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--- |
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language: en |
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pipeline_tag: zero-shot-classification |
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tags: |
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- deberta-base-base |
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datasets: |
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- multi_nli |
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- snli |
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metrics: |
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- accuracy |
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license: apache-2.0 |
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--- |
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# Cross-Encoder for Natural Language Inference |
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This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. |
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## Training Data |
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The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. |
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## Performance |
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For evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli). |
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## Usage |
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Pre-trained models can be used like this: |
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```python |
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from sentence_transformers import CrossEncoder |
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model = CrossEncoder('cross-encoder/nli-deberta-base') |
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scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')]) |
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#Convert scores to labels |
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label_mapping = ['contradiction', 'entailment', 'neutral'] |
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labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)] |
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``` |
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## Usage with Transformers AutoModel |
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You can use the model also directly with Transformers library (without SentenceTransformers library): |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-deberta-base') |
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tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-base') |
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features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt") |
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model.eval() |
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with torch.no_grad(): |
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scores = model(**features).logits |
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label_mapping = ['contradiction', 'entailment', 'neutral'] |
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labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)] |
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print(labels) |
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``` |
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## Zero-Shot Classification |
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This model can also be used for zero-shot-classification: |
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```python |
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from transformers import pipeline |
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classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-base') |
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sent = "Apple just announced the newest iPhone X" |
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candidate_labels = ["technology", "sports", "politics"] |
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res = classifier(sent, candidate_labels) |
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print(res) |
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``` |
