Instructions to use NbAiLab/roberta_des_512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/roberta_des_512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLab/roberta_des_512")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLab/roberta_des_512") model = AutoModelForMaskedLM.from_pretrained("NbAiLab/roberta_des_512") - Notebooks
- Google Colab
- Kaggle
ready to train
Browse files- run_mlm_flax.py +1 -1
run_mlm_flax.py
CHANGED
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@@ -325,7 +325,7 @@ if __name__ == "__main__":
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# download the dataset.
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if data_args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir)
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if "validation" not in datasets.keys():
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datasets["validation"] = load_dataset(
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# download the dataset.
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| 326 |
if data_args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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| 328 |
+
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name, use_auth_token=data_args.auth_token, cache_dir=model_args.cache_dir)
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if "validation" not in datasets.keys():
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datasets["validation"] = load_dataset(
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