xlm-mlm-tlm-xnli15-1024 (Masked language modeling + Translation, XNLI languages)
xlm-clm-enfr-1024 (Causal language modeling, English-French)
xlm-clm-ende-1024 (Causal language modeling, English-German)
These checkpoints require language embeddings that will specify the language used at inference time. These language
embeddings are represented as a tensor that is of the same shape as the input ids passed to the model. The values in
these tensors depend on the language used and are identifiable using the lang2id and id2lang attributes from
the tokenizer.
Here is an example using the xlm-clm-enfr-1024 checkpoint (Causal language modeling, English-French):
```py
import torch
from transformers import XLMTokenizer, XLMWithLMHeadModel
tokenizer = XLMTokenizer.from_pretrained("xlm-clm-enfr-1024")
