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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google/flan-t5-small |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: check-amount-deverbalizer-flan-t5-small |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# check-amount-deverbalizer-flan-t5-small |
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This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0685 |
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- Parse Rate: 1.0 |
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- Dollar Accuracy: 0.9298 |
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- Cents Accuracy: 0.9991 |
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- Digit Count Accuracy: 1.0 |
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- Perfect Match: 0.9296 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Parse Rate | Dollar Accuracy | Cents Accuracy | Digit Count Accuracy | Perfect Match | |
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|:-------------:|:------:|:----:|:---------------:|:----------:|:---------------:|:--------------:|:--------------------:|:-------------:| |
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| 1.0914 | 0.2128 | 200 | 0.7008 | 0.9987 | 0.4598 | 0.8981 | 0.36 | 0.1972 | |
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| 0.6574 | 0.4255 | 400 | 0.3127 | 0.9999 | 0.6983 | 0.9889 | 0.8008 | 0.6573 | |
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| 0.4343 | 0.6383 | 600 | 0.1917 | 1.0 | 0.8006 | 0.9978 | 0.9763 | 0.7880 | |
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| 0.3524 | 0.8511 | 800 | 0.1484 | 1.0 | 0.8458 | 0.9979 | 0.9923 | 0.8407 | |
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| 0.2875 | 1.0638 | 1000 | 0.1249 | 1.0 | 0.8716 | 0.9982 | 0.9950 | 0.8682 | |
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| 0.2608 | 1.2766 | 1200 | 0.1089 | 1.0 | 0.8845 | 0.9985 | 0.9983 | 0.8827 | |
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| 0.2545 | 1.4894 | 1400 | 0.0974 | 1.0 | 0.8915 | 0.9981 | 0.9998 | 0.8909 | |
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| 0.2112 | 1.7021 | 1600 | 0.0895 | 1.0 | 0.8998 | 0.9988 | 1.0 | 0.8995 | |
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| 0.2074 | 1.9149 | 1800 | 0.0800 | 1.0 | 0.9128 | 0.9985 | 1.0 | 0.9124 | |
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| 0.1984 | 2.1277 | 2000 | 0.0766 | 1.0 | 0.9166 | 0.9987 | 1.0 | 0.9165 | |
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| 0.1837 | 2.3404 | 2200 | 0.0725 | 1.0 | 0.9246 | 0.9986 | 1.0 | 0.9245 | |
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| 0.1835 | 2.5532 | 2400 | 0.0705 | 1.0 | 0.9263 | 0.9991 | 1.0 | 0.9261 | |
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| 0.1743 | 2.7660 | 2600 | 0.0686 | 1.0 | 0.9313 | 0.9991 | 1.0 | 0.9311 | |
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| 0.1871 | 2.9787 | 2800 | 0.0685 | 1.0 | 0.9298 | 0.9991 | 1.0 | 0.9296 | |
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### Framework versions |
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- Transformers 4.48.2 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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