Text Classification
Transformers
PyTorch
Safetensors
Serbian
electra
serbian
sentiment-analysis
wordnet
sentiwordnet
lexicon-induction
Instructions to use Tanor/BERTicSENTNEG2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tanor/BERTicSENTNEG2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tanor/BERTicSENTNEG2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tanor/BERTicSENTNEG2") model = AutoModelForSequenceClassification.from_pretrained("Tanor/BERTicSENTNEG2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Tanor/BERTicSENTNEG2: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/Tanor/BERTicSENTNEG2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Tanor/BERTicSENTNEG2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Tanor/BERTicSENTNEG2/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- b410ae0b09ba99ef58e014bd1a3725fdbab68ac1b95cef0bd62e90557e947fd5
- Size of remote file:
- 443 MB
- SHA256:
- 37654a9ebeddd97595db69c419ae539f794c6cb4040cbad372ad3470cb23b099
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