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
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README.md
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This model is a fine-tuned version of [jojoUla/bert-large-cased-sigir-support-refute-no-label-40](https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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### Framework versions
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This model is a fine-tuned version of [jojoUla/bert-large-cased-sigir-support-refute-no-label-40](https://huggingface.co/jojoUla/bert-large-cased-sigir-support-refute-no-label-40) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0321
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| 4.7535 | 1.0 | 1 | 1.5970 |
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| 3.3407 | 2.0 | 2 | 1.3329 |
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| 2.0305 | 3.0 | 3 | 0.8160 |
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| 1.9057 | 4.0 | 4 | 0.0579 |
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| 1.2441 | 5.0 | 5 | 0.0013 |
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| 1.8458 | 6.0 | 6 | 1.5072 |
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| 2.7407 | 7.0 | 7 | 7.4155 |
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| 0.4888 | 8.0 | 8 | 0.0002 |
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### Framework versions
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