Update README.md

README.md

@@ -19,38 +19,27 @@ This is the official repository for [IEPile: Unearthing Large-Scale Schema-Based
 
 [**Datasets**](https://huggingface.co/datasets/zjunlp/iepie) | 
 [**Paper**](https://huggingface.co/papers/2402.14710) | 
-[**Usage**](./README.md#🚴3using-IEPile-to-train-models) |
-[**Limitations**](./README.md#7-limitations) |
-[**Statement & License**](./README.md#6-statement-and-license) |
-[**Citation**](./README.md#8-cite) 
+[**Usage**](./README.md#3using-IEPile-to-train-models) |
+[**Limitations**](./README.md#5-limitations) |
+[**Statement & License**](./README.md#4-statement-and-license) |
+[**Citation**](./README.md#6-cite) 
 
 > Please note that our IEPile may undergo **updates** (we will inform you upon their release). It is recommended to utilize the most current version.
 
 
 - [IEPile: A Large-Scale Information Extraction Corpus](#iepile-a-large-scale-information-extraction-corpus)
-  - [🎯1.Introduction](#1introduction)
-  - [📊2.Data](#2data)
+  - [1.Introduction](#1introduction)
+  - [2.Data](#2data)
     - [2.1Construction of IEPile](#21construction-of-iepile)
     - [2.2Data Format of IEPile](#22data-format-of-iepile)
-  - [🚴3.Using IEPile to Train Models](#3using-iepile-to-train-models)
-    - [3.1Environment](#31environment)
-    - [3.2Download Data and Models](#32download-data-and-models)
-    - [3.4LoRA Fine-tuning](#34lora-fine-tuning)
-  - [4.Continued Training with In-Domain Data](#4continued-training-with-in-domain-data)
-    - [4.1Training Data Conversion](#41training-data-conversion)
-    - [4.2Continued Training](#42continued-training)
-  - [5.Prediction](#5prediction)
-    - [5.1Test Data Conversion](#51test-data-conversion)
-    - [5.2Basic Model + LoRA Prediction](#52basic-model--lora-prediction)
-    - [5.3IE-Specific Model Prediction](#53ie-specific-model-prediction)
-  - [6.Evaluation](#6evaluation)
-  - [7.Statement and License](#7statement-and-license)
-  - [8.Limitations](#8limitations)
-  - [9.Cite](#9cite)
-  - [10.Acknowledgements](#10acknowledgements)
-
-
-## 🎯1.Introduction
+  - [3.Using IEPile to Train Models](#3using-iepile-to-train-models)
+  - [4.Statement and License](#4statement-and-license)
+  - [5.Limitations](#5limitations)
+  - [6.Cite](#6cite)
+  - [7.Acknowledgements](#7acknowledgements)
+
+
+## 1.Introduction
 
 
 **`IEPile`** dataset download links: [Google Drive](https://drive.google.com/file/d/1jPdvXOTTxlAmHkn5XkeaaCFXQkYJk5Ng/view?usp=sharing) | [Hugging Face](https://huggingface.co/datasets/zjunlp/iepile)
@@ -90,7 +79,7 @@ Based on **IEPile**, we fine-tuned the `Baichuan2-13B-Chat` and `LLaMA2-13B-Chat
 </details>
 
 
-## 📊2.Data
+## 2.Data
 
 
 ### 2.1Construction of IEPile
@@ -180,296 +169,13 @@ The data instance belongs to the `NER` task, is part of the `CoNLL2003` dataset,
 
 
 
