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---
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
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