Instructions to use joyebright/EAMT2023-RU-EN-DAG2-NOTAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joyebright/EAMT2023-RU-EN-DAG2-NOTAG with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("joyebright/EAMT2023-RU-EN-DAG2-NOTAG", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from joyebright/EAMT2023-RU-EN-DAG2-NOTAG: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/joyebright/EAMT2023-RU-EN-DAG2-NOTAG/resolve/main/tokenizer.json
- Command line
-
hf download hf://joyebright/EAMT2023-RU-EN-DAG2-NOTAG/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/joyebright/EAMT2023-RU-EN-DAG2-NOTAG/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- acdf4c4e28b9d330c4e5bd5dbddaaecc7e26ca2f5660b142ca0ce8aac304ded5
- Size of remote file:
- 17.1 MB
- SHA256:
- d979aa92e8e501bf8b22c98026810c641f263dda8c74f4e4681b9c800b94f4fe
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