cross-encoder / nli-MiniLM2-L6-H768

huggingface.co
Total runs: 8.9K
24-hour runs: -337
7-day runs: 58
30-day runs: 2.9K
Model's Last Updated: Tháng 12 12 2024
zero-shot-classification

Introduction of nli-MiniLM2-L6-H768

Model Details of nli-MiniLM2-L6-H768

Cross-Encoder for Natural Language Inference

This model was trained using SentenceTransformers Cross-Encoder class.

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

For 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-MiniLM2-L6-H768')
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-MiniLM2-L6-H768')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-MiniLM2-L6-H768')

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-MiniLM2-L6-H768')

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-MiniLM2-L6-H768 on huggingface.co

8.9K
Total runs
-337
24-hour runs
-709
3-day runs
58
7-day runs
2.9K
30-day runs

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