distilbert-base-uncased-finetuned-scientific-eval – AI Model by reyhanemyr | AlphaNeural AI

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distilbert-base-uncased-finetuned-scientific-eval

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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distilbert-base-uncased-finetuned-scientific-eval

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

Loss: 0.1810
Precision: 0.5820
Recall: 0.6719
F1: 0.6237
Accuracy: 0.9483

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	71	0.3580	0.3871	0.1893	0.2542	0.9068
No log	2.0	142	0.2235	0.5585	0.4669	0.5086	0.9332
No log	3.0	213	0.1870	0.6355	0.5994	0.6169	0.9456
No log	4.0	284	0.1857	0.5915	0.6120	0.6016	0.9474
No log	5.0	355	0.1810	0.5820	0.6719	0.6237	0.9483
No log	6.0	426	0.1969	0.6108	0.6435	0.6267	0.9505
No log	7.0	497	0.1914	0.5965	0.6530	0.6235	0.9513
0.1851	8.0	568	0.1964	0.6040	0.6593	0.6305	0.9517
0.1851	9.0	639	0.2026	0.6023	0.6593	0.6295	0.9517
0.1851	10.0	710	0.2027	0.6011	0.6751	0.6360	0.9508

Framework versions

Transformers 4.27.2
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3