Instructions to use helena-balabin/pretrained_graphormer_vg_image_graphs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use helena-balabin/pretrained_graphormer_vg_image_graphs with Transformers:
# Load model directly from transformers import GraphormerForEdgePrediction model = GraphormerForEdgePrediction.from_pretrained("helena-balabin/pretrained_graphormer_vg_image_graphs", dtype="auto") - Notebooks
- Google Colab
- Kaggle
pretrained_graphormer_vg_image_graphs
This model is a fine-tuned version of on an unknown dataset.
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: 0.0003
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
Framework versions
- Transformers 4.45.2
- Pytorch 2.7.0+cu126
- Datasets 3.0.2
- Tokenizers 0.20.1
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