Instructions to use geshijoker/distilroberta_eli5_mlm_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use geshijoker/distilroberta_eli5_mlm_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="geshijoker/distilroberta_eli5_mlm_model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("geshijoker/distilroberta_eli5_mlm_model") model = AutoModelForMaskedLM.from_pretrained("geshijoker/distilroberta_eli5_mlm_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the eli5_category dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.
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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 [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the eli5_category dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.0125
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| 2.2867 | 1.0 | 663 | 2.0541 |
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| 2.2023 | 2.0 | 1326 | 2.0364 |
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| 2.1813 | 3.0 | 1989 | 1.9924 |
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### Framework versions
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runs/Aug30_15-25-30_cajal/events.out.tfevents.1725056731.cajal.1806130.0
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