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 special_tokens_map.json from Tanor/SRGPTSENTPOS2: direct link, hf CLI and curl.
- Browser
- Download file 99 Bytes
-
https://huggingface.co/Tanor/SRGPTSENTPOS2/resolve/0d00053f8556746e5989a0edec00d10eab63fd1c/special_tokens_map.json
- Command line
-
hf download hf://Tanor/SRGPTSENTPOS2@0d00053f8556746e5989a0edec00d10eab63fd1c/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/Tanor/SRGPTSENTPOS2/resolve/0d00053f8556746e5989a0edec00d10eab63fd1c/special_tokens_map.json
99 Bytes
| { | |
| "bos_token": "<|endoftext|>", | |
| "eos_token": "<|endoftext|>", | |
| "unk_token": "<|endoftext|>" | |
| } | |