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timesformer-bert-video-captioning
AlexZigma/timesformer-bert-video-captioning
timesformer-bert-video-captioning is a image-text-to-text model from AlexZigma. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image-Text-to-Text
- Library
- transformers
- Model type
- vision-encoder-decoder
- Access
- Public
- Created
- Jul 12, 2023
- Updated
- Jul 13, 2023
- SHA
- bd34c4f0
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Model card
From the Hugging Face model README
<!-- 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. -->
timesformer-bert-video-captioning
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2821
- Rouge1: 30.0468
- Rouge2: 8.4998
- Rougel: 29.0632
- Rougelsum: 29.0231
- Bleu: 4.8298
- Gen Len: 9.5332
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Bleu | Gen Len | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|---|---|
| 2.4961 | 0.12 | 200 | 1.5879 | 9.5332 | 1.6548 | 25.4717 | 5.11 | 24.6679 | 24.6696 |
| 1.6561 | 0.25 | 400 | 2.3515 | 9.5332 | 1.5339 | 26.1748 | 5.9106 | 25.413 | 25.3958 |
| 1.5772 | 0.37 | 600 | 2.266 | 9.5332 | 1.4510 | 28.6891 | 6.0431 | 27.7387 | 27.8043 |
| 1.492 | 0.49 | 800 | 3.6517 | 9.5332 | 1.3760 | 29.0257 | 7.8515 | 28.3142 | 28.3036 |
| 1.4736 | 0.61 | 1000 | 3.4866 | 9.5332 | 1.3425 | 27.9774 | 6.2175 | 26.7783 | 26.7207 |
| 1.3856 | 0.74 | 1200 | 3.1649 | 9.5332 | 1.3118 | 27.3532 | 6.5569 | 26.4964 | 26.5087 |
| 1.3972 | 0.86 | 1400 | 3.5337 | 9.5332 | 1.2868 | 28.233 | 7.6471 | 27.3651 | 27.3354 |
| 1.374 | 0.98 | 1600 | 3.5737 | 9.5332 | 1.2571 | 28.8216 | 7.542 | 27.9166 | 27.9353 |
| 1.2207 | 1.1 | 1800 | 3.7983 | 9.5332 | 1.3362 | 29.9574 | 8.1088 | 28.8866 | 28.855 |
| 1.1861 | 1.23 | 2000 | 3.6521 | 9.5332 | 1.3295 | 30.072 | 7.7799 | 28.8417 | 28.864 |
| 1.1173 | 1.35 | 2200 | 3.9784 | 9.5332 | 1.3335 | 29.736 | 7.9661 | 28.6877 | 28.6974 |
| 1.1255 | 1.47 | 2400 | 4.3021 | 9.5332 | 1.3097 | 29.8176 | 8.4656 | 28.958 | 28.9571 |
| 1.0909 | 1.6 | 2600 | 1.3095 | 30.0233 | 8.4896 | 29.2562 | 29.2375 | 4.4782 | 9.5332 |
| 1.1205 | 1.72 | 2800 | 1.2992 | 29.7164 | 8.007 | 28.5027 | 28.5018 | 4.44 | 9.5332 |
| 1.1069 | 1.84 | 3000 | 1.2830 | 29.851 | 8.4312 | 28.8139 | 28.8205 | 4.6065 | 9.5332 |
| 1.076 | 1.96 | 3200 | 1.2821 | 30.0468 | 8.4998 | 29.0632 | 29.0231 | 4.8298 | 9.5332 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- Created
- Jul 12, 2023
- Updated
- Jul 13, 2023