Text Classification
Transformers
PyTorch
Serbian
gpt2
serbian
sentiment-analysis
wordnet
sentiwordnet
lexicon-induction
Instructions to use Tanor/SRGPTSENTNEG0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tanor/SRGPTSENTNEG0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tanor/SRGPTSENTNEG0")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tanor/SRGPTSENTNEG0") model = AutoModelForSequenceClassification.from_pretrained("Tanor/SRGPTSENTNEG0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Declare model license and document upstream attribution
Verified signatureApply maintainer-approved family license: cc-by-sa-4.0. Add the full LICENSE and NOTICE; document upstream provenance, declaration history and unresolved exact SrpWN release permissions. Only README.md, LICENSE and NOTICE are changed. Model artifacts and visibility are unchanged.f230ea7 -
Document Serbian WordNet sentiment classifier and research paper
Verified signatureExpand model card with task, labels, original base model, paper citation, paired classifiers, inference example, archived results and provenance. Preserve historical training records and declared license. README-only update; no model artifacts or repository visibility changes.df9b785
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