GoLLIE

<p align="center"> <br> <img src="assets/GoLLIE.png" style="height: 250px;"> <br> <h2 align="center"><b>G</b>uideline f<b>o</b>llowing <b>L</b>arge <b>L</b>anguage Model for <b>I</b>nformation <b>E</b>xtraction</h2> <p align="center"> <a href="https://twitter.com/intent/tweet?text=Wow+this+new+model+is+amazing:&url=https%3A%2F%2Fgithub.com%2Fhitz-zentroa%2FGoLLIE"><img alt="Twitter" src="https://img.shields.io/twitter/url?style=social&url=https%3A%2F%2Fgithub.com%2Fhitz-zentroa%2FGoLLIE"></a> <a href="https://github.com/hitz-zentroa/GoLLIE/blob/main/LICENSE"><img alt="GitHub license" src="https://img.shields.io/github/license/hitz-zentroa/GoLLIE"></a> <a href="https://huggingface.co/collections/HiTZ/gollie-651bf19ee315e8a224aacc4f"><img alt="Pretrained Models" src="https://img.shields.io/badge/🤗HuggingFace-Pretrained Models-green"></a> <a href="https://hitz-zentroa.github.io/GoLLIE/"><img alt="Blog" src="https://img.shields.io/badge/📒-Blog Post-blue"></a> <a href="https://arxiv.org/abs/2310.03668"><img alt="Paper" src="https://img.shields.io/badge/📖-Paper-orange"></a> <br> <a href="http://www.hitz.eus/"><img src="https://img.shields.io/badge/HiTZ-Basque%20Center%20for%20Language%20Technology-blueviolet"></a> <a href="http://www.ixa.eus/?language=en"><img src="https://img.shields.io/badge/IXA-%20NLP%20Group-ff3333"></a> <br> <br> </p> <p align="justify"> We present <img src="assets/GoLLIE.png" width="20"> 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
• 📖 Paper: GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
• <img src="assets/GoLLIE.png" width="20">GoLLIE in the 🤗HuggingFace Hub: HiTZ/gollie
• 🚀 Example Jupyter Notebooks: GoLLIE Notebooks

</p> <p align="center"> <img src="assets/zero_shot_results.png"> </p>

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.

<!--- ```Python # Entity definitions @dataclass class Launcher(Template): """Refers to a vehicle designed primarily to transport payloads from the Earth's surface to space. Launchers can carry various payloads, including satellites, crewed spacecraft, and cargo, into various orbits or even beyond Earth's orbit. They are usually multi-stage vehicles that use rocket engines for propulsion.""" mention: str """ The name of the launcher vehicle. Such as: "Sturn V", "Atlas V", "Soyuz", "Ariane 5" """ space_company: str # The company that operates the launcher. Such as: "Blue origin", "ESA", "Boeing", "ISRO", "Northrop Grumman", "Arianespace" crew: List[str] # Names of the crew members boarding the Launcher. Such as: "Neil Armstrong", "Michael Collins", "Buzz Aldrin" @dataclass class Mission(Template): """Any planned or accomplished journey beyond Earth's atmosphere with specific objectives, either crewed or uncrewed. It includes missions to satellites, the International Space Station (ISS), other celestial bodies, and deep space.""" mention: str """ The name of the mission. Such as: "Apollo 11", "Artemis", "Mercury" """ date: str # The start date of the mission departure: str # The place from which the vehicle will be launched. Such as: "Florida", "Houston", "French Guiana" destination: str # The place or planet to which the launcher will be sent. Such as "Moon", "low-orbit", "Saturn" # This is the text to analyze text = ( "The Ares 3 mission to Mars is scheduled for 2032. The Starship rocket build by SpaceX will take off from Boca Chica," "carrying the astronauts Max Rutherford, Elena Soto, and Jake Martinez." ) # The annotation instances that take place in the text above are listed here result = [ Mission(mention='Ares 3', date='2032', departure='Boca Chica', destination='Mars'), Launcher(mention='Starship', space_company='SpaceX', crew=['Max Rutherford', 'Elena Soto', 'Jake Martinez']) ] ``` --> <p align="center"> <img src="assets/snippets/space_transparent.png"> </p>

Installation

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

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

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

Pretrained models

We release three GoLLIE models based on CODE-LLama (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 | | GoLLIE-13B | 73.9 | 56.0 | HiTZ/GoLLIE-13B | | GoLLIE-34B | 75.0 | 57.2 | HiTZ/GoLLIE-34B |

How to use GoLLIE

Please take a look at our 🚀 Example Jupyter Notebooks to learn how to use GoLLIE: GoLLIE 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 GoLLIE can perform a wide range of unseen tasks. For more info, read our 📖Paper.

<p align="center"> <img src="assets/datasets.png"> </p>

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

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/ file: ACE05 (Preprocessing script), CASIE, CrossNer, DIANN, E3C, HarveyNER, MitMovie, MitRestaurant, RAMS, TACRED, WikiEvents.

Regarding the ACE05 dataset, you can obtain the splits from the code of OneIE paper: [http://blender.cs.illinois.edu/software/oneie/](h