deepa2

Resources for creating, importing and using DeepA2 Argument Analysis Framework datasets

https://github.com/debatelab/deepa2

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
○
DOI references
✓
Academic publication links
Links to: arxiv.org
○
Academic email domains
○
Institutional organization owner
○
JOSS paper metadata
○
Scientific vocabulary similarity
Low similarity (14.8%) to scientific vocabulary

Keywords

argumentation datasets machine-learning natural-language-processing

Last synced: 10 months ago · JSON representation ·

Repository

Resources for creating, importing and using DeepA2 Argument Analysis Framework datasets

Basic Info

Host: GitHub
Owner: debatelab
License: apache-2.0
Language: Python
Default Branch: main
Homepage:
Size: 1.11 MB

Statistics

Stars: 6
Watchers: 2
Forks: 0
Open Issues: 16
Releases: 16

Topics

argumentation datasets machine-learning natural-language-processing

Created over 4 years ago · Last pushed over 3 years ago

Metadata Files

Readme License Citation

Deep Argument Analysis (`deepa2`)

This project provides deepa2, which

🥚 takes NLP data (e.g. NLI, argument mining) as ingredients;
🎂 bakes DeepA2 datatsets conforming to the Deep Argument Analysis Framework;
🍰 serves DeepA2 data as text2text datasets suitable for training language models.

There's a public collection of 🎂 DeepA2 datatsets baked with deepa2 at the HF hub.

The Documentation describes usage options and gives background info on the Deep Argument Analysis Framework.

Quickstart

Integrating `deepa2` into Your Training Pipeline

Install deepa2 into your ML project's virtual environment, e.g.:

bash source my-projects-venv/bin/activate python --version # should be ^3.7 python -m pip install deepa2

