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--- |
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datasets: |
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- multi_nli |
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language: en |
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license: mit |
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pipeline_tag: zero-shot-classification |
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tags: |
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- bart |
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- zero-shot-classification |
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--- |
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# Bart large model for NLI-based Zero Shot Text Classification |
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This model uses [bart-large](https://huggingface.co/facebook/bart-large). |
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## Training Data |
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This model was trained on the [MultiNLI (MNLI)](https://huggingface.co/datasets/multi_nli) dataset in the manner originally described in [Yin et al. 2019](https://arxiv.org/abs/1909.00161). |
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It can be used to predict whether a topic label can be assigned to a given sequence, whether or not the label has been seen before. |
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## Usage and Performance |
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The trained model can be used like this: |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline |
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# Load model & tokenizer |
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bart_model = AutoModelForSequenceClassification.from_pretrained('navteca/bart-large-mnli') |
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bart_tokenizer = AutoTokenizer.from_pretrained('navteca/bart-large-mnli') |
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# Get predictions |
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nlp = pipeline('zero-shot-classification', model=bart_model, tokenizer=bart_tokenizer) |
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sequence = 'One day I will see the world.' |
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candidate_labels = ['cooking', 'dancing', 'travel'] |
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result = nlp(sequence, candidate_labels, multi_label=True) |
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print(result) |
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#{ |
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# "sequence": "One day I will see the world.", |
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# "labels": [ |
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# "travel", |
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# "dancing", |
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# "cooking" |
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# ], |
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# "scores": [ |
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# 0.9941897988319397, |
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# 0.0060537424869835, |
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# 0.0020010927692056 |
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# ] |
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#} |
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``` |
