Instructions to use pnichite/QAClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pnichite/QAClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pnichite/QAClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pnichite/QAClassification") model = AutoModelForSequenceClassification.from_pretrained("pnichite/QAClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pnichite/QAClassification: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/pnichite/QAClassification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://pnichite/QAClassification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pnichite/QAClassification/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 738e3f4007c18430b3ca0a5a19bfaa81998e9e8a8580291f10e1f8109e413409
- Size of remote file:
- 438 MB
- SHA256:
- 65a97709e8bc06a046f21c8bd12b27c3ef6c6d1ab7b52ccd18506a3c8e20eb11
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