akiFQC/bert-base-japanese-v3_nli-jsnli-jnli-jsick

Cross-Encoder for Natural Language Inference(NLI) for Japanese

This model was trained using SentenceTransformers Cross-Encoder class. This model is based on tohoku-nlp/bert-base-japanese-v3.

Training Data

The model was trained on following datasets.

• JSNLI
• JNLI (only train set)
• JSICK (only train set)

For a given sentence pair, it will output three scores corresponding to the labels: {0:"entailment", 1:"neutral", 2:"contradiction}.

Usage

Pre-trained models can be used like this:

from sentence_transformers import CrossEncoder
model = CrossEncoder('akiFQC/bert-base-japanese-v3_nli-jsnli')
scores = model.predict([('男はピザを食べています', '男は何かを食べています'), ('黒いレーシングカーが観衆の前から発車します。', '男は誰もいない道を運転しています。')])

#Convert scores to labels
label_mapping = ['entailment', 'neutral', 'contradiction',]
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(['男はピザを食べています', '黒いレーシングカーが観衆の前から発車します。'], ['男は何かを食べています', '男は誰もいない道を運転しています。'],  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='akiFQC/bert-base-japanese-v3_nli-jsnli')

sent = "Appleは先程、iPhoneの最新機種について発表しました。"
candidate_labels = ["技術", "スポーツ", "政治"]
res = classifier(sent, candidate_labels)
print(res)

Benchmarks

JGLUE-JNLI validation set accuracy: 0.914