-## 🚴3.Using IEPile to Train Models
-
-### 3.1Environment
-
-Before you begin, make sure to create an appropriate **virtual environment** following the instructions below:
-
-```bash
-conda create -n IEPile python=3.9   # Create a virtual environment
-conda activate IEPile               # Activate the environment
-pip install -r requirements.txt     # Install dependencies
-```
-
-
-### 3.2Download Data and Models
-
-**`IEPile`** dataset download links: [Google Drive](https://drive.google.com/file/d/1jPdvXOTTxlAmHkn5XkeaaCFXQkYJk5Ng/view?usp=sharing) | [Hugging Face](https://huggingface.co/datasets/zjunlp/IEPile)
-
-
-```python
-IEPile
-├── train.json    # Training set
-└── dev.json      # Validation set
-```
-
-Here are some of the models supported by the code in this repository:
-[[llama](https://huggingface.co/meta-llama), [alpaca](https://github.com/tloen/alpaca-lora), [vicuna](https://huggingface.co/lmsys), [zhixi](https://github.com/zjunlp/KnowLM), [falcon](https://huggingface.co/tiiuae), [baichuan](https://huggingface.co/baichuan-inc), [chatglm](https://huggingface.co/THUDM), [qwen](https://huggingface.co/Qwen), [moss](https://huggingface.co/fnlp), [openba](https://huggingface.co/OpenBA)]
-
-
-Model download links for **`LLaMA2-IEPile`** | **`Baichuan2-IEPile`** | **`KnowLM-IE-v2`**: [zjunlp/llama2-13b-IEPile-lora](https://huggingface.co/zjunlp/llama2-13b-IEPile-lora/tree/main) | [zjunlp/baichuan2-13b-IEPile-lora](https://huggingface.co/zjunlp/baichuan2-13b-IEPile-lora) | [zjunlp/KnowLM-IE-v2]()
-
-
-**`LLaMA2-IEPile`** and **`Baichuan2-IEPile`** are two models mentioned in the IEPile paper that were fine-tuned on `LLaMA2-13B-Chat` and `Baichuan2-13B-Chat` using LoRA.
-
-
-```bash
-mkdir data         # Put data here
-mkdir models       # Put base models here
-mkdir results      # Put prediction results here
-mkdir lora         # Put LoRA fine-tuning results here
-```
-
-Data should be placed in the `./data` directory.
-
-
-
-### 3.4LoRA Fine-tuning
-
-> Important Note: All the commands below should be executed within the `IEPile` directory. For example, if you want to run the fine-tuning script, you should use the following command: `bash ft_scripts/fine_llama.bash`. Please ensure your current working directory is correct.
-
-
-
-```bash
-output_dir='lora/llama2-13b-chat-v1'
-mkdir -p ${output_dir}
-CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" torchrun --nproc_per_node=8 --master_port=1287 src/test_finetune.py \
-    --do_train --do_eval \
-    --overwrite_output_dir \
-    --model_name_or_path 'models/llama2-13b-chat' \
-    --stage 'sft' \
-    --model_name 'llama' \
-    --template 'llama2' \
-    --train_file 'data/train.json' \
-    --valid_file 'data/dev.json' \
-    --output_dir=${output_dir} \
-    --per_device_train_batch_size 24 \
-    --per_device_eval_batch_size 24 \
-    --gradient_accumulation_steps 4 \
-    --preprocessing_num_workers 16 \
-    --num_train_epochs 10 \
-    --learning_rate 5e-5 \
-    --max_grad_norm 0.5 \
-    --optim "adamw_torch" \
-    --max_source_length 400 \
-    --cutoff_len 700 \
-    --max_target_length 300 \
-    --report_to tensorboard \
-    --evaluation_strategy "epoch" \
-    --save_strategy "epoch" \
-    --save_total_limit 10 \
-    --lora_r 16 \
-    --lora_alpha 32 \
-    --lora_dropout 0.05 \
-    --bf16 
-```
-
-* `model_name`: Specifies the **name of the model architecture** you want to use (7B, 13B, Base, Chat belong to the same model architecture). Currently supported models include: ["`llama`", "`alpaca`", "`vicuna`", "`zhixi`", "`falcon`", "`baichuan`", "`chatglm`", "`qwen`", "`moss`", "`openba`"]. **Please note**, this parameter should be distinguished from `--model_name_or_path`.
-* `model_name_or_path`: Model path, please download the corresponding model from [HuggingFace](https://huggingface.co/models).
-* `template`: The **name of the template** used, including: `alpaca`, `baichuan`, `baichuan2`, `chatglm3`, etc. Refer to [src/datamodule/template.py](./src/datamodule/template.py) to see all supported template names. The default is the `alpaca` template. **For `Chat` versions of models, it is recommended to use the matching template, while `Base` version models can default to using `alpaca`**.
-* `train_file`, `valid_file (optional)`: The **file paths** for the training set and validation set. Note: Currently, the format for files only supports **JSON format**.
-* `output_dir`: The **path to save the weight parameters** after LoRA fine-tuning.
-* `val_set_size`: The number of samples in the **validation set**, default is 1000.
