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README.md

@@ -1,7 +1,20 @@
+---
+language: en
+tags:
+- TCM
+- chinese-medicine
+- conversational
+license: apache-2.0
+datasets:
+- ZJUFanLab/TCMChat-dataset-600k
+model-index:
+- name: TCMChat-dataset-600k
+  results: []
+---
 [**中文**](./README_ZH.md) | [**English**](./README.md)
 
 <p align="center" width="100%">
-<a href="https://github.com/daiyizheng/TCMChat" target="_blank"><img src="assets/logo.png" alt="TCMChat" style="width: 25%; min-width: 300px; display: block; margin: auto;"></a>
+<a href="https://github.com/daiyizheng/TCMChat" target="_blank"><img src="logo.png" alt="TCMChat" style="width: 25%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 # TCMChat: A Generative Large Language Model for Traditional Chinese Medicine
@@ -9,29 +22,30 @@
 [![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)](https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese/blob/main/LICENSE) [![Python 3.10.12](https://img.shields.io/badge/python-3.10.12-blue.svg)](https://www.python.org/downloads/release/python-390/)
 
 ## News
-
+[2024-11-1] We have fully open-sourced the model weights and training dataset on Huggingface.                   
 [2024-5-17] Open source model weight on HuggingFace.                 
 
 ## Application
 
 ### Install
-
-```
+```shell
 git clone https://github.com/daiyizheng/TCMChat
 cd TCMChat
 ```
+Create a conda environment
+```shell
+conda create -n baichuan2 python=3.10 -y
+```
 First install the dependency package. python environment 3.10+ is recommended.
 
-```
+```shell
 pip install -r requirements.txt
 ```
 
 ### Weights download
-
 - [TCMChat](https://huggingface.co/daiyizheng/TCMChat): QA and recommendation of TCM knowledge based on baichuan2-7B-Chat.
 
 ### Inference
-
 #### Command line
 
 ```
@@ -50,111 +64,113 @@ We provide an online tool：[https://xomics.com.cn/tcmchat](https://xomics.com.c
 
 
 ### Retrain
-
 #### Dataset Download
 
-- [Pretrain dataset](https://github.com/ZJUFanLab/TCMChat/tree/master/data/pretrain) 
-- [SFT dataset](https://github.com/ZJUFanLab/TCMChat/tree/master/data/sft)
-- [Benchmark dataset](https://github.com/ZJUFanLab/TCMChat/tree/master/data/evaluate)
-
-> Note: Currently only sample data is provided. In the near future, we will fully open source the original data.
+- [Pretrain dataset](https://huggingface.co/datasets/ZJUFanLab/TCMChat-dataset-600k) 
+- [SFT dataset](https://huggingface.co/datasets/ZJUFanLab/TCMChat-dataset-600k)
+- [Benchmark dataset](https://github.com/ZJUFanLab/TCMChat/tree/master/evaluation/resources)
 
 
+> Note: Before performing pre-training, fine-tuning, and inference, please modify the relevant paths for your model, data, and other related files.
 #### Pre-training
 
