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
bert-large-cased-sigir-support-refute-no-label-40-2nd-test-LR10-8-fast-1 / runs /May31_03-45-05_4e3381825ef5 /events.out.tfevents.1685505017.4e3381825ef5.2650.3
- Xet hash:
- ab3e631e6e5b3c52e1a40b305f7277bfb63be37fbcf15cfabb0cb7172b9e2b63
- Size of remote file:
- 8.35 kB
- SHA256:
- 0abf5d64349e40d8d772a5b2b676cd86a0d3584a220528d4e4b9b5308f1438fe
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.