-* `per_device_train_batch_size`, `per_device_eval_batch_size`: The `batch_size` on each GPU device, adjust according to the size of the memory.
-* `max_source_length`, `max_target_length`, `cutoff_len`: The maximum input and output lengths, and the cutoff length, which can simply be considered as the maximum input length + maximum output length. Set appropriate values according to specific needs and memory size.
-* `deepspeed`: Remove if there is not enough device resources.
-
-> Quantization can be performed by setting `bits` to 8 or 4.
-
-To learn more about parameter configuration, please refer to the [src/utils/args](./src/args). 
-
-The specific script for fine-tuning the `LLaMA2-13B-Chat` model can be found in [ft_scripts/fine_llama.bash](./ft_scripts/fine_llama.bash).
-
-
-The specific script for fine-tuning the `Baichuan2-13B-Chat` model can be found in [ft_scripts/fine_baichuan.bash](./ft_scripts/fine_baichuan.bash).bash.
-
-
-
-
-
-## 4.Continued Training with In-Domain Data
-
-Although the `Baichuan2-IEPile` and `LLaMA2-IEPile` models have undergone extensive instruction fine-tuning on multiple general datasets and thus possess a degree of **general information extraction capability**, they may still exhibit certain limitations when processing data in **specific domains** (such as `law`, `education`, `science`, `telecommunications`). To address this challenge, it is recommended to conduct **secondary training** of these models on datasets specific to these domains. This will help the models better adapt to the semantic and structural characteristics of the specific domains, significantly enhancing their **information extraction capability within those domains**.
-
-
-### 4.1Training Data Conversion
-
-Firstly, it's necessary to **format the data** to include `instruction` and `output` fields. For this purpose, we provide a script [convert_func.py](./ie2instruction/convert_func.py), which can batch convert data into a format that can be directly used by the model.
-
-
-> Before using the [convert_func.py](./ie2instruction/convert_func.py) script, please make sure to refer to the [data](./data) directory. This directory provides detailed instructions on the data format required for each task. Refer to `sample.json` to understand the format of the data before conversion, `schema.json` to see the organization of the schema, and `train.json` to describe the data format after conversion.
-
-> Additionally, you can directly use the bilingual (Chinese and English) information extraction dataset [zjunlp/InstructIE](https://huggingface.co/datasets/zjunlp/InstructIE), which includes 12 themes such as characters, vehicles, works of art, natural science, man-made objects, astronomical objects, etc.
-
-
-```bash
-python ie2instruction/convert_func.py \
-    --src_path data/NER/sample.json \
-    --tgt_path data/NER/train.json \
-    --schema_path data/NER/schema.json \
-    --language zh \
-    --task NER \
-    --split_num 6 \       
-    --random_sort \
-    --split train
-```
-
-
-* `language`: Supports two languages, `zh` (Chinese) and `en` (English), with different instruction templates used for each language.
-* `task`: Currently supports five types of tasks: ['`RE`', '`NER`', '`EE`', '`EET`', '`EEA`'].
-* `split_num`: Defines the maximum number of schemas that can be included in a single instruction. The default value is 4, and setting it to -1 means no splitting is done. The recommended number of task splits varies by task: **6 for NER, and 4 for RE, EE, EET, EEA**.
-* `random_sort`: Whether to randomize the order of schemas in the instructions. The default is False, which means schemas are sorted alphabetically.
-* `split`: Specifies the type of dataset, with options `train` or `test`.
-
-The converted training data will contain four fields: `task`, `source`, `instruction`, `output`.
-
-
-### 4.2Continued Training
-
-
-```bash
-output_dir='lora/llama2-13b-chat-v1-continue'
-mkdir -p ${output_dir}
-CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" torchrun --nproc_per_node=8 --master_port=1287 src/test_finetune.py \
-    --do_train --do_eval \
-    --overwrite_output_dir \
-    --model_name_or_path 'models/llama2-13B-Chat' \
-    --checkpoint_dir 'zjunlp/llama2-13b-iepile-lora' \
-    --stage 'sft' \
-    --model_name 'llama' \
-    --template 'llama2' \
-    --train_file 'data/train.json' \
-    --valid_file 'data/dev.json' \
-    --output_dir=${output_dir} \
-    --per_device_train_batch_size 24 \
-    --per_device_eval_batch_size 24 \
-    --gradient_accumulation_steps 4 \
-    --preprocessing_num_workers 16 \
-    --num_train_epochs 10 \
-    --learning_rate 5e-5 \
-    --max_grad_norm 0.5 \
