Instructions to use MistahCase/distilroberta-base-testingSB-testingSB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MistahCase/distilroberta-base-testingSB-testingSB with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="MistahCase/distilroberta-base-testingSB-testingSB")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("MistahCase/distilroberta-base-testingSB-testingSB") model = AutoModelForMaskedLM.from_pretrained("MistahCase/distilroberta-base-testingSB-testingSB") - Notebooks
- Google Colab
- Kaggle
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update model card README.md
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README.md
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This model is a fine-tuned version of [MistahCase/distilroberta-base-testingSB](https://huggingface.co/MistahCase/distilroberta-base-testingSB) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.
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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 [MistahCase/distilroberta-base-testingSB](https://huggingface.co/MistahCase/distilroberta-base-testingSB) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0403
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| 0.9885 | 1.0 | 1461 | 1.0564 |
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| 0.9963 | 2.0 | 2922 | 1.0278 |
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| 0.9903 | 3.0 | 4383 | 1.0403 |
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### Framework versions
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