---
title: Instruction-Tagger
canonical_url: "https://www.modelscope.cn/models/pai/Instruction-Tagger"
md_url: "https://www.modelscope.cn/models/pai/Instruction-Tagger.md"
repository: pai/Instruction-Tagger
last_updated: 2025-04-08
model_type:
  - deberta-v2
architectures:
  - DebertaV2ForSequenceClassification
parameters: 184.4M
tensor_type:
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 516
stars: 0
---

# Instruction-Tagger

> Instruction-Tagger - pai 在 ModelScope 开源的模型。Instruction-Tagger is a powerful model for labeling instructions with task tags. It allows users to easily adjust the proportion of tasks in a dataset.

- **Repository**: pai/Instruction-Tagger
- **Parameters**: 184.4M
- **Downloads**: 516
- **Stars**: 0
- **Last updated**: 2025-04-08

Source: https://www.modelscope.cn/models/pai/Instruction-Tagger

---

## 📖 Introduction

**Instruction-Tagger**  is a powerful model for labeling instructions with task tags. It allows users to easily adjust the proportion of tasks in a dataset.

#### Example Input

>What are the main differences between Type 1 and Type 2 diabetes, and how do their treatment approaches differ?"

#### Example Output
>Medicine


## 🚀 Quick Start

Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.

```python
import torch
from transformers import DebertaV2Tokenizer,DebertaV2ForSequenceClassification, Trainer, TrainingArguments

model = DebertaV2ForSequenceClassification.from_pretrained('deberta_cls', num_labels=33).cuda()
tokenizer = DebertaV2Tokenizer.from_pretrained('alibaba-pai/Instruction-Tagger')

labels={14: 'Writting',
 0: 'Common-Sense',
 28: 'Ecology',
 22: 'Medicine',
 17: 'Grammar',
 3: 'Code Generation',
 31: 'Others',
 20: 'Paraphrase',
 19: 'Economy',
 6: 'Code Debug',
 21: 'Reasoning',
 18: 'Computer Science',
 4: 'Technology',
 13: 'Math',
 32: 'Literature',
 26: 'Chemistry',
 15: 'Complex Format',
 25: 'Ethics',
 27: 'Multilingual',
 29: 'Roleplay',
 30: 'Entertainment',
 23: 'Biology',
 16: 'Art',
 10: 'Academic Writing',
 24: 'Health',
 11: 'Philosophy',
 5: 'Sport',
 1: 'History',
 12: 'Music',
 7: 'Toxicity',
 2: 'Law',
 9: 'Physics',
 8: 'Counterfactual'}

def task_cls(pp):
    inputs = tokenizer(pp, return_tensors="pt",padding=True).to("cuda")

    with torch.no_grad():
        logits = model(**inputs).logits

    predicted_class_id = logits.argmax().item()

    return labels[predicted_class_id]

instruct="""
What are the main differences between Type 1 and Type 2 diabetes, and how do their treatment approaches differ?"
"""

tag=task_cls(instruct)
```

## 🔍 Evaluation

To assess the accuracy of task classification, we manually evaluate a sample set of 100 entries (not in the training set), resulting in a classification precision of 92%.

## 📜 Citation

If you find our work helpful, please cite it!

```
@misc{TAPIR,
      title={Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning}, 
      author={Yuanhao Yue and Chengyu Wang and Jun Huang and Peng Wang},
      year={2024},
      eprint={2405.13448},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2405.13448}, 
}
```