Instructions to use NbAiLabArchive/test_NCC_small_pytorch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabArchive/test_NCC_small_pytorch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLabArchive/test_NCC_small_pytorch")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLabArchive/test_NCC_small_pytorch") model = AutoModelForMaskedLM.from_pretrained("NbAiLabArchive/test_NCC_small_pytorch") - Notebooks
- Google Colab
- Kaggle
small change
Browse files- __pycache__/run_mlm.cpython-38.pyc +0 -0
- run_pytorch.sh +1 -1
__pycache__/run_mlm.cpython-38.pyc
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run_pytorch.sh
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@@ -13,7 +13,7 @@ python3 ./xla_spawn.py \
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--learning_rate="6e-4" \
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--warmup_steps="10000" \
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--overwrite_output_dir \
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--num_train_epochs
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--adam_beta1="0.9" \
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--adam_beta2="0.98" \
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--logging_steps="10000" \
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--learning_rate="6e-4" \
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--warmup_steps="10000" \
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--overwrite_output_dir \
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--num_train_epochs="10" \
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--adam_beta1="0.9" \
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--adam_beta2="0.98" \
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--logging_steps="10000" \
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