Instructions to use Wiebke/results_flausch_classification_gbert-large_comment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wiebke/results_flausch_classification_gbert-large_comment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Wiebke/results_flausch_classification_gbert-large_comment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Wiebke/results_flausch_classification_gbert-large_comment") model = AutoModelForSequenceClassification.from_pretrained("Wiebke/results_flausch_classification_gbert-large_comment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Wiebke/results_flausch_classification_gbert-large_comment: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Wiebke/results_flausch_classification_gbert-large_comment/resolve/main/training_args.bin
- Command line
-
hf download hf://Wiebke/results_flausch_classification_gbert-large_comment/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Wiebke/results_flausch_classification_gbert-large_comment/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- d44955153bd5a58cbbd0ed867950819edb65184684ef23e3b2ed0f578d23b300
- Size of remote file:
- 5.3 kB
- SHA256:
- ff2fa9b31dfec736f57bfd615c34cf9921eeb30f1b88645e7fb65a5318ae96ae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.