Instructions to use seanghay/albert-khmer-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seanghay/albert-khmer-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="seanghay/albert-khmer-small")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("seanghay/albert-khmer-small") model = AutoModelForMaskedLM.from_pretrained("seanghay/albert-khmer-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cc9442a1b4053ae5b24e6d042393433b481de4c8e28da2c373d91b82c2eb34c2
- Size of remote file:
- 677 kB
- SHA256:
- 069733a699002399db1385d88d56d89557f459958b136063cc84a44cc18ea8f7
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