Instructions to use MinervaBotTeam/Question_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MinervaBotTeam/Question_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MinervaBotTeam/Question_Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MinervaBotTeam/Question_Classification") model = AutoModelForSequenceClassification.from_pretrained("MinervaBotTeam/Question_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from MinervaBotTeam/Question_Classification: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/MinervaBotTeam/Question_Classification/resolve/main/model.safetensors
- Command line
-
hf download hf://MinervaBotTeam/Question_Classification/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/MinervaBotTeam/Question_Classification/resolve/main/model.safetensors
268 MB
- Xet hash:
- 5f62714c0f77c5f3541599dc6ef84417b5201152ea7350fa24b92f0507fbaf73
- Size of remote file:
- 268 MB
- SHA256:
- 617451b28a8937895878e77cf982a91d4ac606e95089da930d1e1a1a36556645
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.