Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/dayyass/qaner

Unofficial implementation of QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition.
https://github.com/dayyass/qaner

data-science machine-learning named-entity-recognition natural-language-processing ner nlp python python3 question-answering

Last synced: 7 days ago
JSON representation

Unofficial implementation of QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition.

Host: GitHub
URL: https://github.com/dayyass/qaner
Owner: dayyass
License: mit
Created: 2022-06-16T10:37:47.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2022-10-15T09:53:58.000Z (about 2 years ago)
Last Synced: 2024-04-25T00:02:36.010Z (7 months ago)
Topics: data-science, machine-learning, named-entity-recognition, natural-language-processing, ner, nlp, python, python3, question-answering
Language: Python
Homepage: https://arxiv.org/abs/2203.01543
Size: 2.29 MB
Stars: 62
Watchers: 2
Forks: 6
Open Issues: 10
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        [![tests](https://github.com/dayyass/qaner/actions/workflows/tests.yml/badge.svg)](https://github.com/dayyass/qaner/actions/workflows/tests.yml)

[![linter](https://github.com/dayyass/qaner/actions/workflows/linter.yml/badge.svg)](https://github.com/dayyass/qaner/actions/workflows/linter.yml)

[![python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://github.com/dayyass/qaner#requirements)

[![release (latest by date)](https://img.shields.io/github/v/release/dayyass/qaner)](https://github.com/dayyass/qaner/releases/latest)

[![license](https://img.shields.io/github/license/dayyass/qaner?color=blue)](https://github.com/dayyass/qaner/blob/main/LICENSE)

[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-black)](https://github.com/dayyass/qaner/blob/main/.pre-commit-config.yaml)

[![code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

[![pypi version](https://img.shields.io/pypi/v/qaner)](https://pypi.org/project/qaner)

[![pypi downloads](https://img.shields.io/pypi/dm/qaner)](https://pypi.org/project/qaner)

My public talk about this project at Sberloga:


[**QaNER - NER via Exractive QA**](https://youtu.be/JRec8FpjhpM)

# QaNER

Unofficial implementation of [*QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition*](https://arxiv.org/abs/2203.01543).

You can adopt this pipeline for arbitrary [BIO-markup](https://github.com/dayyass/QaNER/tree/main/data/conll2003) data.

## Installation

```

pip install qaner

```

## CoNLL-2003

Pipeline results on CoNLL-2003 dataset:

- [Metrics](https://tensorboard.dev/experiment/FEsbNJdmSd2LGVhga8Ku0Q/)

- [Trained Hugging Face model](https://huggingface.co/dayyass/qaner-conll-bert-base-uncased)

## How to use

### Training

Script for training QaNER model:

```

qaner-train \

--bert_model_name 'bert-base-uncased' \

--path_to_prompt_mapper 'data/conll2003/prompt_mapper.json' \

--path_to_train_data 'data/conll2003/train.bio' \

--path_to_test_data 'data/conll2003/test.bio' \

--path_to_save_model 'dayyass/qaner-conll-bert-base-uncased' \

--n_epochs 2 \

--batch_size 128 \

--learning_rate 1e-5 \

--seed 42 \

--log_dir 'runs/qaner'

```

Required arguments:

- **--bert_model_name** - base bert model for QaNER fine-tuning

- **--path_to_prompt_mapper** - path to prompt mapper json file

- **--path_to_train_data** - path to train data ([BIO-markup](https://github.com/dayyass/QaNER/tree/main/data/conll2003))

- **--path_to_test_data** - path to test data ([BIO-markup](https://github.com/dayyass/QaNER/tree/main/data/conll2003))

- **--path_to_save_model** - path to save trained QaNER model

- **--n_epochs** - number of epochs to fine-tune

- **--batch_size** - batch size

- **--learning_rate** - learning rate

Optional arguments:

- **--seed** - random seed for reproducibility (default: 42)

- **--log_dir** - tensorboard log_dir (default: 'runs/qaner')

### Infrerence

Script for inference trained QaNER model:

```

qaner-inference \

--context 'EU rejects German call to boycott British lamb .' \

--question 'What is the organization?' \

--path_to_prompt_mapper 'data/conll2003/prompt_mapper.json' \

--path_to_trained_model 'dayyass/qaner-conll-bert-base-uncased' \

--n_best_size 1 \

--max_answer_length 100 \

--seed 42

```

Result:

```

question: What is the organization?

context: EU rejects German call to boycott British lamb .

answer: [Span(token='EU', label='ORG', start_context_char_pos=0, end_context_char_pos=2)]

```

Required arguments:

- **--context** - sentence to extract entities from

- **--question** - question prompt with entity name to extract (examples below)

- **--path_to_prompt_mapper** - path to prompt mapper json file

- **--path_to_trained_model** - path to trained QaNER model

- **--n_best_size** - number of best QA answers to consider

Optional arguments:

- **--max_answer_length** - entity max length to eliminate very long entities (default: 100)

- **--seed** - random seed for reproducibility (default: 42)

Possible inference questions for CoNLL-2003:

- What is the location? (LOC)

- What is the person? (PER)

- What is the organization? (ORG)

- What is the miscellaneous entity? (MISC)

## Requirements

Python >= 3.7

## Citation

```bibtex

@misc{liu2022qaner,

    title         = {QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition},

    author        = {Andy T. Liu and Wei Xiao and Henghui Zhu and Dejiao Zhang and Shang-Wen Li and Andrew Arnold},

    year          = {2022},

    eprint        = {2203.01543},

    archivePrefix = {arXiv},

    primaryClass  = {cs.LG}

}

```