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
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# BERTicSENTPOS4
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This model is a fine-tuned version of [
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It achieves the following results on the evaluation set:
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- Loss: 0.0444
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- F1: 0.5263
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# BERTicSENTPOS4
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This model is a fine-tuned version of [classla/bcms-bertic](https://huggingface.co/classla/bcms-bertic)
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modified for text classification on the curated definitions from Serbian Wordent.
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It achieves the following results on the evaluation set:
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- Loss: 0.0444
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- F1: 0.5263
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