-    --optim "adamw_torch" \
-    --max_source_length 400 \
-    --cutoff_len 700 \
-    --max_target_length 300 \
-    --report_to tensorboard \
-    --evaluation_strategy "epoch" \
-    --save_strategy "epoch" \
-    --save_total_limit 10 \
-    --lora_r 64 \
-    --lora_alpha 64 \
-    --lora_dropout 0.05 \
-    --bf16 
-```
-
-* To continue training based on the fine-tuned LoRA weights, simply point the `--checkpoint_dir` parameter to the path of the LoRA weights, for example by setting it to `'zjunlp/llama2-13b-iepile-lora'`.
-
-> Quantization can be performed by setting `bits` to 8 or 4.
-
-
-> Please note that when using **`LLaMA2-IEPile`** or **`Baichuan2-IEPile`**, keep both lora_r and lora_alpha at 64. We do not provide recommended settings for these parameters.
-
-
-* To continue training based on the fine-tuned model weights, just set the `--model_name_or_path` parameter to the path of the weights, such as `'zjunlp/KnowLM-IE-v2'`, without setting `--checkpoint_dir`.
-
-
-The script can be found at [ft_scripts/fine_continue.bash](./ft_scripts/fine_continue.bash).
-
-
-## 5.Prediction
-
-### 5.1Test Data Conversion
-
-
-Before preparing the test data conversion, please visit the [data](./data) directory to understand the data structure required for each task: 1) For the input data format, see `sample.json`. 2) For the schema format, please refer to `schema.json`. 3) For the format of the transformed data, refer to `train.json`. **Unlike training data, test data input does not need to include annotation fields (`entity`, `relation`, `event`)**.
-
-
-```bash
-python ie2instruction/convert_func.py \
-    --src_path data/NER/sample.json \
-    --tgt_path data/NER/test.json \
-    --schema_path data/NER/schema.json \
-    --language zh \
-    --task NER \
-    --split_num 6 \
-    --split test
-```
-
-When setting `split` to **test**, select the appropriate number of schemas according to the task type: **6 is recommended for NER, while 4 is recommended for RE, EE, EET, EEA**. The transformed test data will contain five fields: `id`, `task`, `source`, `instruction`, `label`.
-
-The `label` field will be used for subsequent evaluation. If the input data lacks the annotation fields (`entity`, `relation`, `event`), the transformed test data will not contain the `label` field, which is suitable for scenarios where no original annotated data is available.
-
-
-### 5.2Basic Model + LoRA Prediction
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python src/inference.py \
-    --stage sft \
-    --model_name_or_path 'models/llama2-13B-Chat' \
-    --checkpoint_dir 'zjunlp/llama2-13b-IEPile-lora' \
-    --model_name 'llama' \
-    --template 'llama2' \
-    --do_predict \
-    --input_file 'data/input.json' \
-    --output_file 'results/llama2-13b-IEPile-lora_output.json' \
-    --finetuning_type lora \
-    --output_dir 'lora/test' \
-    --predict_with_generate \
-    --max_source_length 512 \
-    --bf16 \
-    --max_new_tokens 300
-```
-
-* During inference, `model_name`, `template`, and `bf16` must be the same as the settings used during training.
-* `model_name_or_path`: Specify the path to the base model being used, which must match the corresponding LoRA model.
-* `checkpoint_dir`: The path to the LoRA weight files.
-* `output_dir`: This parameter does not take effect during inference and any path can be specified.
-* `input_file`, `output_file`: Specify the input path for the test file and the output path for the prediction results, respectively.
-* `max_source_length`, `max_new_tokens`: Set the maximum input length and the number of new tokens to be generated, adjusting according to device performance.
-
-> Quantization can be performed by setting `bits` to 8 or 4.
-
-
-### 5.3IE-Specific Model Prediction
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python src/inference.py \
-    --stage sft \
-    --model_name_or_path 'zjunlp/KnowLM-IE-v2' \
-    --model_name 'baichuan' \
-    --template 'baichuan2' \
-    --do_predict \
-    --input_file 'data/input.json' \
-    --output_file 'results/KnowLM-IE-v2_output.json' \
-    --output_dir 'lora/test' \
-    --predict_with_generate \
-    --max_source_length 512 \
-    --bf16 \
-    --max_new_tokens 300
-```
-
-`model_name_or_path`: The path to the weights of the model specialized for Information Extraction (IE).
-
-> Quantization can be performed by setting `bits` to 8 or 4.
-
-
-## 6.Evaluation
-
-We provide scripts for evaluating the F1 scores for various tasks.
-
-```bash
-python ie2instruction/eval_func.py \
-  --path1 data/NER/processed.json \
-  --task NER 
-```
+## 3.Using IEPile to Train Models
 
