---
tags:
- generated_from_trainer
model-index:
- name: led-large
  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. -->

# led-large

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1850

## 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: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- lr_scheduler_warmup_steps: 500
- training_steps: 20000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1479        | 0.11  | 500   | 0.1901          |
| 0.1442        | 0.22  | 1000  | 0.1917          |
| 0.1466        | 0.33  | 1500  | 0.1959          |
| 0.1447        | 0.45  | 2000  | 0.1918          |
| 0.1633        | 0.56  | 2500  | 0.1874          |
| 0.171         | 0.67  | 3000  | 0.1849          |
| 0.1662        | 0.78  | 3500  | 0.1843          |
| 0.1743        | 0.89  | 4000  | 0.1837          |
| 0.1492        | 1.0   | 4500  | 0.1842          |
| 0.1515        | 1.11  | 5000  | 0.1849          |
| 0.1497        | 1.23  | 5500  | 0.1840          |
| 0.1515        | 1.34  | 6000  | 0.1839          |
| 0.1482        | 1.45  | 6500  | 0.1841          |
| 0.145         | 1.56  | 7000  | 0.1849          |
| 0.1467        | 1.67  | 7500  | 0.1824          |
| 0.1509        | 1.78  | 8000  | 0.1809          |
| 0.15          | 1.89  | 8500  | 0.1832          |
| 0.1383        | 2.01  | 9000  | 0.1831          |
| 0.1331        | 2.12  | 9500  | 0.1820          |
| 0.1406        | 2.23  | 10000 | 0.1830          |
| 0.1362        | 2.34  | 10500 | 0.1844          |
| 0.1373        | 2.45  | 11000 | 0.1836          |
| 0.1269        | 2.56  | 11500 | 0.1842          |
| 0.1362        | 2.67  | 12000 | 0.1819          |
| 0.14          | 2.79  | 12500 | 0.1832          |
| 0.1319        | 2.9   | 13000 | 0.1837          |
| 0.1304        | 3.01  | 13500 | 0.1845          |
| 0.1278        | 3.12  | 14000 | 0.1844          |
| 0.1235        | 3.23  | 14500 | 0.1832          |
| 0.1293        | 3.34  | 15000 | 0.1855          |
| 0.1302        | 3.45  | 15500 | 0.1836          |
| 0.1285        | 3.57  | 16000 | 0.1860          |
| 0.1274        | 3.68  | 16500 | 0.1860          |
| 0.1261        | 3.79  | 17000 | 0.1854          |
| 0.1304        | 3.9   | 17500 | 0.1859          |
| 0.1223        | 4.01  | 18000 | 0.1862          |
| 0.1235        | 4.12  | 18500 | 0.1849          |
| 0.1286        | 4.23  | 19000 | 0.1858          |
| 0.1186        | 4.35  | 19500 | 0.1856          |
| 0.1293        | 4.46  | 20000 | 0.1850          |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1