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
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Download README.md from CaseLoop/rti-formatter: direct link, hf CLI and curl.
- Browser
- Download file 1.86 kB
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https://huggingface.co/CaseLoop/rti-formatter/resolve/main/README.md
- Command line
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hf download hf://CaseLoop/rti-formatter/README.md
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curl -L -o README.md https://huggingface.co/CaseLoop/rti-formatter/resolve/main/README.md
1.86 kB
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: google/flan-t5-small | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: rti-formatter | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # rti-formatter | |
| This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.0075 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.0003 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.0701 | 1.0 | 57 | 0.0314 | | |
| | 0.0238 | 2.0 | 114 | 0.0156 | | |
| | 0.0156 | 3.0 | 171 | 0.0108 | | |
| | 0.0130 | 4.0 | 228 | 0.0107 | | |
| | 0.0126 | 5.0 | 285 | 0.0089 | | |
| | 0.0113 | 6.0 | 342 | 0.0082 | | |
| | 0.0117 | 7.0 | 399 | 0.0089 | | |
| | 0.0092 | 8.0 | 456 | 0.0078 | | |
| | 0.0102 | 9.0 | 513 | 0.0074 | | |
| | 0.0089 | 10.0 | 570 | 0.0075 | | |
| ### Framework versions | |
| - Transformers 5.15.0 | |
| - Pytorch 2.11.0+cu128 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.2 | |