Text Classification
Transformers
PyTorch
Serbian
gpt2
serbian
sentiment-analysis
wordnet
sentiwordnet
lexicon-induction
Instructions to use Tanor/SRGPTSENTNEG0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tanor/SRGPTSENTNEG0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tanor/SRGPTSENTNEG0")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tanor/SRGPTSENTNEG0") model = AutoModelForSequenceClassification.from_pretrained("Tanor/SRGPTSENTNEG0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Tanor/SRGPTSENTNEG0: direct link, hf CLI and curl.
- Browser
- Download file 261 Bytes
-
https://huggingface.co/Tanor/SRGPTSENTNEG0/resolve/4056b8743aed1b99564d245607728880c73fd1b7/tokenizer_config.json
- Command line
-
hf download hf://Tanor/SRGPTSENTNEG0@4056b8743aed1b99564d245607728880c73fd1b7/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Tanor/SRGPTSENTNEG0/resolve/4056b8743aed1b99564d245607728880c73fd1b7/tokenizer_config.json
261 Bytes
| { | |
| "add_prefix_space": false, | |
| "bos_token": "<|endoftext|>", | |
| "clean_up_tokenization_spaces": true, | |
| "eos_token": "<|endoftext|>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "tokenizer_class": "GPT2Tokenizer", | |
| "unk_token": "<|endoftext|>" | |
| } | |