---
library_name: transformers
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
model-index:
- name: check-amount-deverbalizer-flan-t5-small
  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. -->

# check-amount-deverbalizer-flan-t5-small

This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0685
- Parse Rate: 1.0
- Dollar Accuracy: 0.9298
- Cents Accuracy: 0.9991
- Digit Count Accuracy: 1.0
- Perfect Match: 0.9296

## 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: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Parse Rate | Dollar Accuracy | Cents Accuracy | Digit Count Accuracy | Perfect Match |
|:-------------:|:------:|:----:|:---------------:|:----------:|:---------------:|:--------------:|:--------------------:|:-------------:|
| 1.0914        | 0.2128 | 200  | 0.7008          | 0.9987     | 0.4598          | 0.8981         | 0.36                 | 0.1972        |
| 0.6574        | 0.4255 | 400  | 0.3127          | 0.9999     | 0.6983          | 0.9889         | 0.8008               | 0.6573        |
| 0.4343        | 0.6383 | 600  | 0.1917          | 1.0        | 0.8006          | 0.9978         | 0.9763               | 0.7880        |
| 0.3524        | 0.8511 | 800  | 0.1484          | 1.0        | 0.8458          | 0.9979         | 0.9923               | 0.8407        |
| 0.2875        | 1.0638 | 1000 | 0.1249          | 1.0        | 0.8716          | 0.9982         | 0.9950               | 0.8682        |
| 0.2608        | 1.2766 | 1200 | 0.1089          | 1.0        | 0.8845          | 0.9985         | 0.9983               | 0.8827        |
| 0.2545        | 1.4894 | 1400 | 0.0974          | 1.0        | 0.8915          | 0.9981         | 0.9998               | 0.8909        |
| 0.2112        | 1.7021 | 1600 | 0.0895          | 1.0        | 0.8998          | 0.9988         | 1.0                  | 0.8995        |
| 0.2074        | 1.9149 | 1800 | 0.0800          | 1.0        | 0.9128          | 0.9985         | 1.0                  | 0.9124        |
| 0.1984        | 2.1277 | 2000 | 0.0766          | 1.0        | 0.9166          | 0.9987         | 1.0                  | 0.9165        |
| 0.1837        | 2.3404 | 2200 | 0.0725          | 1.0        | 0.9246          | 0.9986         | 1.0                  | 0.9245        |
| 0.1835        | 2.5532 | 2400 | 0.0705          | 1.0        | 0.9263          | 0.9991         | 1.0                  | 0.9261        |
| 0.1743        | 2.7660 | 2600 | 0.0686          | 1.0        | 0.9313          | 0.9991         | 1.0                  | 0.9311        |
| 0.1871        | 2.9787 | 2800 | 0.0685          | 1.0        | 0.9298          | 0.9991         | 1.0                  | 0.9296        |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0