Hub-Report-20251201153542

This model is a fine-tuned version of intfloat/e5-base-v2 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0672
• F1: 0.8979
• Roc Auc: 0.9449
• Accuracy: 0.8937

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 13

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.2122	1.0	936	0.0776	0.8705	0.9217	0.8510
0.0581	2.0	1872	0.0638	0.8844	0.9358	0.8765
0.032	3.0	2808	0.0627	0.8929	0.9421	0.8877
0.0207	4.0	3744	0.0633	0.8951	0.9424	0.8881
0.0158	5.0	4680	0.0672	0.8979	0.9449	0.8937
0.012	6.0	5616	0.0720	0.8974	0.9442	0.8949
0.0113	7.0	6552	0.0783	0.8930	0.9425	0.8887
0.0092	8.0	7488	0.0799	0.8958	0.9435	0.8915
0.0069	9.0	8424	0.0908	0.8871	0.9390	0.8846
0.0063	10.0	9360	0.0858	0.8948	0.9428	0.8912
0.0054	11.0	10296	0.0861	0.8942	0.9430	0.8909
0.0054	12.0	11232	0.0867	0.8930	0.9428	0.8887
0.0036	13.0	12168	0.0888	0.8912	0.9415	0.8868

Framework versions

• Transformers 4.57.2
• Pytorch 2.9.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.1