 ```shell
-train_type="pretrain"
-train_file="data/pretrain/train"
-validation_file="data/pretrain/test"
-block_size="1024"
-deepspeed_dir="data/resources/deepspeed_zero_stage2_config.yml"
-num_train_epochs="2"
-export WANDB_PROJECT="TCM-${train_type}"
-date_time=$(date +"%Y%m%d%H%M%S")
-run_name="${date_time}_${block_size}"
-model_name_or_path="your/path/Baichuan2-7B-Chat"
-output_dir="output/${train_type}/${date_time}_${block_size}"
-
-
-accelerate launch --config_file ${deepspeed_dir} src/pretraining.py \
---model_name_or_path ${model_name_or_path}  \
---train_file  ${train_file}  \
---validation_file ${validation_file}  \
---preprocessing_num_workers 20  \
---cache_dir ./cache \
---block_size  ${block_size}  \
---seed 42  \
---do_train  \
---do_eval  \
---per_device_train_batch_size  32  \
---per_device_eval_batch_size  32  \
---num_train_epochs ${num_train_epochs}  \
---low_cpu_mem_usage  True \
---torch_dtype bfloat16  \
---bf16  \
---ddp_find_unused_parameters False  \
---gradient_checkpointing True  \
---learning_rate 2e-4 \
---warmup_ratio 0.05 \
---weight_decay 0.01 \
---report_to wandb  \
---run_name ${run_name}  \
---logging_dir  logs \
---logging_strategy steps \
---logging_steps 10 \
---eval_steps 50 \
---evaluation_strategy steps \
---save_steps 100 \
---save_strategy steps \
---save_total_limit 13 \
---output_dir  ${output_dir}  \
---overwrite_output_dir
+## Slurm cluster
+sbatch scripts/pretrain/baichuan2_7b_chat.slurm
+## or
+bash scripts/pretrain/baichuan2_7b_chat.sh
 ```
 
 #### Fine-tuning
-
 ```shell
-train_type="SFT"
-model_max_length="1024"
-date_time=$(date +"%Y%m%d%H%M%S")
-data_path="data/sft/sample_train_baichuan_data.json"
-model_name_or_path="your/path/pretrain"
-deepspeed_dir="data/resources/deepspeed_zero_stage2_confi_baichuan2.json"
-export WANDB_PROJECT="TCM-${train_type}"
-run_name="${train_type}_${date_time}"
-output_dir="output/${train_type}/${date_time}_${model_max_length}"
-
-
-deepspeed --hostfile="" src/fine-tune.py  \
-    --report_to "wandb" \
-    --run_name ${run_name}  \
-    --data_path ${data_path} \
-    --model_name_or_path ${model_name_or_path} \
-    --output_dir ${output_dir} \
-    --model_max_length ${model_max_length} \
-    --num_train_epochs 4 \
-    --per_device_train_batch_size 16 \
-    --gradient_accumulation_steps 1 \
-    --save_strategy epoch \
-    --learning_rate 2e-5 \
-    --lr_scheduler_type constant \
-    --adam_beta1 0.9 \
-    --adam_beta2 0.98 \
-    --adam_epsilon 1e-8 \
-    --max_grad_norm 1.0 \
-    --weight_decay 1e-4 \
-    --warmup_ratio 0.0 \
-    --logging_steps 1 \
-    --gradient_checkpointing True \
-    --deepspeed ${deepspeed_dir} \
-    --bf16 True \
-    --tf32 True
+## Slurm cluster
+sbatch scripts/sft/baichuan2_7b_chat.slurm
+## or
+bash scripts/sft/baichuan2_7b_chat.sh
 ```
-
 ### Training details
 
 Please refer to the experimental section of the paper for instructions.
 
 
+### Benchmark evaluation
+
+#### Choice Question
+```shell
+python evaluation/choices_evaluate/eval.py   --model_path_or_name /your/model/path --model_name  baichuan2-7b-chat --few_shot -sz herb --dev_file_path evaluation/resources/choice/single/tcm-herb_dev.csv --val_file_path evaluation/resources/choice/single/choice_herb_500.csv --log_dir logs/choices
+```
+
+#### Reading Comprehension
+```shell
+python infers/baichuan_infer.py \
+--model_name_or_path /your/model/path / \
+--model_type chat \
+--save_path /your/save/data/path \
+--data_path /your/data/path
+##BertScore
+python evaluation/question_rouge_bleu.py/question_bert_score.py
+## BLEU METEOR
+python evaluation/question_rouge_bleu.py/open_question_bleu.py
+## ROUGE-x
+python evaluation/question_rouge_bleu.py/open_question_rouge.py
+
+```
+
+#### Entity Extraction
+```shell
+python infers/baichuan_infer.py \
+--model_name_or_path /your/model/path / \
+--model_type chat \
+--save_path /your/save/data/path \
+--data_path /your/data/path
+
+python evaluation/ner_evaluate/tcm_entity_recognition.py
+
+```
+
+#### Medical Case Diagnosis
+```shell
+python infers/baichuan_infer.py \
+--model_name_or_path /your/model/path / \
+--model_type chat \
+--save_path /your/save/data/path \
+--data_path /your/data/path
+
+python evaluation/acc_evaluate/extract_syndrome.py
+
+```
+
+#### Herb or Formula Recommendation
+```shell
+python infers/baichuan_infer.py \
+--model_name_or_path /your/model/path / \
+--model_type chat \
+--save_path /your/save/data/path \
+--data_path /your/data/path
+
+python evaluation/recommend_evaluate/mrr_ndcg_p_r.py
+
+```
+### ADMET Prediction
+#### Regression
+```shell
+python infers/baichuan_infer.py \
+--model_name_or_path /your/model/path / \
+--model_type chat \
+--save_path /your/save/data/path \
+--data_path /your/data/path
+
+python evaluation/admet_evaluate/rmse_mae_mse.py
+
+```
+#### Classification
+```shell
+python infers/baichuan_infer.py \
+--model_name_or_path /your/model/path / \
+--model_type chat \
+--save_path /your/save/data/path \
+--data_path /your/data/path
 
+python evaluation/admet_evaluate/acc_recall_f1.py
 
+```