bert-reg-crossencoder-mse – AI Model by minoosh | AlphaNeural AI

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minoosh/
bert-reg-crossencoder-mse

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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bert-reg-crossencoder-mse

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0752
Mse: 0.0752
Mae: 0.2120
Pearson Corr: 0.3937
Spearman Corr: 0.3178
Cosine Sim: 0.9163

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Mse	Mae	Pearson Corr	Spearman Corr	Cosine Sim
0.1034	1.0	41	0.0704	0.0704	0.2198	0.1914	0.2429	0.9070
0.097	2.0	82	0.0739	0.0739	0.2161	0.2185	0.2208	0.9059
0.0877	3.0	123	0.0663	0.0663	0.2154	0.3214	0.2426	0.9133
0.0679	4.0	164	0.0723	0.0723	0.2054	0.3722	0.3382	0.9175
0.0569	5.0	205	0.0644	0.0644	0.2058	0.3867	0.3552	0.9155
0.0408	6.0	246	0.0773	0.0773	0.2102	0.4045	0.3105	0.9190
0.0317	7.0	287	0.0752	0.0752	0.2120	0.3937	0.3178	0.9163

Framework versions

Transformers 4.44.2
Pytorch 2.5.0+cu121
Datasets 3.1.0
Tokenizers 0.19.1