Instructions to use prakhar2112/sectionHeaderBest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prakhar2112/sectionHeaderBest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prakhar2112/sectionHeaderBest")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prakhar2112/sectionHeaderBest") model = AutoModelForSequenceClassification.from_pretrained("prakhar2112/sectionHeaderBest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from prakhar2112/sectionHeaderBest: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/prakhar2112/sectionHeaderBest/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://prakhar2112/sectionHeaderBest/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/prakhar2112/sectionHeaderBest/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 81762c21e9cdf81d619fd70d64906ef95a00a97b361f065167e223828bf61d63
- Size of remote file:
- 433 MB
- SHA256:
- 32f79cd134081915feba958d106386a3b48fc49f24af7b13d9b9fd294209374e
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