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 training_args.bin from vedanta2003/ipd_model: direct link, hf CLI and curl.
- Browser
- Download file 1.45 kB
-
https://huggingface.co/vedanta2003/ipd_model/resolve/main/training_args.bin
- Command line
-
hf download hf://vedanta2003/ipd_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/vedanta2003/ipd_model/resolve/main/training_args.bin
1.45 kB
- Xet hash:
- aff4e12fcf4bddcb8e8eda9e7bb4698c49c2c6e51388272d862c624fee85d05f
- Size of remote file:
- 1.45 kB
- SHA256:
- 1078fcc114db1f6fe40e34788123e0a2224bd0e0e2bba5d06bb07478ab754959
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