---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: convnext-Mistral-SYDNEY-without-captioning
  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. -->

# convnext-Mistral-SYDNEY-without-captioning

This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1330
- Accuracy: 66.89
- Bleu-1: 0.7077
- Bleu-2: 0.6268
- Bleu-3: 0.5589
- Bleu-4: 0.5054
- Meteor: 0.6430
- Rouge-l: 0.6403
- Cider: 2.1213

## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 50
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 1024
- num_epochs: 128
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Bleu-1 | Bleu-2 | Bleu-3 | Bleu-4 | Meteor | Rouge-l | Cider  |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:------:|:-------:|:------:|
| No log        | 1.0   | 44   | 4.1274          | 15.85    | 0.1592 | 0.0702 | 0.0187 | 0.0046 | 0.1675 | 0.1995  | 0.0297 |
| No log        | 2.0   | 88   | 3.6176          | 44.55    | 0.1722 | 0.0991 | 0.0482 | 0.0234 | 0.1199 | 0.1919  | 0.0449 |
| No log        | 3.0   | 132  | 2.6945          | 54.06    | 0.4796 | 0.4131 | 0.3526 | 0.3018 | 0.4825 | 0.4832  | 0.7144 |
| No log        | 4.0   | 176  | 1.1003          | 63.69    | 0.6803 | 0.5727 | 0.4940 | 0.4260 | 0.6185 | 0.6096  | 1.9285 |
| No log        | 5.0   | 220  | 0.8785          | 64.59    | 0.6991 | 0.5960 | 0.5174 | 0.4568 | 0.6156 | 0.6107  | 1.8999 |
| No log        | 6.0   | 264  | 0.8430          | 66.56    | 0.7087 | 0.6125 | 0.5405 | 0.4797 | 0.6845 | 0.6561  | 2.1839 |
| No log        | 7.0   | 308  | 0.8344          | 65.76    | 0.7480 | 0.6570 | 0.5820 | 0.5169 | 0.6814 | 0.6759  | 2.2340 |
| No log        | 8.0   | 352  | 0.8744          | 64.16    | 0.6873 | 0.5899 | 0.5208 | 0.4661 | 0.6778 | 0.6405  | 2.1731 |
| No log        | 9.0   | 396  | 0.8152          | 65.58    | 0.7681 | 0.6929 | 0.6344 | 0.5855 | 0.7326 | 0.7228  | 2.7204 |
| No log        | 10.0  | 440  | 0.8463          | 65.68    | 0.7484 | 0.6613 | 0.5913 | 0.5331 | 0.6891 | 0.6779  | 2.3994 |
| No log        | 11.0  | 484  | 0.8295          | 66.19    | 0.7308 | 0.6472 | 0.5765 | 0.5175 | 0.6854 | 0.6762  | 2.3252 |
| No log        | 12.0  | 528  | 0.8563          | 66.76    | 0.7071 | 0.6033 | 0.5286 | 0.4649 | 0.6508 | 0.6274  | 1.9048 |
| No log        | 13.0  | 572  | 0.9066          | 65.26    | 0.7745 | 0.6850 | 0.6148 | 0.5563 | 0.7058 | 0.6980  | 2.3512 |
| No log        | 14.0  | 616  | 0.9738          | 66.73    | 0.6833 | 0.5907 | 0.5193 | 0.4645 | 0.6206 | 0.6089  | 2.0059 |
| No log        | 15.0  | 660  | 0.9778          | 65.21    | 0.7471 | 0.6589 | 0.5880 | 0.5276 | 0.6800 | 0.6704  | 2.3928 |
| No log        | 16.0  | 704  | 1.0099          | 67.3     | 0.7493 | 0.6671 | 0.6059 | 0.5569 | 0.7204 | 0.6960  | 2.3103 |
| No log        | 17.0  | 748  | 1.0429          | 67.33    | 0.6889 | 0.5977 | 0.5291 | 0.4738 | 0.6658 | 0.6307  | 2.1452 |
| No log        | 18.0  | 792  | 1.0302          | 67.17    | 0.7137 | 0.6411 | 0.5831 | 0.5334 | 0.6533 | 0.6467  | 2.0804 |
| No log        | 19.0  | 836  | 1.1330          | 66.89    | 0.7077 | 0.6268 | 0.5589 | 0.5054 | 0.6430 | 0.6403  | 2.1213 |


### Framework versions

- Transformers 5.12.1
- Pytorch 2.12.1+cu130
- Datasets 5.0.0
- Tokenizers 0.22.2