---
base_model: OFA-Sys/chinese-clip-vit-base-patch16
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: sentance_split_by_time_gpt_None
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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[<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/o5nhn3mn)
# sentance_split_by_time_gpt_None

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: 2.7000
- Accuracy: 0.2724

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 2.0366        | 5.9928  | 1866  | 2.3592          | 0.3213   |
| 1.8711        | 11.9855 | 3732  | 2.4432          | 0.3110   |
| 1.7987        | 17.9783 | 5598  | 2.5182          | 0.3014   |
| 1.7585        | 23.9711 | 7464  | 2.5565          | 0.2937   |
| 1.7381        | 29.9639 | 9330  | 2.5971          | 0.2895   |
| 1.7139        | 35.9566 | 11196 | 2.6406          | 0.2849   |
| 1.7102        | 41.9494 | 13062 | 2.6703          | 0.2815   |
| 1.6951        | 47.9422 | 14928 | 2.6753          | 0.2783   |
| 1.6954        | 53.9350 | 16794 | 2.6847          | 0.2761   |
| 1.6888        | 59.9277 | 18660 | 2.7000          | 0.2741   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1