Instructions to use fagner/kaggle-detect-generated-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fagner/kaggle-detect-generated-text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fagner/kaggle-detect-generated-text")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fagner/kaggle-detect-generated-text") model = AutoModelForSequenceClassification.from_pretrained("fagner/kaggle-detect-generated-text", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from fagner/kaggle-detect-generated-text: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/fagner/kaggle-detect-generated-text/resolve/main/training_args.bin
- Command line
-
hf download hf://fagner/kaggle-detect-generated-text/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/fagner/kaggle-detect-generated-text/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- bad5459d72adc2470d78ed47dab6d6788fac8b3f4a3db577cfe9da7ce88f9c09
- Size of remote file:
- 4.09 kB
- SHA256:
- 73434371f5894e95037ceb51d1d67740940bf57dc3e001e9449a0c6b861e5318
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.