Instructions to use vedanta2003/ipd_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vedanta2003/ipd_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vedanta2003/ipd_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vedanta2003/ipd_model") model = AutoModelForSequenceClassification.from_pretrained("vedanta2003/ipd_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vedanta2003/ipd_model: direct link, hf CLI and curl.
- Browser
- Download file 1.33 GB
-
https://huggingface.co/vedanta2003/ipd_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://vedanta2003/ipd_model@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vedanta2003/ipd_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
1.33 GB
- Xet hash:
- 3684c54dd34bc91bba8c37bcb52459959438de4e3c1e15a54467bc6bc6991116
- Size of remote file:
- 1.33 GB
- SHA256:
- eb300a0ac9fdf3b487e43977afa1f56e201c43bc8aae3d63ee5c1e1ad214367b
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