trained and should be used as follows:
```python
input_ids = tokenizer.encode("This is a sentence from [MASK] training data", return_tensors="pt")
mlm_labels = tokenizer.encode("This is a sentence from the training data", return_tensors="pt")
loss = model(input_ids, labels=input_ids, masked_lm_labels=mlm_labels)[0]
```
LongformerConfig
[[autodoc]] LongformerConfig
LongformerTokenizer
[[autodoc]] LongformerTokenizer
LongformerTokenizerFast
[[autodoc]] LongformerTokenizerFast
Longformer specific outputs
[[autodoc]] models.longformer.modeling_longformer.LongformerBaseModelOutput
[[autodoc]] models.longformer.modeling_longformer.LongformerBaseModelOutputWithPooling
[[autodoc]] models.longformer.modeling_longformer.LongformerMaskedLMOutput
[[autodoc]] models.longformer.modeling_longformer.LongformerQuestionAnsweringModelOutput
