We reshape it to be of size (batch_size, sequence_length)
langs = langs.view(1, -1)  # is now of shape [1, sequence_length] (we have a batch size of 1)
```
You can then feed it all as input to your model:
```py
outputs = model(input_ids, langs=langs)
```
The example run_generation.py can generate text
using the CLM checkpoints from XLM, using the language embeddings.
XLM without Language Embeddings
This section concerns the following checkpoints:
xlm-mlm-17-1280 (Masked language modeling, 17 languages)
xlm-mlm-100-1280 (Masked language modeling, 100 languages)
These checkpoints do not require language embeddings at inference time. These models are used to have generic sentence
representations, differently from previously-mentioned XLM checkpoints.
BERT
BERT has two checkpoints that can be used for multi-lingual tasks:
