Instructions to use harish/ENAllE5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harish/ENAllE5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="harish/ENAllE5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("harish/ENAllE5") model = AutoModelForMaskedLM.from_pretrained("harish/ENAllE5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from harish/ENAllE5: direct link, hf CLI and curl.
- Browser
- Download file 2.14 kB
-
https://huggingface.co/harish/ENAllE5/resolve/main/training_args.bin
- Command line
-
hf download hf://harish/ENAllE5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/harish/ENAllE5/resolve/main/training_args.bin
2.14 kB
- Xet hash:
- 19dc6781095b64fa3668250cbcd67ebdfb4a9ae18df99212c47aef370c728d36
- Size of remote file:
- 2.14 kB
- SHA256:
- d33fec3b5bb0cb5b5bfd6b03b750cd6d909d74fec8eb053af21ff458455ba1ef
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