Instructions to use pere/roberta-debug-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pere/roberta-debug-32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pere/roberta-debug-32")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pere/roberta-debug-32") model = AutoModelForMaskedLM.from_pretrained("pere/roberta-debug-32") - Notebooks
- Google Colab
- Kaggle
Saving weights and logs of step 25001
Browse files- flax_model.msgpack +3 -0
flax_model.msgpack
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version https://git-lfs.github.com/spec/v1
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oid sha256:7cbf50a4aec490950e32a02fadebb2d8207cdd804579106d514b74443d9149f5
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size 1113187999
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