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https://github.com/hitz-zentroa/GoLLIE

Guideline following Large Language Model for Information Extraction
https://github.com/hitz-zentroa/GoLLIE

code-llama event-extraction gollie guidelines hugginface-hub huggingface inference information-extraction llama llama2 llm llms named-entity-recognition relation-extraction state-of-the-art text-generation training transformer

Last synced: 3 months ago
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Guideline following Large Language Model for Information Extraction

Host: GitHub
URL: https://github.com/hitz-zentroa/GoLLIE
Owner: hitz-zentroa
License: apache-2.0
Created: 2023-10-05T15:38:20.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2024-07-24T16:22:15.000Z (4 months ago)
Last Synced: 2024-08-02T04:37:07.743Z (3 months ago)
Topics: code-llama, event-extraction, gollie, guidelines, hugginface-hub, huggingface, inference, information-extraction, llama, llama2, llm, llms, named-entity-recognition, relation-extraction, state-of-the-art, text-generation, training, transformer
Language: Python
Homepage: https://hitz-zentroa.github.io/GoLLIE/
Size: 10.8 MB
Stars: 264
Watchers: 7
Forks: 18
Open Issues: 6
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

StarryDivineSky - hitz-zentroa/GoLLIE

README

        


    


    

    


    


Guideline following Large Language Model for Information Extraction




    

    

    

    

    




     

    

    


     






We present   GoLLIE, a Large Language Model trained to follow annotation guidelines. GoLLIE outperforms previous approaches on zero-shot Information Extraction and allows the user to perform inferences with annotation schemas defined on the fly. Different from previous approaches, GoLLIE is able to follow detailed definitions and does not only rely on the knowledge already encoded in the LLM. Code and models are publicly available.

- 📒 Blog Post: [GoLLIE: Guideline-following Large Language Model for Information Extraction](https://hitz-zentroa.github.io/GoLLIE/)

- 📖 Paper: [GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction](https://openreview.net/forum?id=Y3wpuxd7u9)

- GoLLIE in the 🤗HuggingFace Hub: [HiTZ/gollie](https://huggingface.co/collections/HiTZ/gollie-651bf19ee315e8a224aacc4f)

- 🚀 Example Jupyter Notebooks: [GoLLIE Notebooks](notebooks/)









## Schema definition and inference example

The labels are represented as Python classes, and the guidelines or instructions are introduced as docstrings. The model start generating after the `result = [` line.







## Installation

You will need to install the following dependencies to run the GoLLIE codebase:

```bash

Pytorch >= 2.0.0 | https://pytorch.org/get-started

We recommend that you install the 2.1.0 version or newer, as it includes important bug fixes.

transformers >= 4.33.1

pip install --upgrade transformers

PEFT >= 0.4.0

pip install --upgrade peft

bitsandbytes >= 0.40.0

pip install --upgrade bitsandbytes

Flash Attention 2.0

pip install flash-attn --no-build-isolation

pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary

```

You will also need these dependencies

```bash

pip install numpy black Jinja2 tqdm rich psutil datasets ruff wandb fschat

```

## Pretrained models

We release three GoLLIE models based on [CODE-LLama](https://huggingface.co/codellama) (7B, 13B, and 34B). The models are available in the 🤗HuggingFace Hub.

| Model | Supervised average F1 | Zero-shot average F1 |                     🤗HuggingFace Hub                     |

|---|:---------------------:|:--------------------:|:---------------------------------------------------------:|

| GoLLIE-7B |         73.0          |         55.3         |  [HiTZ/GoLLIE-7B](https://huggingface.co/HiTZ/GoLLIE-7B)  |

| GoLLIE-13B |         73.9          |         56.0         | [HiTZ/GoLLIE-13B](https://huggingface.co/HiTZ/GoLLIE-13B) |

| GoLLIE-34B |       **75.0**        |       **57.2**       | [HiTZ/GoLLIE-34B](https://huggingface.co/HiTZ/GoLLIE-34B) |

## How to use GoLLIE

Please take a look at our 🚀 Example Jupyter Notebooks to learn how to use GoLLIE: [GoLLIE Notebooks](notebooks/)

## Currently supported tasks

This is the list of task used for training and evaluating GoLLIE. However, as demonstrated in the  🚀 [Create Custom Task notebook](notebooks/Create%20Custom%20Task.ipynb) GoLLIE can perform a wide range of unseen tasks. 

For more info, read our [📖Paper](https://arxiv.org/abs/2310.03668).







We plan to continue adding more tasks to the list. If you want to contribute, please feel free to open a PR or contact us. You can use as example the already implemented tasks in the `src/tasks` folder.

## Generate the GoLLIE dataset

The configuration files used to generate the GoLLIE dataset are available in the [configs/data_configs/](configs/data_configs/) folder.

