cross-encoder_nli-deberta-v3-small

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language: en pipeline_tag: zero-shot-classification tags:

• transformers datasets:
• nyu-mll/multi_nli
• stanfordnlp/snli metrics:
• accuracy license: apache-2.0 base_model:
• microsoft/deberta-v3-small library_name: sentence-transformers

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Cross-Encoder for Natural Language Inference

This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-small

Training Data

The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.

Performance

• Accuracy on SNLI-test dataset: 91.65
• Accuracy on MNLI mismatched set: 87.55

For futher evaluation results, see SBERT.net - Pretrained Cross-Encoder.

Usage

Pre-trained models can be used like this:

from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/nli-deberta-v3-small')
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.')])

#Convert scores to labels
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]

Usage with Transformers AutoModel

You can use the model also directly with Transformers library (without SentenceTransformers library):

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-deberta-v3-small')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-small')

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")

model.eval()
with torch.no_grad():
    scores = model(**features).logits
    label_mapping = ['contradiction', 'entailment', 'neutral']
    labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
    print(labels)

Zero-Shot Classification

This model can also be used for zero-shot-classification:

from transformers import pipeline

classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-v3-small')

sent = "Apple just announced the newest iPhone X"
candidate_labels = ["technology", "sports", "politics"]
res = classifier(sent, candidate_labels)
print(res)