roberta-logic-sentiment-zh

RoBERTa-Logic-Finetuned on Chinese Logic Dataset

->中文

This model is a RoBERTa model fine-tuned on a Chinese logic dataset to perform logic-related sentiment classification tasks. The dataset was generated using the Doubao API. It achieves an F1 score of 96.00% on the validation set.

Model Details

• Architecture: RoBERTa
• Base Model: uer/roberta-base-finetuned-dianping-chinese
• Framework: Transformers, PyTorch
• Fine-tuning Dataset: YiMeng-SYSU/chinese-logic-sentiment-dataset (Generated by Doubao API)
• Hardware: Trained on NVIDIA RTX 5070 Ti + AMD 9800X3D

Performance

The performance of this model on the Golden Test Set (960 samples) is as follows:

Detailed Performance by Logic Type

Usage

You can use the model for sentiment analysis tasks in Chinese by loading it through the Hugging Face Transformers library.

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
model_name = "YiMeng-SYSU/roberta-logic-sentiment-zh"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
text = "这部剧简直有毒，害得我昨晚又熬到凌晨三点，黑眼圈都出来了!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)  # 补充tokenizer参数，更健壮
with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
# 结合config中的id2label映射，输出可读标签（而非数字）
id2label = {0: "Negative (负面)", 1: "Positive (正面)"}
pred_label_id = torch.argmax(probs).item()
print(f"Label: {id2label[pred_label_id]}")
print(f"Confidence: {probs.max().item():.4f}")