Instructions to use GKLMIP/bert-khmer-small-uncased-tokenized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GKLMIP/bert-khmer-small-uncased-tokenized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="GKLMIP/bert-khmer-small-uncased-tokenized")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("GKLMIP/bert-khmer-small-uncased-tokenized") model = AutoModelForMaskedLM.from_pretrained("GKLMIP/bert-khmer-small-uncased-tokenized") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22d76a88ef817b8436f6a112d823b191d02499cc954e657eeaef4542dfa2c341
- Size of remote file:
- 2.35 kB
- SHA256:
- 6fa051388f1cbaa76ccde8ca49ceaa24eb6b3446350c6c61240db5589e0d275c
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