Text Classification
Transformers
PyTorch
Serbian
gpt2
serbian
sentiment-analysis
wordnet
sentiwordnet
lexicon-induction
Instructions to use Tanor/SRGPTSENTNEG2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tanor/SRGPTSENTNEG2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tanor/SRGPTSENTNEG2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tanor/SRGPTSENTNEG2") model = AutoModelForSequenceClassification.from_pretrained("Tanor/SRGPTSENTNEG2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Tanor/SRGPTSENTNEG2: direct link, hf CLI and curl.
- Browser
- Download file 3.09 GB
-
https://huggingface.co/Tanor/SRGPTSENTNEG2/resolve/f10a6e0e87c75e5c0a6bb66fa0d50ed3f75d82d8/pytorch_model.bin
- Command line
-
hf download hf://Tanor/SRGPTSENTNEG2@f10a6e0e87c75e5c0a6bb66fa0d50ed3f75d82d8/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Tanor/SRGPTSENTNEG2/resolve/f10a6e0e87c75e5c0a6bb66fa0d50ed3f75d82d8/pytorch_model.bin
3.09 GB
- Xet hash:
- 3c006cfbd710c318e76f38cc3f9c618565f51f142918271dbf60e8e3f9bc7463
- Size of remote file:
- 3.09 GB
- SHA256:
- eb65b4121418e203ee8f14e433be946c4cde671b18cab91916932e8cb538c912
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