Instructions to use Ik45/mt-science-en-id-finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ik45/mt-science-en-id-finetuning with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("translation", model="Ik45/mt-science-en-id-finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Ik45/mt-science-en-id-finetuning") model = AutoModelForSeq2SeqLM.from_pretrained("Ik45/mt-science-en-id-finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Ik45/mt-science-en-id-finetuning: direct link, hf CLI and curl.
- Browser
- Download file 5.33 kB
-
https://huggingface.co/Ik45/mt-science-en-id-finetuning/resolve/main/training_args.bin
- Command line
-
hf download hf://Ik45/mt-science-en-id-finetuning/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ik45/mt-science-en-id-finetuning/resolve/main/training_args.bin
5.33 kB
- Xet hash:
- 5824fc7d12486bada318ebb18cbd14bd941e354d2f265dd5e5f85ce1c8960bcf
- Size of remote file:
- 5.33 kB
- SHA256:
- 33453928e9724ec36bbe9eea14840bd2e9cfbbde038670d06117d334d2909b33
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