Add deepa2 preprocessor to your training pipeline. Your training script may look like, for example:

```sh

!/bin/bash

configure and activate environment

...

download deepa2 datasets and

prepare for text2text training

deepa2 serve \ --path some-deepa2-dataset \ # <<< 🎂 --exportformat csv \ --exportpath t2t \ # >>> 🍰

run default training script,

e.g., with 🤗 Transformers

python .../runsummarization.py \ --trainfile t2t/train.csv \ # <<< 🍰 --textcolumn "text" \ --summarycolumn "target" \ --...

clean-up

rm -r t2t ```

That's it.

Create DeepA2 datasets with `deepa2` from existing NLP data

Install poetry.

Clone the repository: bash git clone https://github.com/debatelab/deepa2-datasets.git

Install this package from within the repo's root folder: bash poetry install

Bake a DeepA2 dataset, e.g.: bash poetry run deepa2 bake \\ --name esnli \\ # <<< 🥚 --debug-size 100 \\ --export-path ./data/processed # >>> 🎂

Contribute a DeepA2Builder for another Dataset

We welcome contributions to this repository, especially scripts that port existing datasets to the DeepA2 Framework. Within this repo, a code module that transforms data into the DeepA2 format contains

a Builder class that describes how DeepA2 examples will be constructed and that implements the abstract builder.Builder interface (such as, e.g., builder.entailmentbank_builder.EnBankBuilder);
a DataLoader which provides a method for loading the raw data as a 🤗 Dataset object (such as, for example, builder.entailmentbank_builder.EnBankLoader) -- you may use deepa2.DataLoader as is in case the data is available in a way compatible with 🤗 Dataset;
dataclasses which describe the features of the raw data and the preprocessed data, and which extend the dummy classes deepa2.RawExample and deepa2.PreprocessedExample;
a collection of unit tests that check the concrete Builder's methods (such as, e.g., tests/test_enbank.py);
a documentation of the pipeline (as for example in docs/esnli.md).

Consider suggesting to collaboratively construct such a pipeline by opening a new issue.

Citation

This repository builds on and extends the DeepA2 Framework originally presented in:

bibtex @article{betz2021deepa2, title={DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models}, author={Gregor Betz and Kyle Richardson}, year={2021}, eprint={2110.01509}, archivePrefix={arXiv}, primaryClass={cs.CL} }

Owner

Name: DebateLab @ KIT
Login: debatelab
Kind: organization

Website: https://debatelab.github.io/
Repositories: 5
Profile: https://github.com/debatelab

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Betz"
  given-names: "Gregor"
  orcid: "https://orcid.org/0000-0001-5802-5030"
  website: "https://www.gregorbetz.de/"
- family-names: "Richardson"
  given-names: "Kyle"
  website: "https://www.krichardson.me/"
title: "DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models"
version: 0.1.1
date-released: 2021-10-04
url: "https://arxiv.org/abs/2110.01509"

GitHub Events

Total

Watch event: 3

Last Year

Watch event: 3

Issues and Pull Requests

Last synced: about 1 year ago

All Time

Total issues: 27
Total pull requests: 20
Average time to close issues: 14 days
Average time to close pull requests: 35 minutes
Total issue authors: 2
Total pull request authors: 2
Average comments per issue: 0.19
Average comments per pull request: 0.0
Merged pull requests: 20
Bot issues: 0
Bot pull requests: 1

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

ggbetz (24)
yakazimir (3)

Pull Request Authors

ggbetz (19)
dependabot[bot] (1)

Top Labels

Issue Labels

new dataset (12) documentation (2) bug (1) enhancement (1)

Pull Request Labels

dependencies (1)

Packages

Total packages: 1
Total downloads:
- pypi 63 last-month

Total dependent packages: 0
Total dependent repositories: 1
Total versions: 17
Total maintainers: 1

pypi.org: deepa2

Cast NLP data as multiangular DeepA2 datasets and integrate these in training pipeline

Homepage: https://github.com/debatelab/deepa2
Documentation: https://deepa2.readthedocs.io/
License: Apache-2.0
Latest release: 0.1.16
published over 3 years ago

Versions: 17
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 63 Last month

Rankings

Dependent packages count: 10.1%

Dependent repos count: 21.5%

Average: 27.4%

Stargazers count: 27.8%

Forks count: 29.8%

Downloads: 47.9%

Maintainers (1)

ggbetz

Last synced: 10 months ago

Dependencies

poetry.lock pypi

104 dependencies

pyproject.toml pypi

black ^22.1.0 develop
coverage ^6.4.1 develop
flake8 ^4.0.1 develop
ipykernel ^6.7.0 develop
ipython 7.31.1 develop
matplotlib ^3.5.1 develop
mypy ^0.931 develop
pandas-stubs ^1.2.0 develop
pylint ^2.12.2 develop
pytest ^6.2.5 develop
types-PyYAML ^6.0.4 develop
types-requests ^2.27.8 develop
Jinja2 ^3.0.3
datasets ^1.18.0
editdistance ^0.6.0
networkx ^2.6.3
numpy 1.21.5
pandas 1.3.5
pyarrow ^6.0.1
python >=3.7.1,<3.11
requests ^2.27.1
sacrebleu ^2.1.0
ttp ^0.8.4
typer ^0.4.0

.github/workflows/code_quality_checks.yml actions

actions/cache v2 composite
actions/checkout v2 composite
actions/setup-python v1 composite
snok/install-poetry v1.3.0 composite

.github/workflows/run_cc_pytest.yml actions

actions/cache v2 composite
actions/checkout v2 composite
actions/setup-python v1 composite
paambaati/codeclimate-action v3.0.0 composite
snok/install-poetry v1.3.0 composite

.github/workflows/run_pytest.yml actions

actions/cache v2 composite
actions/checkout v2 composite
actions/setup-python v1 composite
snok/install-poetry v1.3.0 composite

.github/workflows/run_pytest37.yml actions

actions/cache v2 composite
actions/checkout v2 composite
actions/setup-python v1 composite
snok/install-poetry v1.3.0 composite