A collection of discourse parsers and associated code focused on readability and usability.

Navigation

Verified details

These details have been verified by PyPI
Project links
GitHub Statistics
Maintainers
Avatar for lgessler from gravatar.com lgessler

Unverified details

These details have not been verified by PyPI
Meta
  • License: MIT License (MIT)
  • Author: Luke Gessler
  • Tags discourse parsing , rhetorical structure theory , RST , NLP , computational linguistics
  • Requires: Python >=3.10
  • Provides-Extra: dev
Classifiers
Report project as malware

Project description

IUDEX

The Indiana University Discourse Exhibition (IUDEX) is a collection of parsers and other code related to discourse parsing.

Setup

For the latest release:

pip install larc-iudex

For the current state of master:

pip install git+https://github.com/larc-iu/iudex

Or for development:

git clone https://github.com/larc-iu/iudex && cd iudex
pip install -e .

Note that the command you will invoke is iudex, not larc-iudex.

Quick Start with Inference

Parse a sample document end-to-end with a pretrained DMRST model pulled from the HuggingFace Hub. From the command line:

iudex dmrst predict \
    --hub-id larc-iu/dmrst-gum-12.1.0 \
    --text "Although the experiment was carefully designed, the results were inconclusive. We plan to repeat it tonight."

This yields the parsed tree in .rs3 format printed to stdout:

<rst>
  <relations><!-- ... --></relations>
  <body>
    <segment id="1" parent="2" relname="adversative-concession">Although the experiment was carefully # designed,</segment>
    <segment id="2" parent="4" relname="span">the results were inconclusive.</segment>
    <segment id="3" parent="5" relname="span">We plan to repeat it tonight.</segment>
    <group id="4" type="span" parent="3" relname="adversative-antithesis"/>
    <group id="5" type="span"/>
  </body>
</rst>

The same flow from Python:

from iudex.rst.parsers.dmrst.modeling_dmrst import DMRSTParser
parser = DMRSTParser.from_pretrained("larc-iu/dmrst-gum-12.1.0")
tree = parser.predict_from_text(
    "Although the experiment was carefully designed, "
    "the results were inconclusive. "
    "We plan to repeat it tonight."
)
print(tree.to_rs4_string())

Yields:

<rst>
  <relations><!-- ... --></relations>
  <body>
    <segment id="1" parent="2" relname="adversative-concession">Although the experiment was carefully # designed,</segment>
    <segment id="2" parent="4" relname="span">the results were inconclusive.</segment>
    <segment id="3" parent="5" relname="span">We plan to repeat it tonight.</segment>
    <group id="4" type="span" parent="3" relname="adversative-antithesis"/>
    <group id="5" type="span"/>
  </body>
</rst>

Inference CLI

To identify a model on the command line, you may use a configuration file (--config), a PyTorch checkpoint (--checkpoint), or a HuggingFace Hub repository (--hub-id).

To provide input, you may specify an inline string (--text), a path to a raw text file or directory (--text-file, for parsers which support this), or an RS3/RS4 file or directory with gold EDUs already supplied (--input).

For --text-file and --input, results are written to --output-dir as .rs4 files.

# From the Hub, end-to-end on a directory of .txt files:
iudex dmrst predict \
    --hub-id larc-iu/dmrst-gum-12.1.0 \
    --text-file path/to/docs/ \
    --output-dir out/ \
    --device cuda

# From an explicit checkpoint:
iudex dmrst predict \
    --checkpoint checkpoints/<run_id>/best_model.pt \
    --text-file path/to/doc.txt \
    --output-dir out/

# From a trained run's config, parsing pre-segmented RS3/RS4 with gold EDUs:
iudex topdown_biaffine predict \
    --config configs/topdown_biaffine_rstdt.jsonnet \
    --input data/rstdt/test \
    --output-dir out/

Available Models

All official IUDEX model releases are tagged with iudex on the HuggingFace Hub.

Training

To train a new top-down biaffine parser on RSTDT:

iudex topdown_biaffine train configs/topdown_biaffine_rstdt.jsonnet

Note that configs/topdown_biaffine_rstdt.jsonnet is a configuration. You may either edit it directly or copy and modify it in a new location.

Grabbing Example Configurations

Model configurations required for training are not bundled with the package distributed via PyPI.

To get them you may visit the associated directory and download the configurations you're interested in manually.

If you want to grab all of them at once, you can use the command line like so:

bash / zsh / macOS / Linux:

curl -fL https://github.com/larc-iu/iudex/archive/refs/heads/master.tar.gz \
  | tar -xz --strip-components=1 --wildcards '*/configs'

Windows PowerShell:

Invoke-WebRequest https://github.com/larc-iu/iudex/archive/refs/heads/master.zip -OutFile iudex.zip
Expand-Archive iudex.zip -DestinationPath .
Move-Item iudex-master/configs configs
Remove-Item -Recurse -Force iudex-master, iudex.zip

Either leaves you with a local configs/ directory you can edit and pass to iudex … train configs/<name>.jsonnet.

Configuration Hashes

Your configuration is used as the basis for a unique hash, which (by default) corresponds to a directory under checkpoints/. This hash is used for several purposes. For example, running the same config again resumes from the last epoch's checkpoint last.pt automatically if the run was interrupted.

