OliveSensorAPI/evaluate/General_evaluation_EN.md
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基于母亲角色的多轮对话模型微调完毕。已上传到 Huggingface。

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---------

Co-authored-by: jujimeizuo <fengzetao.zed@foxmail.com>
Co-authored-by: xzw <62385492+aJupyter@users.noreply.github.com>
Co-authored-by: Zeyu Ba <72795264+ZeyuBa@users.noreply.github.com>
Co-authored-by: Bryce Wang <90940753+brycewang2018@users.noreply.github.com>
Co-authored-by: zealot52099 <songyan5209@163.com>
Co-authored-by: HongCheng <kwchenghong@gmail.com>
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# EmoLLM's general evaluation
## Introduction
This document provides instructions on how to use the 'eval.py' and 'metric.py' scripts. These scripts are used to evaluate the generation results of EmoLLM- a large model of mental health.
## Installation
- Python 3.x
- PyTorch
- Transformers
- Datasets
- NLTK
- Rouge
- Jieba
It can be installed using the following command:
```bash
pip install torch transformers datasets nltk rouge jieba
```
## Usage
### convert.py
Convert raw multi-round conversation data into single round data for evaluation.
### eval.py
The `eval.py` script is used to generate the doctor's response and evaluate it, mainly divided into the following parts:
1. Load the model and word divider.
2. Set test parameters, such as the number of test data and batch size.
3. Obtain data.
4. Generate responses and evaluate.
### metric.py
The `metric.py` script contains functions to calculate evaluation metrics, which can be set to evaluate by character level or word level, currently including BLEU and ROUGE scores.
## Results
Test the data in data.json with the following results:
| Model | ROUGE-1 | ROUGE-2 | ROUGE-L | BLEU-1 | BLEU-2 | BLEU-3 | BLEU-4 |
|----------|---------|---------|---------|---------|---------|---------|---------|
| Qwen1_5-0_5B-chat | 27.23% | 8.55% | 17.05% | 26.65% | 13.11% | 7.19% | 4.05% |
| InternLM2_7B_chat_qlora | 37.86% | 15.23% | 24.34% | 39.71% | 22.66% | 14.26% | 9.21% |
| InternLM2_7B_chat_full | 32.45% | 10.82% | 20.17% | 30.48% | 15.67% | 8.84% | 5.02% |
| InternLM2_7B_base_qlora_5epoch | 41.94% | 20.21% | 29.67% | 42.98% | 27.07% | 19.33% | 14.62% |
| InternLM2_7B_base_qlora_10epoch | 43.47% | 22.06% | 31.4% | 44.81% | 29.15% | 21.44% | 16.72% |