cross-encoder / nli-deberta-v3-base

huggingface.co
Total runs: 154.6K
24-hour runs: 1.8K
7-day runs: 17.9K
30-day runs: -62.7K
Model's Last Updated: Décembre 12 2024
zero-shot-classification

Introduction of nli-deberta-v3-base

Model Details of nli-deberta-v3-base

Cross-Encoder for Natural Language Inference

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

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: 92.38
  • Accuracy on MNLI mismatched set: 90.04

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-base')
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-base')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-base')

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-base')

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

Runs of cross-encoder nli-deberta-v3-base on huggingface.co

154.6K
Total runs
1.8K
24-hour runs
5.4K
3-day runs
17.9K
7-day runs
-62.7K
30-day runs

More Information About nli-deberta-v3-base huggingface.co Model

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nli-deberta-v3-base install

nli-deberta-v3-base is an open source model from GitHub that offers a free installation service, and any user can find nli-deberta-v3-base on GitHub to install. At the same time, huggingface.co provides the effect of nli-deberta-v3-base install, users can directly use nli-deberta-v3-base installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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