128, but if the max sequence length of your downstream task is less than max_len, you may set max_len to a small value like 64 to save the computing resources.

Should I add `max_length: 128` on the model hub? Happy to take this small PR directly.

Cheers,

Alex
<|||||>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/master/CONTRIBUTING.md) are likely to be ignored.