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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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- transformers
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datasets:
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- nyu-mll/multi_nli
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- stanfordnlp/snli
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metrics:
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- accuracy
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license: apache-2.0
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base_model:
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- microsoft/deberta-v3-small
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library_name: sentence-transformers
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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. This model is based on [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small)
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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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- Accuracy on SNLI-test dataset: 91.65
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- Accuracy on MNLI mismatched set: 87.55
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For futher 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-v3-small')
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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-v3-small')
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tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-small')
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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-v3-small')
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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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``` |
