Instructions to use TuringGame/gpt2-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TuringGame/gpt2-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TuringGame/gpt2-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TuringGame/gpt2-classifier") model = AutoModelForSequenceClassification.from_pretrained("TuringGame/gpt2-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from TuringGame/gpt2-classifier: direct link, hf CLI and curl.
- Browser
- Download file 498 MB
-
https://huggingface.co/TuringGame/gpt2-classifier/resolve/main/model.safetensors
- Command line
-
hf download hf://TuringGame/gpt2-classifier/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/TuringGame/gpt2-classifier/resolve/main/model.safetensors
498 MB
- Xet hash:
- 85280cc91e814ad3d8ce61558147d2563633b2d76b0d50aa2a1334a7073b5c9b
- Size of remote file:
- 498 MB
- SHA256:
- d4d51e9a8234810f10418262ebbc7c9f177f1971bece866672d77ead18995b6a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.