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---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: flan_t5_base_ledgar
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. -->
# flan_t5_base_ledgar
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5092
- Accuracy: 0.8685
- F1 Macro: 0.7955
- F1 Micro: 0.8685
## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
| 1.2824 | 0.11 | 100 | 1.0243 | 0.7464 | 0.5723 | 0.7464 |
| 0.8961 | 0.21 | 200 | 0.8572 | 0.7743 | 0.6359 | 0.7743 |
| 0.8233 | 0.32 | 300 | 0.7788 | 0.7968 | 0.6756 | 0.7968 |
| 0.7722 | 0.43 | 400 | 0.7432 | 0.8014 | 0.6689 | 0.8014 |
| 0.7739 | 0.53 | 500 | 0.6933 | 0.8135 | 0.7020 | 0.8135 |
| 0.7435 | 0.64 | 600 | 0.6871 | 0.8137 | 0.7028 | 0.8137 |
| 0.6877 | 0.75 | 700 | 0.6751 | 0.8169 | 0.7012 | 0.8169 |
| 0.6968 | 0.85 | 800 | 0.6471 | 0.8225 | 0.7249 | 0.8225 |
| 0.6218 | 0.96 | 900 | 0.6366 | 0.8219 | 0.7264 | 0.8219 |
| 0.5101 | 1.07 | 1000 | 0.6130 | 0.8378 | 0.7388 | 0.8378 |
| 0.5411 | 1.17 | 1100 | 0.6088 | 0.8375 | 0.7392 | 0.8375 |
| 0.5918 | 1.28 | 1200 | 0.5864 | 0.8449 | 0.7577 | 0.8449 |
| 0.4785 | 1.39 | 1300 | 0.5917 | 0.8391 | 0.7460 | 0.8391 |
| 0.4372 | 1.49 | 1400 | 0.5790 | 0.8409 | 0.7563 | 0.8409 |
| 0.5022 | 1.6 | 1500 | 0.5868 | 0.8437 | 0.7524 | 0.8437 |
| 0.5391 | 1.71 | 1600 | 0.5613 | 0.8447 | 0.7520 | 0.8447 |
| 0.4971 | 1.81 | 1700 | 0.5466 | 0.8545 | 0.7702 | 0.8545 |
| 0.4686 | 1.92 | 1800 | 0.5260 | 0.8566 | 0.7774 | 0.8566 |
| 0.349 | 2.03 | 1900 | 0.5416 | 0.8565 | 0.7721 | 0.8565 |
| 0.3216 | 2.13 | 2000 | 0.5441 | 0.8565 | 0.7763 | 0.8565 |
| 0.3531 | 2.24 | 2100 | 0.5444 | 0.8553 | 0.7753 | 0.8553 |
| 0.3276 | 2.35 | 2200 | 0.5380 | 0.8604 | 0.7796 | 0.8604 |
| 0.3372 | 2.45 | 2300 | 0.5231 | 0.8634 | 0.7831 | 0.8634 |
| 0.3227 | 2.56 | 2400 | 0.5210 | 0.8651 | 0.7872 | 0.8651 |
| 0.2987 | 2.67 | 2500 | 0.5188 | 0.8665 | 0.7910 | 0.8665 |
| 0.3354 | 2.77 | 2600 | 0.5150 | 0.8666 | 0.7931 | 0.8666 |
| 0.3103 | 2.88 | 2700 | 0.5103 | 0.8681 | 0.7942 | 0.8681 |
| 0.3248 | 2.99 | 2800 | 0.5092 | 0.8685 | 0.7955 | 0.8685 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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