Instructions to use Simonom/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Simonom/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Simonom/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Simonom/results") model = AutoModelForSequenceClassification.from_pretrained("Simonom/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Simonom/results: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/Simonom/results/resolve/main/training_args.bin
- Command line
-
hf download hf://Simonom/results/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Simonom/results/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 1af76bdc0c3aa9cd1067b7b659aa8a19349357e76d13eebe9a642774c3b893b9
- Size of remote file:
- 5.3 kB
- SHA256:
- ce1ab98cd60a6bab127bf238a5d21c4fbcb256a89435ed4d8bb5e80d3bbbf680
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.