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- run_mlm_flax_stream.py +0 -1
run_mlm_flax_stream.py
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@@ -487,7 +487,6 @@ if __name__ == "__main__":
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def tokenize_function(examples):
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return tokenizer(
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examples[data_args.text_column_name],
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max_length=512,
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return_special_tokens_mask=True
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)
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def tokenize_function(examples):
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return tokenizer(
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examples[data_args.text_column_name],
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return_special_tokens_mask=True
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)
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