Bert Base Japanese Jsnli

🚀 bert-base-japanese-jsnli

This model is a fine - tuned version of [cl - tohoku/bert - base - japanese - v2](https://huggingface.co/cl - tohoku/bert - base - japanese - v2) on the [JSNLI](https://nlp.ist.i.kyoto - u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) dataset. It can be used for zero - shot classification and text - classification tasks, achieving high accuracy in natural language inference.

🚀 Quick Start

This model is a fine - tuned version of [cl - tohoku/bert - base - japanese - v2](https://huggingface.co/cl - tohoku/bert - base - japanese - v2) on the [JSNLI](https://nlp.ist.i.kyoto - u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) dataset. It achieves the following results on the evaluation set:

• Loss: 0.2085
• Accuracy: 0.9288

💻 Usage Examples

Basic Usage

from transformers import pipeline

classifier = pipeline("zero-shot-classification", model="Formzu/bert-base-japanese-jsnli")

sequence_to_classify = "いつか世界を見る。"
candidate_labels = ['旅行', '料理', '踊り']
out = classifier(sequence_to_classify, candidate_labels, hypothesis_template="この例は{}です。")
print(out)
#{'sequence': 'いつか世界を見る。', 
# 'labels': ['旅行', '料理', '踊り'], 
# 'scores': [0.6758995652198792, 0.22110949456691742, 0.1029909998178482]}

Advanced Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

model_name = "Formzu/bert-base-japanese-jsnli"
model = AutoModelForSequenceClassification.from_pretrained(model_name).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name)

premise = "いつか世界を見る。"
label = '旅行'
hypothesis = f'この例は{label}です。'

input = tokenizer.encode(premise, hypothesis, return_tensors='pt').to(device)
with torch.no_grad():
    logits = model(input)["logits"][0]
    probs = logits.softmax(dim=-1)
    print(probs.cpu().numpy(), logits.cpu().numpy())
#[0.68940836 0.29482093 0.01577068] [ 1.7791482   0.92968255 -1.998533  ]

📚 Documentation

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e - 05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Framework versions

• Transformers 4.21.2
• Pytorch 1.12.1+cu116
• Datasets 2.4.0
• Tokenizers 0.12.1

📄 License

This model is licensed under the CC - BY - SA 4.0 license.

📋 Model Information