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ViT-BERT-Chess-V2
Migga/ViT-BERT-Chess-V2
ViT-BERT-Chess-V2 is a image-text-to-text model from Migga. 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 25, 2022
- Updated
- Jul 25, 2022
- SHA
- 3192e809
Try a prompt
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. -->
ViT-BERT-Chess-V2
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.7128
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: 2e-05
- train_batch_size: 10
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.0385 | 1.0 | 2770 | 3.9132 |
| 3.7453 | 2.0 | 5540 | 3.7552 |
| 3.6513 | 3.0 | 8310 | 3.7128 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- Created
- Jul 25, 2022
- Updated
- Jul 25, 2022