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* Add files via upload * 新增ENmd文档 * Update README.md * Update README_EN.md * Update LICENSE * [docs] update lmdeploy file * add ocr.md * Update tutorial.md * Update tutorial_EN.md * Update General_evaluation_EN.md * Update General_evaluation_EN.md * Update README.md Add InternLM2_7B_chat_full's professional evaluation results * Update Professional_evaluation.md * Update Professional_evaluation.md * Update Professional_evaluation.md * Update Professional_evaluation.md * Update Professional_evaluation_EN.md * Update README.md * Update README.md * Update README_EN.md * Update README_EN.md * Update README_EN.md * [DOC] update readme * Update LICENSE * Update LICENSE * update personal info and small format optimizations * update personal info and translations for contents in a table * Update RAG README * Update demo link in README.md * Update xlab app link * Update xlab link * add xlab model * Update web_demo-aiwei.py * add bitex --------- Co-authored-by: xzw <62385492+aJupyter@users.noreply.github.com> Co-authored-by: এ許我辞忧࿐♡ <127636623+Smiling-Weeping-zhr@users.noreply.github.com> Co-authored-by: Vicky <vicky_3021@163.com> Co-authored-by: MING_X <119648793+MING-ZCH@users.noreply.github.com> Co-authored-by: Nobody-ML <1755309985@qq.com> Co-authored-by: 8baby8 <3345710651@qq.com> Co-authored-by: chaoke <101492509+8baby8@users.noreply.github.com> Co-authored-by: aJupyter <ajupyter@163.com> Co-authored-by: HongCheng <kwchenghong@gmail.com> Co-authored-by: santiagoTOP <“1537211712top@gmail.com”>
51 lines
1.5 KiB
Markdown
51 lines
1.5 KiB
Markdown
# EmoLLM's general evaluation
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## Introduction
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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.
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## Installation
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- Python 3.x
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- PyTorch
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- Transformers
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- Datasets
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- NLTK
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- Rouge
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- Jieba
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It can be installed using the following command:
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```bash
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pip install torch transformers datasets nltk rouge jieba
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```
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## Usage
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### convert.py
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Convert raw multi-round conversation data into single round data for evaluation.
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### eval.py
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The `eval.py` script is used to generate the doctor's response and evaluate it, mainly divided into the following parts:
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1. Load the model and word divider.
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2. Set test parameters, such as the number of test data and batch size.
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3. Obtain data.
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4. Generate responses and evaluate.
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### metric.py
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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.
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## Results
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Test the data in data.json with the following results:
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| Model | ROUGE-1 | ROUGE-2 | ROUGE-L | BLEU-1 | BLEU-2 | BLEU-3 | BLEU-4 |
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|----------|---------|---------|---------|---------|---------|---------|---------|
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| Qwen1_5-0_5B-chat | 27.23% | 8.55% | 17.05% | 26.65% | 13.11% | 7.19% | 4.05% |
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| InternLM2_7B_chat_qlora | 37.86% | 15.23% | 24.34% | 39.71% | 22.66% | 14.26% | 9.21% |
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| InternLM2_7B_chat_full | 32.45% | 10.82% | 20.17% | 30.48% | 15.67% | 8.84% | 5.02% |
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