---
library_name: transformers
license: mit
base_model: ai-forever/sage-mt5-large
tags:
- generated_from_trainer
model-index:
- name: MT5_large_A_art
  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. -->

# MT5_large_A_art

This model is a fine-tuned version of [ai-forever/sage-mt5-large](https://huggingface.co/ai-forever/sage-mt5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2006

## 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: 3.83229e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 3300

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9979        | 0.0303 | 100  | 0.2649          |
| 0.5176        | 0.0606 | 200  | 0.2170          |
| 0.3916        | 0.0909 | 300  | 0.1973          |
| 0.3356        | 0.1212 | 400  | 0.1928          |
| 0.2993        | 0.1515 | 500  | 0.1937          |
| 0.2783        | 0.1818 | 600  | 0.1919          |
| 0.268         | 0.2121 | 700  | 0.1907          |
| 0.2697        | 0.2424 | 800  | 0.1914          |
| 0.2491        | 0.2726 | 900  | 0.1901          |
| 0.2488        | 0.3029 | 1000 | 0.1888          |
| 0.238         | 0.3332 | 1100 | 0.1861          |
| 0.2414        | 0.3635 | 1200 | 0.1872          |
| 0.2378        | 0.3938 | 1300 | 0.1857          |
| 0.2286        | 0.4241 | 1400 | 0.1842          |
| 0.2201        | 0.4544 | 1500 | 0.1849          |
| 0.2217        | 0.4847 | 1600 | 0.1845          |
| 0.2195        | 0.5150 | 1700 | 0.1835          |
| 0.2137        | 0.5453 | 1800 | 0.1818          |
| 0.2147        | 0.5756 | 1900 | 0.1822          |
| 0.2246        | 0.6059 | 2000 | 0.1806          |
| 0.2151        | 0.6362 | 2100 | 0.1806          |
| 0.2179        | 0.6665 | 2200 | 0.1805          |
| 0.2219        | 0.6968 | 2300 | 0.1806          |
| 0.2126        | 0.7271 | 2400 | 0.1808          |
| 0.2149        | 0.7573 | 2500 | 0.1802          |
| 0.2137        | 0.7876 | 2600 | 0.1806          |
| 0.2146        | 0.8179 | 2700 | 0.1803          |
| 0.2078        | 0.8482 | 2800 | 0.1803          |
| 0.2084        | 0.8785 | 2900 | 0.1805          |
| 0.2153        | 0.9088 | 3000 | 0.1801          |
| 0.2134        | 0.9391 | 3100 | 0.1799          |
| 0.2169        | 0.9694 | 3200 | 0.1799          |
| 0.2181        | 0.9997 | 3300 | 0.1799          |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.1
- Tokenizers 0.21.0