Instructions to use GKLMIP/electra-khmer-base-uncased-tokenized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GKLMIP/electra-khmer-base-uncased-tokenized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="GKLMIP/electra-khmer-base-uncased-tokenized")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("GKLMIP/electra-khmer-base-uncased-tokenized") model = AutoModelForMaskedLM.from_pretrained("GKLMIP/electra-khmer-base-uncased-tokenized", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c077e9fb09786d12012d4d6f208e6bc92e083b7b6970f836c31b0f272ce2d81
- Size of remote file:
- 2.35 kB
- SHA256:
- c473f133e219e92451a5e30cc8f24c981241dcff7b6fed1443a1dbb9129b876f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.