|
--- |
|
license: cc-by-nc-4.0 |
|
base_model: MCG-NJU/videomae-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: videomae-base-groub2-finetuned-SLT-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. --> |
|
|
|
# videomae-base-groub2-finetuned-SLT-subset |
|
|
|
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2405 |
|
- Accuracy: 1.0 |
|
|
|
## 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: 2 |
|
- eval_batch_size: 2 |
|
- 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: 960 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:| |
|
| 3.928 | 0.06 | 61 | 3.6624 | 0.0244 | |
|
| 3.8332 | 1.06 | 122 | 3.5354 | 0.0732 | |
|
| 3.5587 | 2.06 | 183 | 3.2996 | 0.0976 | |
|
| 3.4907 | 3.06 | 244 | 3.1796 | 0.0976 | |
|
| 3.4674 | 4.06 | 305 | 3.1159 | 0.0976 | |
|
| 3.5079 | 5.06 | 366 | 3.0202 | 0.1220 | |
|
| 2.9034 | 6.06 | 427 | 2.8292 | 0.1707 | |
|
| 2.9286 | 7.06 | 488 | 2.4582 | 0.6098 | |
|
| 2.4388 | 8.06 | 549 | 1.8469 | 0.7317 | |
|
| 1.7172 | 9.06 | 610 | 1.2915 | 0.8537 | |
|
| 1.2504 | 10.06 | 671 | 0.8991 | 0.9512 | |
|
| 0.974 | 11.06 | 732 | 0.5943 | 0.9268 | |
|
| 0.4528 | 12.06 | 793 | 0.4040 | 0.9512 | |
|
| 0.3593 | 13.06 | 854 | 0.3052 | 1.0 | |
|
| 0.2068 | 14.06 | 915 | 0.2569 | 1.0 | |
|
| 0.185 | 15.05 | 960 | 0.2405 | 1.0 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.3 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|
