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 optimizer.pt from imohammad12/GRS-complex-simple-classifier-DeBerta: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/imohammad12/GRS-complex-simple-classifier-DeBerta/resolve/main/optimizer.pt
- Command line
-
hf download hf://imohammad12/GRS-complex-simple-classifier-DeBerta/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/imohammad12/GRS-complex-simple-classifier-DeBerta/resolve/main/optimizer.pt
1.11 GB
- Xet hash:
- e00967675248350e6caf9402e5ff69027a5b14c792eb341c79dc2b95bb7caac6
- Size of remote file:
- 1.11 GB
- SHA256:
- be67aab1defbc2e674e3c598f5e055a15c3d98f3b7d6c69c1d9a847162667fd2
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