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 training_args.bin from Tanor/BERTicSENTPOS4: direct link, hf CLI and curl.
- Browser
- Download file 4.41 kB
-
https://huggingface.co/Tanor/BERTicSENTPOS4/resolve/7ff98da87f90eeb2e63e6f59db0aebafc710930c/training_args.bin
- Command line
-
hf download hf://Tanor/BERTicSENTPOS4@7ff98da87f90eeb2e63e6f59db0aebafc710930c/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Tanor/BERTicSENTPOS4/resolve/7ff98da87f90eeb2e63e6f59db0aebafc710930c/training_args.bin
4.41 kB
- Xet hash:
- 0f9bb64961b5bf959460071e95b4a2baa54bfe4f55737fe0513b67cb42254320
- Size of remote file:
- 4.41 kB
- SHA256:
- 1db4a890a76ffaf479697867526475301c82d52b5fd450bec2e5cf73dd360ff1
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