bert-base-uncased-finetuned-scientific-exp – AI Model by reyhanemyr | AlphaNeural AI

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reyhanemyr/
bert-base-uncased-finetuned-scientific-exp

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bert-base-uncased-finetuned-scientific-exp

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: 0.1183
Precision: 0.7109
Recall: 0.7696
F1: 0.7391
Accuracy: 0.9674

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
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	81	0.2439	0.53	0.4482	0.4857	0.9352
No log	2.0	162	0.1769	0.7434	0.5391	0.6250	0.9492
No log	3.0	243	0.1405	0.6967	0.6702	0.6832	0.9623
No log	4.0	324	0.1183	0.7109	0.7696	0.7391	0.9674
No log	5.0	405	0.1292	0.7126	0.7759	0.7429	0.9670
No log	6.0	486	0.1371	0.7159	0.7886	0.7505	0.9662
0.1665	7.0	567	0.1452	0.7137	0.7801	0.7455	0.9671
0.1665	8.0	648	0.1596	0.7071	0.8013	0.7512	0.9655
0.1665	9.0	729	0.1578	0.7084	0.8013	0.7520	0.9657
0.1665	10.0	810	0.1565	0.7173	0.7992	0.756	0.9666

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3