Instructions to use jackmedda/google-t5-t5-base_finetuned_augmented_augmented_deepseek with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jackmedda/google-t5-t5-base_finetuned_augmented_augmented_deepseek with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jackmedda/google-t5-t5-base_finetuned_augmented_augmented_deepseek")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jackmedda/google-t5-t5-base_finetuned_augmented_augmented_deepseek") model = AutoModelForSequenceClassification.from_pretrained("jackmedda/google-t5-t5-base_finetuned_augmented_augmented_deepseek", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jackmedda/google-t5-t5-base_finetuned_augmented_augmented_deepseek: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/jackmedda/google-t5-t5-base_finetuned_augmented_augmented_deepseek/resolve/main/training_args.bin
- Command line
-
hf download hf://jackmedda/google-t5-t5-base_finetuned_augmented_augmented_deepseek/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jackmedda/google-t5-t5-base_finetuned_augmented_augmented_deepseek/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- 5eee3ebead98d8143286b0a28c778d55222d556fc9408bbc208fa598ac8cfeb6
- Size of remote file:
- 5.5 kB
- SHA256:
- 0fe366048cf6430ab918fa34a767cff3bc23ccb66123edf9061f4cd7c5353ab0
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