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 pytorch_model.bin from Tanor/BERTicSENTPOS4: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/Tanor/BERTicSENTPOS4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Tanor/BERTicSENTPOS4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Tanor/BERTicSENTPOS4/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 62ee0d38943c6ad8b6e5a8fef5d81ea7740cc68ea1a58e4c63b4fbd81676a494
- Size of remote file:
- 443 MB
- SHA256:
- c04b0cf589124a0c502481443133bc5b7ac8f995b67ee6a5c1df06363e5ede54
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