Token Classification
Transformers
TensorBoard
Safetensors
PyTorch
Swedish
cobald_parser
feature-extraction
custom_code
Eval Results (legacy)
Instructions to use E-katrin/train20_0_check with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use E-katrin/train20_0_check with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="E-katrin/train20_0_check", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("E-katrin/train20_0_check", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from E-katrin/train20_0_check: direct link, hf CLI and curl.
- Browser
- Download file 1.84 kB
-
https://huggingface.co/E-katrin/train20_0_check/resolve/main/README.md
- Command line
-
hf download hf://E-katrin/train20_0_check/README.md
-
curl -L -o README.md https://huggingface.co/E-katrin/train20_0_check/resolve/main/README.md
1.84 kB
| base_model: xlm-roberta-base | |
| datasets: E-katrin/train20 | |
| language: sv | |
| library_name: transformers | |
| license: gpl-3.0 | |
| metrics: | |
| - accuracy | |
| - f1 | |
| pipeline_tag: token-classification | |
| tags: | |
| - pytorch | |
| model-index: | |
| - name: E-katrin/train20_0_check | |
| results: | |
| - task: | |
| type: token-classification | |
| dataset: | |
| name: train20 | |
| type: E-katrin/train20 | |
| split: validation | |
| metrics: | |
| - type: f1 | |
| value: 0.7483831851253031 | |
| name: Null F1 | |
| - type: f1 | |
| value: 0.013794401146215477 | |
| name: Lemma F1 | |
| - type: f1 | |
| value: 0.04766174451743489 | |
| name: Morphology F1 | |
| - type: accuracy | |
| value: 0.5750560119492159 | |
| name: Ud Jaccard | |
| - type: accuracy | |
| value: 0.40350877192982454 | |
| name: Eud Jaccard | |
| - type: f1 | |
| value: 0.7461145129726658 | |
| name: Miscs F1 | |
| - type: f1 | |
| value: 0.4785232285712018 | |
| name: Deepslot F1 | |
| - type: f1 | |
| value: 0.35387157678175035 | |
| name: Semclass F1 | |
| # Model Card for train20_0_check | |
| 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} | |
| } | |
| ``` |