Instructions to use EndLessTime/fine_tuned_squad_callback10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EndLessTime/fine_tuned_squad_callback10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EndLessTime/fine_tuned_squad_callback10")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EndLessTime/fine_tuned_squad_callback10") model = AutoModelForSequenceClassification.from_pretrained("EndLessTime/fine_tuned_squad_callback10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from EndLessTime/fine_tuned_squad_callback10: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/EndLessTime/fine_tuned_squad_callback10/resolve/main/training_args.bin
- Command line
-
hf download hf://EndLessTime/fine_tuned_squad_callback10/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/EndLessTime/fine_tuned_squad_callback10/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 688ca23679475fb5d997a8820af180ffbb38f18428c0270820f70acf66f91521
- Size of remote file:
- 5.3 kB
- SHA256:
- 5c0033a88dcb07e0ebbaa627548f8ea1c496a3a49971026387d7c6ddb4acfdfa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.