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 training_args.bin from Tanor/BERTicSENTNEG2: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/Tanor/BERTicSENTNEG2/resolve/main/training_args.bin
- Command line
-
hf download hf://Tanor/BERTicSENTNEG2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Tanor/BERTicSENTNEG2/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 9460d23058a57844f31fdd67f70b5a4977e25c33c8b92ca3f024666b9452eab6
- Size of remote file:
- 4.98 kB
- SHA256:
- 5615b8e147bde439d8673fcd95d3fc6d43401464f6cd7e64a3cb7659590898a9
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