xlm-mlm-17-1280: after run model to get embeddings shape 20000
i want to get embeddings for :ru: text by `xlm-mlm-17-1280`
at the end get embeddings with shape 2k

example code (using the last version transformers & torch on ubuntu):
```
from transformers import AutoTokenizer, AutoModelWithLMHead

xlm_mlm = 'xlm-mlm-17-1280'
tokenizer_xlm_mlm = AutoTokenizer.from_pretrained(xlm_mlm)
model_xlm_mlm =  AutoModelWithLMHead.from_pretrained(xlm_mlm) 

input_ids = torch.tensor([tokenizer_xlm_mlm.encode(my_input_text)]) # batch size of 1
print(f'{input_ids.shape=}')
# input_ids.shape=torch.Size([1, 373])

lang_id_ru = tokenizer_xlm_mlm.lang2id['ru']

langs_ru = torch.tensor([lang_id_ru] * input_ids.shape[1])  # torch.tensor([0, 0, 0, ..., 0])
print(f'{langs_ru.shape=}')
# langs_ru.shape=torch.Size([373])

langs_ru = langs_ru.view(1, -1) # is now of shape [1, sequence_length]
print(f'{langs_ru.shape=}')
# langs_ru.shape=torch.Size([1, 373])

outputs = model_xlm_mlm(input_ids, langs=langs_ru)
outputs[0].shape
# torch.Size([1, 373, 200000])
```

so it's a bug or my bad?
hmm, i found `(proj): Linear(in_features=1280, out_features=200000, bias=True))`
why `out_features = 200000`?<|||||>That's the vocab size, which is of size 200 000. You can see it in the [config.json](https://s3.amazonaws.com/models.huggingface.co/bert/xlm-mlm-17-1280-config.json).<|||||>it's true, my bad
and how i can get embeddings 1280 for input text? <|||||>@LysandreJik maybe u can help, how i can get embeddings to sentence after run `outputs = model_xlm_mlm(input_ids, langs=langs_ru)`
at the older version 2.3.0 the `outputs` had the last size <emb_dim> but no it's <vocab_size><|||||>That's probably because you were using the `AutoModel` factory instead of `AutoModelWithLMHead`. The former returns the transformer embeddings of dimension `hidden_size` (1280 in your case), while the latter returns the projected embeddings on the vocabulary, of dimension `vocab_size` (200 000 in your case). 

Change the two lines:

```py
from transformers import AutoTokenizer, AutoModelWithLMHead

model_xlm_mlm =  AutoModelWithLMHead.from_pretrained(xlm_mlm)
```

to

```py
from transformers import AutoTokenizer, AutoModel

model_xlm_mlm =  AutoModel.from_pretrained(xlm_mlm)
```