-* `task`: Currently supports five types of tasks: ['`RE`', '`NER`', '`EE`', '`EET`', '`EEA`'].
-* You can set `sort_by` to `source` to calculate the F1 scores on each dataset separately.
 
+Please visit our official GitHub repository for a comprehensive guide on training and inference with [IEPile](https://github.com/zjunlp/IEPile).
 
 
-## 7.Statement and License
+## 4.Statement and License
 We believe that annotated data contains the wisdom of humanity, and its existence is to promote the benefit of all humankind and help enhance our quality of life. We strongly urge all users not to use our corpus for any actions that may harm national or public security or violate legal regulations.
 We have done our best to ensure the quality and legality of the data provided. However, we also recognize that despite our efforts, there may still be some unforeseen issues, such as concerns about data protection and risks and problems caused by data misuse. We will not be responsible for these potential problems.
 For original data that is subject to usage permissions stricter than the [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en) agreement, IEPile will adhere to those stricter terms. In all other cases, our operations will be based on the [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en) license agreement.
@@ -477,7 +183,7 @@ For original data that is subject to usage permissions stricter than the [CC BY-
 
 
 
-## 8.Limitations
+## 5.Limitations
 
 From the data perspective, our study primarily focuses on schema-based IE, which limits our ability to generalize to human instructions that do not follow our specific format requirements. 
 Additionally, we do not explore the field of Open Information Extraction (Open IE); however, if we remove schema constraints, our dataset would be suitable for Open IE scenarios.
@@ -487,13 +193,13 @@ From the model perspective, due to computational resource limitations, our resea
 
 
 
-## 9.Cite
+## 6.Cite
 If you use the IEPile or the code, please cite the paper:
 
 
 
