# BART
[BART](https://huggingface.co/papers/1910.13461) is a sequence-to-sequence model that combines the pretraining objectives from BERT and GPT. It’s pretrained by corrupting text in different ways like deleting words, shuffling sentences, or masking tokens and learning how to fix it. The encoder encodes the corrupted document and the corrupted text is fixed by the decoder. As it learns to recover the original text, BART gets really good at both understanding and generating language.
You can find all the original BART checkpoints under the [AI at Meta](https://huggingface.co/facebook?search_models=bart) organization.
The example below demonstrates how to predict the `[MASK]` token with [`Pipeline`], [`AutoModel`], and from the command line.
```py
import torch
from transformers import pipeline
pipeline = pipeline(
task="fill-mask",
model="facebook/bart-large",
torch_dtype=torch.float16,
device=0
)
pipeline("Plants create through a process known as photosynthesis.")
```
```py
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"facebook/bart-large",
)
model = AutoModelForMaskedLM.from_pretrained(
"facebook/bart-large",
torch_dtype=torch.float16,
device_map="auto",
attn_implementation="sdpa"
)
inputs = tokenizer("Plants create through a process known as photosynthesis.", return_tensors="pt").to("cuda")
with torch.no_grad():
outputs = model(**inputs)
predictions = outputs.logits
masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
predicted_token = tokenizer.decode(predicted_token_id)
print(f"The predicted token is: {predicted_token}")
```
```bash
echo -e "Plants create through a process known as photosynthesis." | transformers-cli run --task fill-mask --model facebook/bart-large --device 0
```
## Notes
- Inputs should be padded on the right because BERT uses absolute position embeddings.
- The [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) checkpoint doesn't include `mask_token_id` which means it can't perform mask-filling tasks.
- BART doesn’t use `token_type_ids` for sequence classification. Use [`BartTokenizer`] or [`~PreTrainedTokenizerBase.encode`] to get the proper splitting.
- The forward pass of [`BartModel`] creates the `decoder_input_ids` if they're not passed. This can be different from other model APIs, but it is a useful feature for mask-filling tasks.
- Model predictions are intended to be identical to the original implementation when `forced_bos_token_id=0`. This only works if the text passed to `fairseq.encode` begins with a space.
- [`~GenerationMixin.generate`] should be used for conditional generation tasks like summarization.
## BartConfig
[[autodoc]] BartConfig
- all
## BartTokenizer
[[autodoc]] BartTokenizer
- all
## BartTokenizerFast
[[autodoc]] BartTokenizerFast
- all
## BartModel
[[autodoc]] BartModel
- forward
## BartForConditionalGeneration
[[autodoc]] BartForConditionalGeneration
- forward
## BartForSequenceClassification
[[autodoc]] BartForSequenceClassification
- forward
## BartForQuestionAnswering
[[autodoc]] BartForQuestionAnswering
- forward
## BartForCausalLM
[[autodoc]] BartForCausalLM
- forward
## TFBartModel
[[autodoc]] TFBartModel
- call
## TFBartForConditionalGeneration
[[autodoc]] TFBartForConditionalGeneration
- call
## TFBartForSequenceClassification
[[autodoc]] TFBartForSequenceClassification
- call
## FlaxBartModel
[[autodoc]] FlaxBartModel
- __call__
- encode
- decode
## FlaxBartForConditionalGeneration
[[autodoc]] FlaxBartForConditionalGeneration
- __call__
- encode
- decode
## FlaxBartForSequenceClassification
[[autodoc]] FlaxBartForSequenceClassification
- __call__
- encode
- decode
## FlaxBartForQuestionAnswering
[[autodoc]] FlaxBartForQuestionAnswering
- __call__
- encode
- decode
## FlaxBartForCausalLM
[[autodoc]] FlaxBartForCausalLM
- __call__
