Instructions to use jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-1") model = AutoModelForMaskedLM.from_pretrained("jojoUla/bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-1") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- pytorch_model.bin +1 -1
- runs/May19_05-52-48_57878465156a/1684475920.282764/events.out.tfevents.1684475920.57878465156a.7875.4 +3 -0
- runs/May19_05-52-48_57878465156a/events.out.tfevents.1684475920.57878465156a.7875.3 +3 -0
- runs/May19_05-52-48_57878465156a/events.out.tfevents.1684475937.57878465156a.7875.5 +3 -0
- training_args.bin +1 -1
pytorch_model.bin
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