---
license: mit
base_model: microsoft/xclip-base-patch32
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xclip-base-patch32-finetuned-custom-subset
  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/huangyangyu/huggingface/runs/v8zohmjq)
[<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/huangyangyu/huggingface/runs/v8zohmjq)
[<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/huangyangyu/huggingface/runs/v8zohmjq)
[<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/huangyangyu/huggingface/runs/v8zohmjq)
# xclip-base-patch32-finetuned-custom-subset

This model is a fine-tuned version of [microsoft/xclip-base-patch32](https://huggingface.co/microsoft/xclip-base-patch32) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5862
- Accuracy: 0.7308

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1420

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.8431        | 0.0507  | 72   | 0.5928          | 0.7308   |
| 0.6657        | 1.0507  | 144  | 0.7383          | 0.7308   |
| 0.8019        | 2.0507  | 216  | 0.6047          | 0.7308   |
| 0.6275        | 3.0507  | 288  | 0.5946          | 0.7308   |
| 0.561         | 4.0507  | 360  | 0.6646          | 0.7308   |
| 0.594         | 5.0507  | 432  | 0.6098          | 0.7308   |
| 0.6472        | 6.0507  | 504  | 0.5915          | 0.7308   |
| 0.623         | 7.0507  | 576  | 0.5948          | 0.7308   |
| 0.5711        | 8.0507  | 648  | 0.6056          | 0.7308   |
| 0.5967        | 9.0507  | 720  | 0.5887          | 0.7308   |
| 0.5831        | 10.0507 | 792  | 0.5860          | 0.7308   |
| 0.6101        | 11.0507 | 864  | 0.6044          | 0.7308   |
| 0.6265        | 12.0507 | 936  | 0.5856          | 0.7308   |
| 0.6373        | 13.0507 | 1008 | 0.5882          | 0.7308   |
| 0.665         | 14.0507 | 1080 | 0.5852          | 0.7308   |
| 0.6183        | 15.0507 | 1152 | 0.5837          | 0.7308   |
| 0.7786        | 16.0507 | 1224 | 0.5834          | 0.7308   |
| 0.5489        | 17.0507 | 1296 | 0.5849          | 0.7308   |
| 0.6512        | 18.0507 | 1368 | 0.5843          | 0.7308   |
| 0.5266        | 19.0366 | 1420 | 0.5862          | 0.7308   |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.1.1
- Datasets 2.13.2
- Tokenizers 0.19.1