Instructions to use hamzaMM/questionClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hamzaMM/questionClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hamzaMM/questionClassifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hamzaMM/questionClassifier") model = AutoModelForSequenceClassification.from_pretrained("hamzaMM/questionClassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hamzaMM/questionClassifier: direct link, hf CLI and curl.
- Browser
- Download file 44.7 MB
-
https://huggingface.co/hamzaMM/questionClassifier/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://hamzaMM/questionClassifier@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hamzaMM/questionClassifier/resolve/refs%2Fpr%2F2/pytorch_model.bin
44.7 MB
- Xet hash:
- e01e2dcf55ba81537ce69fdd8b3556cc9f969f484bd182c681b5271c0a4b74b4
- Size of remote file:
- 44.7 MB
- SHA256:
- 260725599705b3d3a519bf6d5ce957ae21762d09c6c02e0d8d529f0c2ddfb7c4
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