Text Classification
Transformers
PyTorch
Serbian
gpt2
serbian
sentiment-analysis
wordnet
sentiwordnet
lexicon-induction
Instructions to use Tanor/SRGPTSENTNEG2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tanor/SRGPTSENTNEG2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tanor/SRGPTSENTNEG2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tanor/SRGPTSENTNEG2") model = AutoModelForSequenceClassification.from_pretrained("Tanor/SRGPTSENTNEG2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download NOTICE from Tanor/SRGPTSENTNEG2: direct link, hf CLI and curl.
- Browser
- Download file 1.87 kB
-
https://huggingface.co/Tanor/SRGPTSENTNEG2/resolve/main/NOTICE
- Command line
-
hf download hf://Tanor/SRGPTSENTNEG2/NOTICE
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curl -L -o NOTICE https://huggingface.co/Tanor/SRGPTSENTNEG2/resolve/main/NOTICE
1.87 kB
| Tanor/SRGPTSENTNEG2 | |
| Model and documentation license: Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) | |
| License text: LICENSE | |
| Original base model: jerteh/gpt2-orao | |
| Upstream attribution: Mihailo Škorić; published by JeRTeh | |
| Upstream model card: https://huggingface.co/jerteh/gpt2-orao/blob/a7dfec6b4290bcb48e579e13e39d20d0ea574449/README.md | |
| Upstream license declaration: cc-by-sa-4.0 | |
| 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: | |
| Škorić, Mihailo (2024). Novi jezički modeli za srpski jezik. Infoteka, 24(1). https://arxiv.org/abs/2402.14379 | |
| Fine-tuning and release: Tanor (Saša Petalinkar) | |
| This model adapts the base-model family for binary NEG classification of | |
| Serbian WordNet synset glosses using expansion iteration T2. | |
| Documented fine-tuned weights revision: 609984070541e85cc2f51e0bafdfeeb42653c25c | |
| 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 `cc-by-sa-4.0` declaration, full license text, and attribution were added 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. | |