Instructions to use JackBAI/roberta-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JackBAI/roberta-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JackBAI/roberta-medium")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("JackBAI/roberta-medium") model = AutoModelForMaskedLM.from_pretrained("JackBAI/roberta-medium") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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| ADAM_BETA2 | 0.98 |
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| ADAM_WEIGHT_DECAY | 0.01 |
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| PEAK_LR | 1e-3 |
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| ADAM_BETA2 | 0.98 |
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| ADAM_WEIGHT_DECAY | 0.01 |
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| PEAK_LR | 1e-3 |
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Evaluation Results on GLUE after pretraining (30k steps):
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| Task | Score |
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| CoLA-MattCorr | 20.0 |
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| MRPC-F1 | 83.6 |
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| STS-B-Pearson | 82.7 |
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| SST-2-Acc | 89.7 |
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| QQP-F1 | 89.0 |
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| MNLI-m | 79.7 |
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| MNLI-mm | 80.1 |
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| QNLI-Acc | 89.3 |
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| WNLI-Acc | 31.0 |
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| RTE-Acc | 57.4 |
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Evaluation Scores Curve (AVG of scores) during pretraining:
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