Text Classification
Transformers
PyTorch
Safetensors
Enawené-Nawé
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use GRPUI/sgugit-model-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GRPUI/sgugit-model-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GRPUI/sgugit-model-v3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GRPUI/sgugit-model-v3") model = AutoModelForSequenceClassification.from_pretrained("GRPUI/sgugit-model-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from GRPUI/sgugit-model-v3: direct link, hf CLI and curl.
- Browser
- Download file 2.56 MB
-
https://huggingface.co/GRPUI/sgugit-model-v3/resolve/main/tokenizer.json
- Command line
-
hf download hf://GRPUI/sgugit-model-v3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/GRPUI/sgugit-model-v3/resolve/main/tokenizer.json
2.56 MB
- Xet hash:
- e17e4fbc2b47b280852c9602fca1ab201e8cadf6258ca6c447a30cf8518360ba
- Size of remote file:
- 2.56 MB
- SHA256:
- b048853498a8f0d259e0b5123f05de34f6a2bcbdd56004a4c9da8f37a7cea60a
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