Text Classification
Transformers
PyTorch
Safetensors
Serbian
electra
serbian
sentiment-analysis
wordnet
sentiwordnet
lexicon-induction
Instructions to use Tanor/BERTicSENTPOS4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tanor/BERTicSENTPOS4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tanor/BERTicSENTPOS4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tanor/BERTicSENTPOS4") model = AutoModelForSequenceClassification.from_pretrained("Tanor/BERTicSENTPOS4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download NOTICE from Tanor/BERTicSENTPOS4: direct link, hf CLI and curl.
- Browser
- Download file 1.93 kB
-
https://huggingface.co/Tanor/BERTicSENTPOS4/resolve/main/NOTICE
- Command line
-
hf download hf://Tanor/BERTicSENTPOS4/NOTICE
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curl -L -o NOTICE https://huggingface.co/Tanor/BERTicSENTPOS4/resolve/main/NOTICE
1.93 kB
| Tanor/BERTicSENTPOS4 | |
| Model and documentation license: Apache License 2.0 | |
| License text: LICENSE | |
| Original base model: classla/bcms-bertic | |
| Upstream attribution: Nikola Ljubešić and Davor Lauc; published by CLASSLA | |
| Upstream model card: https://huggingface.co/classla/bcms-bertic/blob/5db9755d6152ec6403c0201223e4848bd1b98a48/README.md | |
| Upstream license declaration: apache-2.0 | |
| The upstream card revision above is the licensing-source snapshot inspected on | |
| 23 September 2026; it is not asserted to be the original training revision. | |
| Base-model reference: | |
| Ljubešić, Nikola and Lauc, Davor (2021). BERTić - The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian. https://aclanthology.org/2021.bsnlp-1.5/ | |
| Fine-tuning and release: Tanor (Saša Petalinkar) | |
| This model adapts the base-model family for binary POS classification of | |
| Serbian WordNet synset glosses using expansion iteration T4. | |
| Documented fine-tuned weights revision: d5dda358196a587e0ab17a00a2da17f996c881a3 | |
| Research reference: | |
| Saša Petalinkar, Ranka M. Stanković, and Milica Ikonić Nešić (2025). | |
| Comparative analysis of methods for creating a sentiment lexicon of the Serbian WordNet. | |
| The Electronic Library, 43(4), 547–577. | |
| https://doi.org/10.1108/EL-08-2024-0253 | |
| Companion code: https://github.com/sasa5linkar/Serbian-WordNet-Sentiment-Lexicon-Analysis | |
| License documentation change, 23 September 2026: | |
| The existing `apache-2.0` declaration was retained and expanded with the full license text and attribution on 23 September 2026. | |
| Model weights, configuration, and tokenizer files were not modified. | |
| External data and code keep their own terms. The exact SrpWN training-release | |
| permissions remain to be documented; see README.md, License / Scope and | |
| training-resource permissions. This notice provides attribution and provenance; | |
| it does not add conditions to the license. Warranty and liability provisions | |
| are set out in LICENSE. | |