Instructions to use GKLMIP/electra-khmer-small-uncased-tokenized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GKLMIP/electra-khmer-small-uncased-tokenized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="GKLMIP/electra-khmer-small-uncased-tokenized")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("GKLMIP/electra-khmer-small-uncased-tokenized") model = AutoModelForMaskedLM.from_pretrained("GKLMIP/electra-khmer-small-uncased-tokenized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from GKLMIP/electra-khmer-small-uncased-tokenized: direct link, hf CLI and curl.
- Browser
- Download file 2.35 kB
-
https://huggingface.co/GKLMIP/electra-khmer-small-uncased-tokenized/resolve/main/training_args.bin
- Command line
-
hf download hf://GKLMIP/electra-khmer-small-uncased-tokenized/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/GKLMIP/electra-khmer-small-uncased-tokenized/resolve/main/training_args.bin
2.35 kB
- Xet hash:
- d5e5d67f930453dbd32933f92de070f9a34f3ed6b2fb301e36aa66c8b6196889
- Size of remote file:
- 2.35 kB
- SHA256:
- da58c17aecd0eb4cf5b441588ff3da519ab5ace1e1415485da8a85b6e14e2eef
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