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---
base_model: OFA-Sys/chinese-clip-vit-base-patch16
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentance_split_by_time_ocr_concate_2
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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/shark_meow_team/huggingface/runs/skkco61i)
# sentance_split_by_time_ocr_concate_2
This model is a fine-tuned version of [OFA-Sys/chinese-clip-vit-base-patch16](https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7759
- Accuracy: 0.0760
## 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: 1e-05
- train_batch_size: 25
- eval_batch_size: 20
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 200
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 2.077 | 5.9928 | 1866 | 3.0593 | 0.0767 |
| 1.8747 | 11.9855 | 3732 | 3.1969 | 0.0788 |
| 1.7613 | 17.9783 | 5598 | 3.2275 | 0.0782 |
| 1.703 | 23.9711 | 7464 | 3.3677 | 0.0788 |
| 1.676 | 29.9639 | 9330 | 3.4368 | 0.0784 |
| 1.6495 | 35.9566 | 11196 | 3.5520 | 0.0783 |
| 1.6449 | 41.9494 | 13062 | 3.5562 | 0.0781 |
| 1.6293 | 47.9422 | 14928 | 3.6218 | 0.0775 |
| 1.6301 | 53.9350 | 16794 | 3.7435 | 0.0770 |
| 1.6232 | 59.9277 | 18660 | 3.7759 | 0.0765 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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