Cannot load studio-ousia/luke-base for AutoModelForTokenClassification
## Environment info
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- `transformers` version: 4.6.0.dev0 (pulled from repo)
- Platform: 3
- Python version: 3.7.10
- PyTorch version (GPU?): 1.7.0 (no)
- Tensorflow version (GPU?): 2.4.1 (no)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help
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Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @LysandreJik
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## Information

I tried loading LUKE's weight for AutoModelForTokenClassification. I intend to train further for NER. It failed due to a configuration error.

## To reproduce

Steps to reproduce the behavior:

```
from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer

from transformers import AutoTokenizer, AutoModel
  
tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-base") #Succesful

model = AutoModel.from_pretrained("studio-ousia/luke-base") #Succesful

model = AutoModelForTokenClassification.from_pretrained("studio-ousia/luke-base", num_labels=39)
```

```
Some weights of the model checkpoint at studio-ousia/luke-base were not used when initializing LukeModel: ['embeddings.position_ids']
- This IS expected if you are initializing LukeModel 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 LukeModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-9-b4efbc1b7796> in <module>
      7 model = AutoModel.from_pretrained("studio-ousia/luke-base")
      8 
----> 9 model = AutoModelForTokenClassification.from_pretrained("studio-ousia/luke-base", num_labels=39)

/opt/conda/lib/python3.7/site-packages/transformers/models/auto/auto_factory.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
    381             return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
    382         raise ValueError(
--> 383             f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
    384             f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}."
    385         )

ValueError: Unrecognized configuration class <class 'transformers.models.luke.configuration_luke.LukeConfig'> for this kind of AutoModel: AutoModelForTokenClassification.
Model type should be one of BigBirdConfig, ConvBertConfig, LayoutLMConfig, DistilBertConfig, CamembertConfig, FlaubertConfig, XLMConfig, XLMRobertaConfig, LongformerConfig, RobertaConfig, SqueezeBertConfig, BertConfig, MegatronBertConfig, MobileBertConfig, XLNetConfig, AlbertConfig, ElectraConfig, FunnelConfig, MPNetConfig, DebertaConfig, DebertaV2Config, IBertConfig.
```

## Expected behavior

Succesful loading
cc @NielsRogge<|||||>LUKE does not have a `*ForTokenClassification` model, so it's not available in the token classification auto model. The following three models are available: `LukeForEntityClassification`, `LukeForEntityPairClassification`, `LukeForEntitySpanClassification`<|||||>Yes, I explicitly did not include the head models of LUKE in any `AutoModel`, as LUKE works a bit differently than other models.

For NER, it does not use a token classification head as other models like BERT do. Instead, LUKE considers all possible entity spans in a sentence, which are then classified accordingly. See the [code example](https://huggingface.co/transformers/master/model_doc/luke.html#transformers.LukeForEntitySpanClassification) for reference. <|||||>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.<|||||>Hi @NielsRogge ,

I am trying to finetune Luke on a custom dataset for the NER task. As mentioned here, I infer we need to do it through LukeForEntitySpanClassification. Can you please guide me on how I could achieve the same? I am unaware of any existing tutorial for the same.

Thank You<|||||>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.<|||||>Hi, @Sreyan88 Were you able to train LUKE on a custom dataset? I am also working on the same and do not have any progress on this yet. Any help is appreciated. Thanks! 