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larc-iu/
dmrst-ettin-400m-gum-12.1.0

Name: dmrst-ettin-400m-gum-12.1.0
Availability: InStock

jhu-clsp/ettin-encoder-400m

finetune

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larc-iu/dmrst-ettin-400m-gum-12.1.0

A pretrained DMRST parser trained with IUDEX.

This model uses jhu-clsp/ettin-encoder-400m as its underlying encoder.

Data

GUM 12.1.0 (Georgetown University Multilayer corpus) (English). A multi-genre English corpus (academic, biography, fiction, interview, news, reddit, speech, textbook, vlog, voyage, whow) annotated for RST among many other layers.

Relation labels: 32 labels:

adversative-antithesis, adversative-concession, adversative-contrast, attribution-negative, attribution-positive, causal-cause, causal-result, context-background, context-circumstance, contingency-condition, elaboration-additional, elaboration-attribute, evaluation-comment, explanation-evidence, explanation-justify, explanation-motivation, joint-disjunction, joint-list, joint-other, joint-sequence, mode-manner, mode-means, organization-heading, organization-phatic, organization-preparation, purpose-attribute, purpose-goal, restatement-partial, restatement-repetition, same-unit, topic-question, topic-solutionhood

Metrics

Split	span_f1	nuc_f1	rel_f1	full_f1	seg_p	seg_r	seg_f1	e2e_span_p	e2e_span_r	e2e_span_f1	e2e_nuc_p	e2e_nuc_r	e2e_nuc_f1	e2e_rel_p	e2e_rel_r	e2e_rel_f1	e2e_full_p	e2e_full_r	e2e_full_f1
dev	0.7264	0.5942	0.4736	0.4705	0.9410	0.9464	0.9437	0.6750	0.6788	0.6769	0.5332	0.5362	0.5347	0.4192	0.4216	0.4204	0.4169	0.4193	0.4181
test	0.7638	0.6365	0.5105	0.5076	0.9466	0.9423	0.9444	0.7177	0.7144	0.7161	0.5807	0.5781	0.5794	0.4571	0.4550	0.4561	0.4542	0.4521	0.4531

Usage

CLI

iudex dmrst predict \
    --hub-id larc-iu/dmrst-ettin-400m-gum-12.1.0 \
    --text "Although the experiment was carefully designed, the results were inconclusive. We plan to repeat it tonight."

To parse a raw .txt file or a directory of them instead, use --text-file <path> --output-dir out/.

Python

python

from iudex.rst.parsers.dmrst.modeling_dmrst import DMRSTParser

parser = DMRSTParser.from_pretrained("larc-iu/dmrst-ettin-400m-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())

Citation

If you use this model, please cite both the underlying paper:

bibtex

@inproceedings{dmrst_paper,
  title = {DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing},
  author = {Zhengyuan Liu, Ke Shi, Nancy F. Chen},
  booktitle = {CODI 2021},
  url = {https://aclanthology.org/2021.codi-main.15/},
}

And the IUDEX library:

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}},
}

Full training configuration

See below for the full training configuration this model was trained with.

json

{
  "train_dir": "data/gum_12.1.0_notok/train",
  "dev_dir": "data/gum_12.1.0_notok/dev",
  "test_dir": "data/gum_12.1.0_notok/test",
  "relation_types": [
    [
      "adversative-antithesis",
      "rst"
    ],
    [
      "adversative-concession",
      "rst"
    ],
    [
      "adversative-contrast",
      "multinuc"
    ],
    [
      "attribution-negative",
      "rst"
    ],
    [
      "attribution-positive",
      "rst"
    ],
    [
      "causal-cause",
      "rst"
    ],
    [
      "causal-result",
      "rst"
    ],
    [
      "context-background",
      "rst"
    ],
    [
      "context-circumstance",
      "rst"
    ],
    [
      "contingency-condition",
      "rst"
    ],
    [
      "elaboration-additional",
      "rst"
    ],
    [
      "elaboration-attribute",
      "rst"
    ],
    [
      "evaluation-comment",
      "rst"
    ],
    [
      "explanation-evidence",
      "rst"
    ],
    [
      "explanation-justify",
      "rst"
    ],
    [
      "explanation-motivation",
      "rst"
    ],
    [
      "joint-disjunction",
      "multinuc"
    ],
    [
      "joint-list",
      "multinuc"
    ],
    [
      "joint-other",
      "multinuc"
    ],
    [
      "joint-sequence",
      "multinuc"
    ],
    [
      "mode-manner",
      "rst"
    ],
    [
      "mode-means",
      "rst"
    ],
    [
      "organization-heading",
      "rst"
    ],
    [
      "organization-phatic",
      "rst"
    ],
    [
      "organization-preparation",
      "rst"
    ],
    [
      "purpose-attribute",
      "rst"
    ],
    [
      "purpose-goal",
      "rst"
    ],
    [
      "restatement-partial",
      "rst"
    ],
    [
      "restatement-repetition",
      "multinuc"
    ],
    [
      "same-unit",
      "multinuc"
    ],
    [
      "topic-question",
      "rst"
    ],
    [
      "topic-solutionhood",
      "rst"
    ]
  ],
  "relation_map": null,
  "model_name": "jhu-clsp/ettin-encoder-400m",
  "stride": 100,
  "attention_type": "dot_product",
  "classifier_use_bias": true,
  "num_rnn_layers": 1,
  "encoder_dropout": 0.5,
  "decoder_dropout": 0.5,
  "labeler_dropout": 0.5,
  "doc_gru_dropout": 0.2,
  "label_input_pooling": "mean",
  "freeze_embeddings": true,
  "freeze_encoder_layers": 3,
  "segmentation": {
    "pos_weight": 10,
    "start_loss": false,
    "scheme": "BIE",
    "loss": "crf",
    "dropout": 0.5
  },
  "detokenizer": null,
  "dlw": {
    "temperature": 2,
    "window": 30
  },
  "lr": 0.0003,
  "encoder_lr": 2e-05,
  "max_epochs": 100,
  "grad_accum": 3,
  "amp": true,
  "patience": 10,
  "max_grad_norm": 10,
  "weight_decay": 0.01,
  "num_warmup_steps": 100,
  "log_every": 1,
  "validate_every": null,
  "checkpoint_every": null,
  "checkpoint_dir": "checkpoints",
  "run_name": null,
  "seed": 42,
  "val_metric_name": "e2e_full_f1"
}