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
print sample
Browse files- run_mlm_flax_stream.py +1 -0
run_mlm_flax_stream.py
CHANGED
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@@ -287,6 +287,7 @@ def advance_iter_and_group_samples(train_iterator, num_samples, max_seq_length):
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i += len(tokenized_samples["input_ids"])
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# concatenate tokenized samples to list
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samples = {
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k: samples[k] + tokenized_samples[k] for k in ["input_ids", "attention_mask", "special_tokens_mask"]
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}
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i += len(tokenized_samples["input_ids"])
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# concatenate tokenized samples to list
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+
print('sample',tokenized_samples)
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samples = {
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k: samples[k] + tokenized_samples[k] for k in ["input_ids", "attention_mask", "special_tokens_mask"]
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}
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