[question] Are the tensorflow bert weights same as the original repo ?
## Environment info
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     Don't forget to fill out the missing fields in that output! -->

- `transformers` version:
- `transformers` version: 4.2.2
- Platform: Linux-4.15.0-133-generic-x86_64-with-debian-stretch-sid
- Python version: 3.6.8
- PyTorch version (GPU?): not installed (NA)
- Tensorflow version (GPU?): 2.5.0-dev20210204 (True)
- Using GPU in script?: yes
- Using distributed or parallel set-up in script?: distributed


### Who can help

Models:

- albert, bert, xlm: @LysandreJik


## Information
I'm using the pretrained bert-base-chinese [here](https://huggingface.co/bert-base-chinese). I print out the pooler_output and the result is different from the tensorflow2.0 published hub saved model [here](https://tfhub.dev/tensorflow/bert_zh_L-12_H-768_A-12/3).
I want to confirm whether the two checkpoints weights are the same.

## To reproduce

Steps to reproduce the behavior:
```
from transformers import TFBert
bert = bert = TFBertModel.from_pretrained('bert-base-chinese', output_hidden_states=True)
output = bert(input_ids=tf.convert_to_tensor([[ 101,  791, 1921, 1921, 3698, 2582,  720, 3416,  102]]), 
                   attention_mask=tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1]]), training=False)
print(output.pooler_output)
```
```
# print the beginning weights:
array([[ 0.99749047,  0.9999622 ,  0.99657625,  0.96953416,  0.8489984 ,
         0.06474952, 
```

The tensorflow hub output can be produced from https://tfhub.dev/tensorflow/bert_zh_L-12_H-768_A-12/3.
The input text is "今天天气怎么样"
```
[[ 9.97488916e-01  9.99962687e-01  9.96489942e-01  9.69992220e-01
   8.49602520e-01  6.62192404e-02 ,
```

The tensorflow part claims they use the original bert checkpoints from tf1.X. 
There is no training=True/False option in tensorflow hub so I'm confused if the difference is due to this option? (I've set trainable=False in tensorflow hub)

## Expected behavior
Expect the outputs to be the same.

Hi! Could you provide the code you used to get the predictions with the BERT checkpoint on TF Hub? The two should be identical.<|||||>> Hi! Could you provide the code you used to get the predictions with the BERT checkpoint on TF Hub? The two should be identical.

I copied from https://tfhub.dev/tensorflow/bert_zh_L-12_H-768_A-12/3
```python
text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)
preprocessor = hub.KerasLayer(
    "https://tfhub.dev/tensorflow/bert_zh_preprocess/3")
encoder_inputs = preprocessor(text_input)
encoder = hub.KerasLayer(
    "https://tfhub.dev/tensorflow/bert_zh_L-12_H-768_A-12/3",
    trainable=False)
outputs = encoder(encoder_inputs)
pooled_output = outputs["pooled_output"]      # [batch_size, 768].
sequence_output = outputs["sequence_output"]  # [batch_size, seq_length, 768].
embedding_model = tf.keras.Model(text_input, pooled_output)
sentences = tf.constant(["今天天气怎么样"])
print(embedding_model(sentences))
```
<|||||>Any update ? @LysandreJik <|||||>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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