Instructions to use Nada81/FineTunedModelTest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nada81/FineTunedModelTest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nada81/FineTunedModelTest")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Nada81/FineTunedModelTest") model = AutoModelForSequenceClassification.from_pretrained("Nada81/FineTunedModelTest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Nada81/FineTunedModelTest: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/Nada81/FineTunedModelTest/resolve/main/training_args.bin
- Command line
-
hf download hf://Nada81/FineTunedModelTest/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Nada81/FineTunedModelTest/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 7929101c7c8a608eaa67c0494c263a55c66e576c9eeae0b68e2fb6925f6b97ed
- Size of remote file:
- 4.92 kB
- SHA256:
- 2e08ace8f2cfc167e93a73026935d4c62a78878a2b8825ac259005bb9f0be67d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.