Text Classification
Transformers
PyTorch
Serbian
gpt2
serbian
sentiment-analysis
wordnet
sentiwordnet
lexicon-induction
Instructions to use Tanor/SRGPTSENTPOS2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tanor/SRGPTSENTPOS2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tanor/SRGPTSENTPOS2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tanor/SRGPTSENTPOS2") model = AutoModelForSequenceClassification.from_pretrained("Tanor/SRGPTSENTPOS2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Tanor/SRGPTSENTPOS2: direct link, hf CLI and curl.
- Browser
- Download file 3.09 GB
-
https://huggingface.co/Tanor/SRGPTSENTPOS2/resolve/5e0b3db4f021bd34ffb4099be6de2681b73643b2/pytorch_model.bin
- Command line
-
hf download hf://Tanor/SRGPTSENTPOS2@5e0b3db4f021bd34ffb4099be6de2681b73643b2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Tanor/SRGPTSENTPOS2/resolve/5e0b3db4f021bd34ffb4099be6de2681b73643b2/pytorch_model.bin
3.09 GB
- Xet hash:
- dcc0cd105b8b8e3159ab04e3b46270a3acf4d4e3d8eaebdeee22b594cc817cae
- Size of remote file:
- 3.09 GB
- SHA256:
- 60494a0571d1a88ed41e04c5801881f24c24613e78bd77648486f1b511d1bc65
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