Get Start with CamembertForSequenceClassification
## 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: 4.3.3
- Platform: Windows
- Python version: 3.9
- PyTorch version (GPU?): 1.9.0
- Tensorflow version (GPU?):
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help
<!-- Your issue will be replied to more quickly if you can figure out the right person to tag with @
 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
- fsmt: @stas00
- funnel: @sgugger
- gpt2: @patrickvonplaten, @LysandreJik
- rag: @patrickvonplaten, @lhoestq
- tensorflow: @Rocketknight1

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

Model hub:

- for issues with a model report at https://discuss.huggingface.co/ and tag the model's creator.

HF projects:

- datasets: [different repo](https://github.com/huggingface/datasets)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Examples:

- maintained examples (not research project or legacy): @sgugger, @patil-suraj
- research_projects/bert-loses-patience: @JetRunner
- research_projects/distillation: @VictorSanh

 -->

## Information

Model I am using (Bert, XLNet ...): CamemBert

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] 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)
https://huggingface.co/transformers/task_summary.html

I follow the above Summary of the tasks, in the Sequence Classification section.

I'm trying to find the paraphrase probability of two French Sentence sentences by using CamemBertForSqeuenceClassification, 
But I got the the following warning and output. How could I edit my code?

```
Some weights of the model checkpoint at ./nlp_models/camembert-base were not used when initializing CamembertForSequenceClassification: ['lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'roberta.pooler.dense.weight', 'roberta.pooler.dense.bias']
- This IS expected if you are initializing CamembertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing CamembertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of CamembertForSequenceClassification were not initialized from the model checkpoint at ./nlp_models/camembert-base and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
not paraphrase: 47%
is paraphrase: 53%
not paraphrase: 47%
is paraphrase: 53%
```



## To reproduce

Steps to reproduce the behavior:

```Python
import torch

from transformers import CamembertTokenizer
from transformers.models.camembert.modeling_camembert import CamembertForSequenceClassification


tokenizer = CamembertTokenizer.from_pretrained("./nlp_models/camembert-base")
model = CamembertForSequenceClassification.from_pretrained("./nlp_models/camembert-base")

classes = ["not paraphrase", "is paraphrase"]

sequence_0 = 'La société HuggingFace est basée à New York City'
sequence_1 = 'Les pommes sont particulièrement mauvaises pour la santé'
sequence_2 = "Le siège social de HuggingFace est situé à Manhattan"

# 
paraphrase = tokenizer(sequence_0, sequence_2, return_tensors="pt")
not_paraphrase = tokenizer(sequence_0, sequence_1, return_tensors="pt")

paraphrase_classification_logits = model(**paraphrase).logits
not_paraphrase_classification_logits = model(**not_paraphrase).logits

paraphrase_results = torch.softmax(paraphrase_classification_logits, dim=1).tolist()[0]
not_paraphrase_results = torch.softmax(not_paraphrase_classification_logits, dim=1).tolist()[0]

# Should be paraphrase
for i in range(len(classes)):
    print(f"{classes[i]}: {int(round(paraphrase_results[i] * 100))}%")

# Should not be paraphrase
for i in range(len(classes)):
    print(f"{classes[i]}: {int(round(not_paraphrase_results[i] * 100))}%")
```

<!-- If you have code snippets, error messages, stack traces please provide them here as well.
     Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
     Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.-->

## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->

Hi,

you are initializing a `CamembertForSequenceClassification` model with weights from `camembert-base`. This means that you are only initializing the base of the model, not the classification head. Hence, the head will have randomly initialized weights. This is also given as a warning:

```
Some weights of the model checkpoint at ./nlp_models/camembert-base were not used when initializing CamembertForSequenceClassification: ['lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight', 'roberta.pooler.dense.weight', 'roberta.pooler.dense.bias']
- This IS expected if you are initializing CamembertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing CamembertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of CamembertForSequenceClassification were not initialized from the model checkpoint at ./nlp_models/camembert-base and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```

This tells you exactly that: one should first fine-tune `CamembertForSequenceClassification` on a downstream task (in this case, a labeled dataset of sentence pairs that are labeled with either paraphrase/not paraphrase). You can check the [hub](https://huggingface.co/models?search=camembert) to see whether someone has already fine-tuned CamemBERT for paraphrasing (however apparently this doesn't seem to be the case).
<|||||>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.