Instructions to use acul3/roberta-base-indo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use acul3/roberta-base-indo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="acul3/roberta-base-indo")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("acul3/roberta-base-indo") model = AutoModelForMaskedLM.from_pretrained("acul3/roberta-base-indo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
add requirements
Browse files- requirements.txt +4 -0
requirements.txt
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flax
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torch
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tensorflow
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transformers
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datasets
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wandb
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jax[tpu]>=0.2.16 -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
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flax
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joblib
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zstandard
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torch
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tensorflow
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tensorboard
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transformers
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datasets
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wandb
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