Token Classification
Transformers
TensorBoard
Safetensors
PyTorch
English
cobald_parser
feature-extraction
custom_code
Eval Results (legacy)
Instructions to use houcha/distil-test-common-vocab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use houcha/distil-test-common-vocab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="houcha/distil-test-common-vocab", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("houcha/distil-test-common-vocab", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Model Card for distil-test-common-vocab
A transformer-based multihead parser for CoBaLD annotation.
This model parses a pre-tokenized CoNLL-U text and jointly labels each token with three tiers of tags:
- Grammatical tags (lemma, UPOS, XPOS, morphological features),
- Syntactic tags (basic and enhanced Universal Dependencies),
- Semantic tags (deep slot and semantic class).
Model Sources
- Repository: https://github.com/CobaldAnnotation/CobaldParser
- Paper: https://dialogue-conf.org/wp-content/uploads/2025/04/BaiukIBaiukAPetrovaM.009.pdf
- Demo: [coming soon]
Citation
@inproceedings{baiuk2025cobald,
title={CoBaLD Parser: Joint Morphosyntactic and Semantic Annotation},
author={Baiuk, Ilia and Baiuk, Alexandra and Petrova, Maria},
booktitle={Proceedings of the International Conference "Dialogue"},
volume={I},
year={2025}
}
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Model tree for houcha/distil-test-common-vocab
Base model
distilbert/distilbert-base-uncasedEvaluation results
- Null F1 on enhanced-ud-syntaxvalidation set self-reported0.250
- Ud Jaccard on enhanced-ud-syntaxvalidation set self-reported0.730
- Eud Jaccard on enhanced-ud-syntaxvalidation set self-reported0.544