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

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  • License: MIT License (MIT)
  • Author: Luke Gessler
  • Tags discourse parsing , rhetorical structure theory , RST , NLP , computational linguistics
  • Requires: Python >=3.10
  • Provides-Extra: dev
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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

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."
<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 parsed RS4 tree is printed to stdout.

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())
# <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>

from_pretrained accepts a Hub repo id, a local run directory, or a .pt path. Optional kwargs include device, revision, cache_dir, token.

Other Ways to Run Inference

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 │
└──────────────┴──────────┴──────────────────┴──────────────────────────────┴───────────────────────┴───────────┴──────┴──────────────────┘

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

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Project links
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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

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