bert-base-uncased-MLP-scirepeval-chemistry-LARGE – AI Model by jonas-luehrs | AlphaNeural AI

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jonas-luehrs/
bert-base-uncased-MLP-scirepeval-chemistry-LARGE

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google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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bert-base-uncased-MLP-scirepeval-chemistry-LARGE

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

Loss: 1.7126

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

Training results

Training Loss	Epoch	Step	Validation Loss
2.2387	1.0	1407	1.9832
2.0503	2.0	2814	1.8959
1.9684	3.0	4221	1.8506
1.9195	4.0	5628	1.8186
1.8864	5.0	7035	1.8010
1.8551	6.0	8442	1.7677
1.8311	7.0	9849	1.7436
1.8185	8.0	11256	1.7415
1.8013	9.0	12663	1.7315
1.796	10.0	14070	1.7378

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3