Instructions to use RonTon05/New_MTL_Full_Finetuning_OverSampling_WeightedLoss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RonTon05/New_MTL_Full_Finetuning_OverSampling_WeightedLoss with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, PhoBERTMultiTask tokenizer = AutoTokenizer.from_pretrained("RonTon05/New_MTL_Full_Finetuning_OverSampling_WeightedLoss") model = PhoBERTMultiTask.from_pretrained("RonTon05/New_MTL_Full_Finetuning_OverSampling_WeightedLoss", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from RonTon05/New_MTL_Full_Finetuning_OverSampling_WeightedLoss: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/RonTon05/New_MTL_Full_Finetuning_OverSampling_WeightedLoss/resolve/main/training_args.bin
- Command line
-
hf download hf://RonTon05/New_MTL_Full_Finetuning_OverSampling_WeightedLoss/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/RonTon05/New_MTL_Full_Finetuning_OverSampling_WeightedLoss/resolve/main/training_args.bin
5.27 kB
- Xet hash:
- fabe95053657c4a810f31514cf94452efac789a64483e306d8d4fe770742168c
- Size of remote file:
- 5.27 kB
- SHA256:
- 88cbac82f08cbdacb6a3697522bd61531581294046b9788e132079a5808bc6d9
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