Text Classification
Transformers
PyTorch
Safetensors
Serbian
electra
serbian
sentiment-analysis
wordnet
sentiwordnet
lexicon-induction
Instructions to use Tanor/BERTicSENTPOS6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tanor/BERTicSENTPOS6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tanor/BERTicSENTPOS6")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tanor/BERTicSENTPOS6") model = AutoModelForSequenceClassification.from_pretrained("Tanor/BERTicSENTPOS6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Tanor/BERTicSENTPOS6: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/Tanor/BERTicSENTPOS6/resolve/820c6e9b82770a6c0bc7a62c0af85f09043b43ac/pytorch_model.bin
- Command line
-
hf download hf://Tanor/BERTicSENTPOS6@820c6e9b82770a6c0bc7a62c0af85f09043b43ac/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Tanor/BERTicSENTPOS6/resolve/820c6e9b82770a6c0bc7a62c0af85f09043b43ac/pytorch_model.bin
443 MB
- Xet hash:
- f4d2c87693d6e9049479897fe671afb3966262f56413cda50162b4a7ac1bb631
- Size of remote file:
- 443 MB
- SHA256:
- 543f01e00bc15011e394f6fb7006d5139abdf000a57d4c4f7e3b9395fbfdf6f3
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