Instructions to use CaseLoop/rti-formatter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CaseLoop/rti-formatter with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("CaseLoop/rti-formatter") model = AutoModelForSeq2SeqLM.from_pretrained("CaseLoop/rti-formatter", 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 [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
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It achieves the following results on the evaluation set:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| 0.0102 | 9.0 | 513 | 0.0074 |
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### Framework versions
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This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0075
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| 0.0701 | 1.0 | 57 | 0.0314 |
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| 0.0238 | 2.0 | 114 | 0.0156 |
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| 0.0156 | 3.0 | 171 | 0.0108 |
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| 0.0130 | 4.0 | 228 | 0.0107 |
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| 0.0126 | 5.0 | 285 | 0.0089 |
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| 0.0113 | 6.0 | 342 | 0.0082 |
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| 0.0117 | 7.0 | 399 | 0.0089 |
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| 0.0092 | 8.0 | 456 | 0.0078 |
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| 0.0102 | 9.0 | 513 | 0.0074 |
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| 0.0089 | 10.0 | 570 | 0.0075 |
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### Framework versions
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