
# Clone from [https://huggingface.co/facebook/bart-large-mnli](bart-large-mnli)

This is the checkpoint for [bart-large](https://huggingface.co/facebook/bart-large) after being trained on the [MultiNLI (MNLI)](https://huggingface.co/datasets/multi_nli) dataset.

Additional information about this model:
- The [bart-large](https://huggingface.co/facebook/bart-large) model page
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
](https://arxiv.org/abs/1910.13461)
- [BART fairseq implementation](https://github.com/pytorch/fairseq/tree/master/fairseq/models/bart)

## NLI-based Zero Shot Text Classification

[Yin et al.](https://arxiv.org/abs/1909.00161) proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class "politics", we could construct a hypothesis of `This text is about politics.`. The probabilities for entailment and contradiction are then converted to label probabilities.

This method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See [this blog post](https://joeddav.github.io/blog/2020/05/29/ZSL.html) for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code.

#### With the zero-shot classification pipeline

The model can be loaded with the `zero-shot-classification` pipeline like so:

```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification",
                      model="facebook/bart-large-mnli")
```

You can then use this pipeline to classify sequences into any of the class names you specify.

```python
sequence_to_classify = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(sequence_to_classify, candidate_labels)
#{'labels': ['travel', 'dancing', 'cooking'],
# 'scores': [0.9938651323318481, 0.0032737774308770895, 0.002861034357920289],
# 'sequence': 'one day I will see the world'}
```

If more than one candidate label can be correct, pass `multi_class=True` to calculate each class independently:

```python
candidate_labels = ['travel', 'cooking', 'dancing', 'exploration']
classifier(sequence_to_classify, candidate_labels, multi_class=True)
#{'labels': ['travel', 'exploration', 'dancing', 'cooking'],
# 'scores': [0.9945111274719238,
#  0.9383890628814697,
#  0.0057061901316046715,
#  0.0018193122232332826],
# 'sequence': 'one day I will see the world'}
```


#### With manual PyTorch

```python
# pose sequence as a NLI premise and label as a hypothesis
from transformers import AutoModelForSequenceClassification, AutoTokenizer
nli_model = AutoModelForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
tokenizer = AutoTokenizer.from_pretrained('facebook/bart-large-mnli')

premise = sequence
hypothesis = f'This example is {label}.'

# run through model pre-trained on MNLI
x = tokenizer.encode(premise, hypothesis, return_tensors='pt',
                     truncation_strategy='only_first')
logits = nli_model(x.to(device))[0]

# we throw away "neutral" (dim 1) and take the probability of
# "entailment" (2) as the probability of the label being true 
entail_contradiction_logits = logits[:,[0,2]]
probs = entail_contradiction_logits.softmax(dim=1)
prob_label_is_true = probs[:,1]
```

{"license": "mit", "datasets": ["multi_nli"], "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png", "pipeline_tag": "zero-shot-classification"}
transformers pytorch jax rust bart text-classification zero-shot-classification dataset:multi_nli arxiv:1910.13461 arxiv:1909.00161 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #rust #bart #text-classification #zero-shot-classification #dataset-multi_nli #arxiv-1910.13461 #arxiv-1909.00161 #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# Clone from URL

This is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset.

Additional information about this model:
- The bart-large model page
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

- BART fairseq implementation

## NLI-based Zero Shot Text Classification

Yin et al. proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class "politics", we could construct a hypothesis of 'This text is about politics.'. The probabilities for entailment and contradiction are then converted to label probabilities.

This method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See this blog post for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code.

#### With the zero-shot classification pipeline

The model can be loaded with the 'zero-shot-classification' pipeline like so:



You can then use this pipeline to classify sequences into any of the class names you specify.



If more than one candidate label can be correct, pass 'multi_class=True' to calculate each class independently:




#### With manual PyTorch



# Clone from URL

This is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset.

Additional information about this model:
- The bart-large model page
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

- BART fairseq implementation ## NLI-based Zero Shot Text Classification

Yin et al. proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class "politics", we could construct a hypothesis of 'This text is about politics.'. The probabilities for entailment and contradiction are then converted to label probabilities.

This method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See this blog post for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code. #### With the zero-shot classification pipeline

The model can be loaded with the 'zero-shot-classification' pipeline like so:



You can then use this pipeline to classify sequences into any of the class names you specify.



If more than one candidate label can be correct, pass 'multi_class=True' to calculate each class independently: #### With manual PyTorch
TAGS
#transformers #pytorch #jax #rust #bart #text-classification #zero-shot-classification #dataset-multi_nli #arxiv-1910.13461 #arxiv-1909.00161 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # Clone from URL

This is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset.

Additional information about this model:
- The bart-large model page
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

- BART fairseq implementation ## NLI-based Zero Shot Text Classification

Yin et al. proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class "politics", we could construct a hypothesis of 'This text is about politics.'. The probabilities for entailment and contradiction are then converted to label probabilities.

This method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See this blog post for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code. #### With the zero-shot classification pipeline

The model can be loaded with the 'zero-shot-classification' pipeline like so:



You can then use this pipeline to classify sequences into any of the class names you specify.



If more than one candidate label can be correct, pass 'multi_class=True' to calculate each class independently: #### With manual PyTorch
passage: TAGS
#transformers #pytorch #jax #rust #bart #text-classification #zero-shot-classification #dataset-multi_nli #arxiv-1910.13461 #arxiv-1909.00161 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# Clone from URL

This is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset.

Additional information about this model:
- The bart-large model page
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

- BART fairseq implementation## NLI-based Zero Shot Text Classification

Yin et al. proposed a method for using pre-trained NLI models as a ready-made zero-shot sequence classifiers. The method works by posing the sequence to be classified as the NLI premise and to construct a hypothesis from each candidate label. For example, if we want to evaluate whether a sequence belongs to the class "politics", we could construct a hypothesis of 'This text is about politics.'. The probabilities for entailment and contradiction are then converted to label probabilities.

This method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See this blog post for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code.#### With the zero-shot classification pipeline

The model can be loaded with the 'zero-shot-classification' pipeline like so:



You can then use this pipeline to classify sequences into any of the class names you specify.



If more than one candidate label can be correct, pass 'multi_class=True' to calculate each class independently:#### With manual PyTorch