bert-base-uncased-finetuned-math_punctuation-25-01-two_linear_layers-frozen_bert

JoshuaRubin/bert-base-uncased-finetuned-math_punctuation-25-01-two_linear_layers-frozen_bert

bert-base-uncased-finetuned-math_punctuation-25-01-two_linear_layers-frozen_bert is a machine learning model from JoshuaRubin. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
Access: Public
Created: Jan 25, 2023
Updated: Jan 25, 2023
SHA: 296fc63f

Model card

From the Hugging Face model README

bert-base-uncased-finetuned-math_punctuation-25-01-two_linear_layers-frozen_bert

This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2150
Micro f1: 0.8910
Macro f1: 0.2672
Weighted f1: 0.8495

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: 5e-05
train_batch_size: 16
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Micro f1	Macro f1	Weighted f1
0.193	0.62	500	0.2146	0.8937	0.2360	0.8435
0.1936	1.23	1000	0.2130	0.8937	0.2360	0.8435
0.1924	1.85	1500	0.2119	0.8937	0.2361	0.8435
0.1911	2.47	2000	0.2128	0.8936	0.2369	0.8437
0.1909	3.09	2500	0.2114	0.8937	0.2369	0.8437
0.1904	3.7	3000	0.2137	0.8935	0.2407	0.8445
0.1935	4.32	3500	0.2138	0.8934	0.2469	0.8458
0.1874	4.94	4000	0.2118	0.8929	0.2561	0.8479
0.1908	5.56	4500	0.2134	0.8925	0.2588	0.8483
0.1877	6.17	5000	0.2135	0.8918	0.2628	0.8490
0.1881	6.79	5500	0.2133	0.8931	0.2554	0.8478
0.1902	7.41	6000	0.2137	0.8922	0.2603	0.8485
0.1883	8.02	6500	0.2155	0.8914	0.2655	0.8493
0.19	8.64	7000	0.2154	0.8914	0.2647	0.8490
0.1881	9.26	7500	0.2149	0.8915	0.2645	0.8492
0.1876	9.88	8000	0.2141	0.8911	0.2671	0.8496
0.1879	10.49	8500	0.2155	0.8897	0.2722	0.8501
0.1897	11.11	9000	0.2156	0.8910	0.2670	0.8494
0.1883	11.73	9500	0.2150	0.8910	0.2672	0.8495

Framework versions

Transformers 4.25.1
Pytorch 2.0.0.dev20230111
Datasets 2.8.0
Tokenizers 0.13.2

Library: transformers
Created: Jan 25, 2023
Updated: Jan 25, 2023