You can generate the dataset by running the following command (See [bash_scripts/generate_data.sh](bash_scripts/generate_data.sh) for more info): 

```bash

CONFIG_DIR="configs/data_configs"

OUTPUT_DIR="data/processed_w_examples"

python -m src.generate_data \

     --configs \

        ${CONFIG_DIR}/ace_config.json \

        ${CONFIG_DIR}/bc5cdr_config.json \

        ${CONFIG_DIR}/broadtwitter_config.json \

        ${CONFIG_DIR}/casie_config.json \

        ${CONFIG_DIR}/conll03_config.json \

        ${CONFIG_DIR}/crossner_ai_config.json \

        ${CONFIG_DIR}/crossner_literature_config.json \

        ${CONFIG_DIR}/crossner_music_config.json \

        ${CONFIG_DIR}/crossner_politics_config.json \

        ${CONFIG_DIR}/crossner_science_config.json \

        ${CONFIG_DIR}/diann_config.json \

        ${CONFIG_DIR}/e3c_config.json \

        ${CONFIG_DIR}/europarl_config.json \

        ${CONFIG_DIR}/fabner_config.json \

        ${CONFIG_DIR}/harveyner_config.json \

        ${CONFIG_DIR}/mitmovie_config.json \

        ${CONFIG_DIR}/mitrestaurant_config.json \

        ${CONFIG_DIR}/mitmovie_config.json \

        ${CONFIG_DIR}/multinerd_config.json \

        ${CONFIG_DIR}/ncbidisease_config.json \

        ${CONFIG_DIR}/ontonotes_config.json \

        ${CONFIG_DIR}/rams_config.json \

        ${CONFIG_DIR}/tacred_config.json \

        ${CONFIG_DIR}/wikievents_config.json \

        ${CONFIG_DIR}/wnut17_config.json \

     --output ${OUTPUT_DIR} \

     --overwrite_output_dir \

     --include_examples

```

**We do not redistribute the datasets used to train and evaluate GoLLIE**. Not all of them are publicly available; some require a license to access them.

For the datasets available in the HuggingFace Datasets library, the script will download them automatically.

For the following datasets, you must provide the path to the dataset by modifying the corresponding [configs/data_configs/](configs/data_configs/) file: [ACE05](https://catalog.ldc.upenn.edu/LDC2006T06) ([Preprocessing script](https://github.com/hitz-zentroa/GoLLIE/blob/main/src/tasks/ace/preprocess_ace.py)), [CASIE](https://github.com/Ebiquity/CASIE/tree/master/data), [CrossNer](https://github.com/zliucr/CrossNER), [DIANN](http://nlp.uned.es/diann/), [E3C](https://github.com/hltfbk/E3C-Corpus/tree/main/preprocessed_data/clinical_entities/English), [HarveyNER](https://github.com/brickee/HarveyNER/tree/main/data/tweets), [MitMovie](https://groups.csail.mit.edu/sls/downloads/movie/), [MitRestaurant](https://groups.csail.mit.edu/sls/downloads/restaurant/), [RAMS](https://nlp.jhu.edu/rams/), [TACRED](https://nlp.stanford.edu/projects/tacred/), [WikiEvents](https://github.com/raspberryice/gen-arg).

If you encounter difficulties generating the dataset, please don't hesitate to contact us.

## How to train your own GoLLIE

First, you need to generate the GoLLIE dataset. See the previous section for more info.

Second, you must create a configuration file. Please, see the [configs/model_configs](configs/model_configs) folder for examples. 

Finally, you can train your own GoLLIE by running the following command (See [bash_scripts/](bash_scripts/) folder for more examples): 

```bash

CONFIGS_FOLDER="configs/model_configs"

python3 -m src.run ${CONFIGS_FOLDER}/GoLLIE+-7B_CodeLLaMA.yaml

```

## How to evaluate a model

First, you need to generate the GoLLIE dataset. See the previous section for more info.

Second, you must create a configuration file. Please, see the [configs/model_configs/eval](configs/model_configs/eval) folder for examples. 

Finally, you can evaluate your own GoLLIE by running the following command (See [bash_scripts/eval](bash_scripts/eval) folder for more examples): 

```bash

CONFIGS_FOLDER="configs/model_configs/eval"

python3 -m src.run ${CONFIGS_FOLDER}/GoLLIE+-7B_CodeLLaMA.yaml

```

## Citation

```bibtex

@inproceedings{

    sainz2024gollie,

    title={Go{LLIE}: Annotation Guidelines improve Zero-Shot Information-Extraction},

    author={Oscar Sainz and Iker Garc{\'\i}a-Ferrero and Rodrigo Agerri and Oier Lopez de Lacalle and German Rigau and Eneko Agirre},

    booktitle={The Twelfth International Conference on Learning Representations},

    year={2024},

    url={https://openreview.net/forum?id=Y3wpuxd7u9}

}

```