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 model.safetensors from Tanor/BERTicSENTPOS4: direct link, hf CLI and curl.
- Browser
- Download file 442 MB
-
https://huggingface.co/Tanor/BERTicSENTPOS4/resolve/main/model.safetensors
- Command line
-
hf download hf://Tanor/BERTicSENTPOS4/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Tanor/BERTicSENTPOS4/resolve/main/model.safetensors
442 MB
- Xet hash:
- 7fedf313f0fc6f2431a7460200dae02d53be14d79e7d8ab32c6fab964f0ae39f
- Size of remote file:
- 442 MB
- SHA256:
- 1e003214012c9281e85e2dfe4b8381f24d00b1660c2672e9b023a58ecf3b64d5
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