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 pytorch_model.bin from imohammad12/GRS-complex-simple-classifier-DeBerta: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/imohammad12/GRS-complex-simple-classifier-DeBerta/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://imohammad12/GRS-complex-simple-classifier-DeBerta/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/imohammad12/GRS-complex-simple-classifier-DeBerta/resolve/main/pytorch_model.bin
557 MB
- Xet hash:
- 2c668e47ca518b17eb3522a8ff987ef7a9b0728f682f90fb5c1c3b82cb8e0954
- Size of remote file:
- 557 MB
- SHA256:
- 785ae71ceee53eb56055250107f3cda0a187ce7a59fe6ab755a01a51c80b9887
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.