Instructions to use imohammad12/GRS-complex-simple-classifier-DeBerta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use imohammad12/GRS-complex-simple-classifier-DeBerta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="imohammad12/GRS-complex-simple-classifier-DeBerta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("imohammad12/GRS-complex-simple-classifier-DeBerta") model = AutoModelForSequenceClassification.from_pretrained("imohammad12/GRS-complex-simple-classifier-DeBerta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from imohammad12/GRS-complex-simple-classifier-DeBerta: direct link, hf CLI and curl.
- Browser
- Download file 2.67 kB
-
https://huggingface.co/imohammad12/GRS-complex-simple-classifier-DeBerta/resolve/main/training_args.bin
- Command line
-
hf download hf://imohammad12/GRS-complex-simple-classifier-DeBerta/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/imohammad12/GRS-complex-simple-classifier-DeBerta/resolve/main/training_args.bin
2.67 kB
- Xet hash:
- 423ac2609acfd20196f5b924ef7ce429d67b6b30c7c9a5c095dae5e71dcf69ff
- Size of remote file:
- 2.67 kB
- SHA256:
- eff90eec8f05e59957f0477a7bffe23ea3878580071064c1bff2e0943f4c6ed8
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