---
library_name: transformers
base_model: OFA-Sys/chinese-clip-vit-base-patch16
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: fusion_None_sep_SEP_describe_llama
  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. -->

# fusion_None_sep_SEP_describe_llama

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: 2.5783
- Accuracy: 0.2842

## 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: 60
- eval_batch_size: 20
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 480
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 2.686         | 5.9653  | 774  | 2.3316          | 0.3232   |
| 2.5095        | 11.9306 | 1548 | 2.3506          | 0.3168   |
| 2.4304        | 17.8960 | 2322 | 2.4180          | 0.3103   |
| 2.3871        | 23.8613 | 3096 | 2.4723          | 0.3052   |
| 2.3556        | 29.8266 | 3870 | 2.5127          | 0.3      |
| 2.3325        | 35.7919 | 4644 | 2.5233          | 0.2965   |
| 2.3155        | 41.7572 | 5418 | 2.5572          | 0.2930   |
| 2.3137        | 47.7225 | 6192 | 2.5639          | 0.2903   |
| 2.2978        | 53.6879 | 6966 | 2.5749          | 0.2878   |
| 2.2964        | 59.6532 | 7740 | 2.5783          | 0.2858   |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.0