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
Upload folder using huggingface_hub
Browse files- optimizer.pt +3 -0
- rng_state.pth +3 -0
- scaler.pt +3 -0
- scheduler.pt +3 -0
- trainer_state.json +0 -0
- training_args.bin +3 -0
optimizer.pt
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rng_state.pth
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oid sha256:17cd930da9783ca70bad4b9cdeee6a06c0acea8f34645a333c93341f487f66a3
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size 14645
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scaler.pt
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oid sha256:8d590650dab568b52b7443e44442334f754ed11ae78d6a06013aee97a040f65b
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size 1383
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scheduler.pt
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oid sha256:72577289aaa220afedfc49757bd2711c237e4fd8d9835f8f27989d2176829abd
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size 1465
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trainer_state.json
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training_args.bin
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oid sha256:6c4ff60293316edcff955a09870ee057b6ccd42844466853ead5a5c4320cdf36
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size 5777
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