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YiMeng-SYSU/
roberta-logic-sentiment-zh

Name: roberta-logic-sentiment-zh
Availability: InStock

YiMeng-SYSU/chinese-logic-sentiment-dataset

uer/roberta-base-finetuned-dianping-chinese

finetune

apache-2.0

text-embeddings-inference

endpoints_compatible

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RoBERTa-Logic-Finetuned on Chinese Logic Dataset

GitHub MengzhongRe

GitHub Source Code

->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:

Metric	Score
Accuracy	96.15%
Macro-F1	96.00%
Recall (Negative)	97.53%
Recall (Positive)	94.15%

Detailed Performance by Logic Type

Type	Accuracy	Note
Simple (简单句)	97.50%	基础稳固
Transition (转折)	97.03%	完美理解转折逻辑
Double Neg (双重否定)	96.67%	彻底解决"双重否定表肯定/否定"的歧义
Irony (反讽)	92.47%	核心亮点：具备识别"反话正说"的能力

Usage

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

python

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}")