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 training_args.bin from Tanor/SRGPTSENTNEG2: direct link, hf CLI and curl.
- Browser
- Download file 4.41 kB
-
https://huggingface.co/Tanor/SRGPTSENTNEG2/resolve/f10a6e0e87c75e5c0a6bb66fa0d50ed3f75d82d8/training_args.bin
- Command line
-
hf download hf://Tanor/SRGPTSENTNEG2@f10a6e0e87c75e5c0a6bb66fa0d50ed3f75d82d8/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Tanor/SRGPTSENTNEG2/resolve/f10a6e0e87c75e5c0a6bb66fa0d50ed3f75d82d8/training_args.bin
4.41 kB
- Xet hash:
- 3aec3e23758629c1287043e46bac96fd0a832ea07ccf602066b4cf8d67069fde
- Size of remote file:
- 4.41 kB
- SHA256:
- 33c32b747d9a25a6168cdb5c81b7b5fb42e93f94463ae39bf904efe3d0a0f766
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