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

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minoosh/
bert-reg-crossencoder-contrastive

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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-contrastive

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: 0.0001
Mse: 0.2717
Mae: 0.4451
Pearson Corr: -0.2034
Spearman Corr: -0.1953
Cosine Sim: 0.9027

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
0.0148	1.0	41	0.0014	0.2889	0.4614	-0.1243	-0.0625	0.9003
0.0096	2.0	82	0.0074	0.3706	0.5451	-0.0433	-0.0347	0.9030
0.0059	3.0	123	0.0001	0.2549	0.4285	-0.0372	-0.0585	0.9032
0.004	4.0	164	0.0023	0.3175	0.4940	-0.0783	-0.0715	0.9029
0.0026	5.0	205	0.0003	0.2770	0.4519	-0.0308	-0.0070	0.9033
0.0019	6.0	246	0.0002	0.2771	0.4512	-0.1884	-0.1805	0.9028
0.0018	7.0	287	0.0001	0.2717	0.4451	-0.2034	-0.1953	0.9027

Framework versions

Transformers 4.44.2
Pytorch 2.5.0+cu121
Datasets 3.1.0
Tokenizers 0.19.1