 
-## 10.Acknowledgements
+## 7.Acknowledgements
 We are very grateful for the inspiration provided by the [MathPile](mathpile) and [KnowledgePile](https://huggingface.co/datasets/Query-of-CC/Knowledge_Pile) projects. Special thanks are due to the builders and maintainers of the following datasets: [AnatEM](https://doi.org/10.1093/BIOINFORMATICS/BTT580)、[BC2GM](https://link.springer.com/chapter/10.1007/978-3-030-68763-2_48)、[BC4CHEMD](https://link.springer.com/chapter/10.1007/978-3-030-68763-2_48)、[NCBI-Disease](https://linkinghub.elsevier.com/retrieve/pii/S1532046413001974)、[BC5CDR](https://openreview.net/pdf?id=9EAQVEINuum)、[HarveyNER](https://aclanthology.org/2022.naacl-main.243/)、[CoNLL2003](https://aclanthology.org/W03-0419/)、[GENIA](https://pubmed.ncbi.nlm.nih.gov/12855455/)、[ACE2005](https://catalog.ldc.upenn.edu/LDC2006T06)、[MIT Restaurant](https://ieeexplore.ieee.org/document/6639301)、[MIT Movie](https://ieeexplore.ieee.org/document/6639301)、[FabNER](https://link.springer.com/article/10.1007/s10845-021-01807-x)、[MultiNERD](https://aclanthology.org/2022.findings-naacl.60/)、[Ontonotes](https://aclanthology.org/N09-4006/)、[FindVehicle](https://arxiv.org/abs/2304.10893)、[CrossNER](https://ojs.aaai.org/index.php/AAAI/article/view/17587)、[MSRA NER](https://aclanthology.org/W06-0115/)、[Resume NER](https://aclanthology.org/P18-1144/)、[CLUE NER](https://arxiv.org/abs/2001.04351)、[Weibo NER](https://aclanthology.org/D15-1064/)、[Boson](https://github.com/InsaneLife/ChineseNLPCorpus/tree/master/NER/boson)、[ADE Corpus](https://jbiomedsem.biomedcentral.com/articles/10.1186/2041-1480-3-15)、[GIDS](https://arxiv.org/abs/1804.06987)、[CoNLL2004](https://aclanthology.org/W04-2412/)、[SciERC](https://aclanthology.org/D18-1360/)、[Semeval-RE](https://aclanthology.org/S10-1006/)、[NYT11-HRL](https://ojs.aaai.org/index.php/AAAI/article/view/4688)、[KBP37](https://arxiv.org/abs/1508.01006)、[NYT](https://link.springer.com/chapter/10.1007/978-3-642-15939-8_10)、[Wiki-ZSL](https://aclanthology.org/2021.naacl-main.272/)、[FewRel](https://aclanthology.org/D18-1514/)、[CMeIE](https://link.springer.com/chapter/10.1007/978-3-030-60450-9_22)、[DuIE](https://link.springer.com/chapter/10.1007/978-3-030-32236-6_72)、[COAE2016](https://github.com/Sewens/COAE2016)、[IPRE](https://arxiv.org/abs/1907.12801)、[SKE2020](https://aistudio.baidu.com/datasetdetail/177191)、[CASIE](https://ojs.aaai.org/index.php/AAAI/article/view/6401)、[PHEE](https://aclanthology.org/2022.emnlp-main.376/)、[CrudeOilNews](https://aclanthology.org/2022.lrec-1.49/)、[RAMS](https://aclanthology.org/2020.acl-main.718/)、[WikiEvents](https://aclanthology.org/2021.naacl-main.69/)、[DuEE](https://link.springer.com/chapter/10.1007/978-3-030-60457-8_44)、[DuEE-Fin](https://link.springer.com/chapter/10.1007/978-3-031-17120-8_14)、[FewFC](https://ojs.aaai.org/index.php/AAAI/article/view/17720)、[CCF law](https://aistudio.baidu.com/projectdetail/4201483), and more. These datasets have significantly contributed to the advancement of this research. We are also grateful for the valuable contributions in the field of information extraction made by [InstructUIE](http://arxiv.org/abs/2304.08085) and [YAYI-UIE](http://arxiv.org/abs/2312.15548), both in terms of data and model innovation. Our research results have benefitted from their creativity and hard work as well. Additionally, our heartfelt thanks go to [hiyouga/LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory); our fine-tuning code implementation owes much to their work. The assistance provided by these academic resources has been instrumental in the completion of our research, and for this, we are deeply appreciative.