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:
# 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
rti-formatter
This model is a fine-tuned version of 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
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Model tree for CaseLoop/rti-formatter
Base model
google/flan-t5-small