To view all runs and their status, you may run the runs list subcommand:

$ iudex runs list
                                                            Runs in checkpoints                                                            
┏━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━┳━━━━━━━━━━━━━━━━━━┓
┃ run_id       ┃ run_name ┃ parser           ┃ model_name                   ┃ train_dir             ┃  best_val ┃ step ┃ modified         ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━╇━━━━━━━━━━━━━━━━━━┩
│ 245b1d774676 │ -        │ dmrst            │ xlm-roberta-base             │ data/gum_12.1.0/train │    0.3099 │ 1704 │ 2026-05-18 18:02 │
│ 41bc0fe1dd50 │ -        │ topdown_biaffine │ SpanBERT/spanbert-base-cased │ data/rstdt/train      │    0.7576 │ 2149 │ 2026-05-18 13:51 │
│ 91525e48d63d │ -        │ topdown_biaffine │ SpanBERT/spanbert-base-cased │ data/gum_12.1.0/train │    0.6364 │ 1899 │ 2026-05-18 14:31 │
│ ad934ca992d4 │ -        │ dmrst            │ xlm-roberta-base             │ data/rstdt/train      │    0.4665 │ 3090 │ 2026-05-18 16:46 │
└──────────────┴──────────┴──────────────────┴──────────────────────────────┴───────────────────────┴───────────┴──────┴──────────────────┘

Monitoring with TensorBoard

Every run writes TensorBoard scalars (train loss, learning rate, gradient norm, and dev metrics) to <run_dir>/tb/. Point TensorBoard at your checkpoints directory to watch any run live or compare runs:

tensorboard --logdir checkpoints/

Pushing Models to HF Hub

You may host a trained model using each parser's push subcommand. Each uploads best_model.pt, config.json, and an auto-generated README.md in a single commit:

iudex topdown_biaffine push \
    --config configs/topdown_biaffine_rstdt.jsonnet \
    --repo-id larc-iu/topdown_biaffine-rstdt-coarse \
    [--private] [--message "..."] [--token $HF_TOKEN]

Citation

If you use IUDEX in your research, please cite it as:

Gessler, Luke. 2026. IUDEX: The Indiana University Discourse Exhibition. https://github.com/larc-iu/iudex.

BibTeX:

@misc{gessler-iudex-2026,
  author       = {Gessler, Luke},
  title        = {{IUDEX: The Indiana University Discourse Exhibition}},
  year         = {2026},
  howpublished = {\url{https://github.com/larc-iu/iudex}},
}

If you use one of the included parser re-implementations, please also cite the original paper (see each model's Hub card for the canonical reference).

Project details

Verified details

These details have been verified by PyPI
Project links
GitHub Statistics
Maintainers
Avatar for lgessler from gravatar.com lgessler

Unverified details

These details have not been verified by PyPI
Meta
  • License: MIT License (MIT)
  • Author: Luke Gessler
  • Tags discourse parsing , rhetorical structure theory , RST , NLP , computational linguistics
  • Requires: Python >=3.10
  • Provides-Extra: dev
Classifiers

Release history Release notifications | RSS feed

0.1.0a7 pre-release

This version

0.1.0a6 pre-release

0.1.0a5 pre-release

0.1.0a4 pre-release

0.1.0a3 pre-release

0.1.0a2 pre-release

0.1.0a1 pre-release

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

larc_iudex-0.1.0a6.tar.gz (85.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

larc_iudex-0.1.0a6-py3-none-any.whl (96.3 kB view details)

Uploaded Python 3

File details

Details for the file larc_iudex-0.1.0a6.tar.gz.

File metadata

  • Download URL: larc_iudex-0.1.0a6.tar.gz
  • Upload date:
  • Size: 85.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for larc_iudex-0.1.0a6.tar.gz
Algorithm Hash digest
SHA256 0c1804ac5aeff067eca3d10f166d3c55f18abbc56719114e14c41c46e6fa2263
MD5 b3546fd077f940cb0279cb2f6faa617b
BLAKE2b-256 e12631605712eeb7f09d9a83d744e0bc756ca15bf3fc0780e8a48bc31e91b03e

See more details on using hashes here.

Provenance

The following attestation bundles were made for larc_iudex-0.1.0a6.tar.gz:

Publisher: publish.yml on larc-iu/iudex

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file larc_iudex-0.1.0a6-py3-none-any.whl.

File metadata

  • Download URL: larc_iudex-0.1.0a6-py3-none-any.whl
  • Upload date:
  • Size: 96.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for larc_iudex-0.1.0a6-py3-none-any.whl
Algorithm Hash digest
SHA256 7271766ded0fee903cc71c5ac56e8c547c6a1720701138bf6361619f0b493102
MD5 e6bfe43403ff6c0719c64ae027a26813
BLAKE2b-256 6a837cd6178557fd90d8652b3fdbddfcb9dd32bd2e182466ae4e6afc8f9a2976

See more details on using hashes here.

Provenance

The following attestation bundles were made for larc_iudex-0.1.0a6-py3-none-any.whl:

Publisher: publish.yml on larc-iu/iudex

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page