widget:

• structured_data: node_feat: -[[0],[0],[0],[0],[0],[0],[0],[0],[1],[0],[0],[0],[0],[1],[2],[0],[0],[0],[0],[0],[0],[3],[0],[0]], edge_index: -[[0, 1, 1, 1, 1, 2, 3, 4, 4, 4, 5, 5, 6, 6, 7, 7, 7, 8, 8, 9, 9, 10, 10, 10, 11, 11, 12, 12, 12, 13, 14, 14, 15, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 20, 20, 20, 21, 22, 22, 22, 23, 23],[1, 0, 2, 3, 4, 1, 1, 1, 5, 23, 4, 6, 5, 7, 6, 8, 22, 7, 9, 8, 10, 9, 11, 22, 10, 12, 11, 13, 14, 12, 12, 15, 14, 16, 20, 15, 17, 16, 18, 17, 19, 18, 20, 15, 19, 21, 20, 7, 10, 23, 4, 22]]

graph-regression

This model is a fine-tuned version of clefourrier/pcqm4mv2_graphormer_base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 7.6257

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: 64
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 10
• total_train_batch_size: 640
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
18.2131	0.8861	7	10.2140
6.1806	1.8987	15	9.1356
5.1328	2.9114	23	8.2925
4.392	3.9241	31	7.6640
3.4272	4.4304	35	7.6257

Framework versions

• Transformers 4.40.2
• Pytorch 2.2.1+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1