sentiment-base-1

apwic/sentiment-base-1

sentiment-base-1 is a machine learning model from apwic. 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. The card lists the license as mit.

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

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Hugging Face

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At a glance

Library: transformers
License: mit
Model type: bert
Access: Public
Created: May 18, 2024
Updated: Jun 3, 2024
SHA: eb7e226f

Base models

indolem/indobert-base-uncased

Model card

From the Hugging Face model README

sentiment-base-1

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

Loss: 0.7908
Accuracy: 0.9023
Precision: 0.8875
Recall: 0.8733
F1: 0.8799

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: 30
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.3889	1.0	122	0.4200	0.8045	0.8255	0.6867	0.7110
0.2335	2.0	244	0.3136	0.8922	0.8644	0.8863	0.8739
0.1411	3.0	366	0.3569	0.8972	0.8781	0.8723	0.8751
0.1078	4.0	488	0.3537	0.9148	0.8923	0.9072	0.8992
0.0822	5.0	610	0.5069	0.8797	0.8795	0.8224	0.8439
0.0529	6.0	732	0.4262	0.9073	0.8862	0.8919	0.8890
0.0365	7.0	854	0.5586	0.8972	0.8743	0.8798	0.8770
0.033	8.0	976	0.5012	0.8947	0.8870	0.8530	0.8675
0.0248	9.0	1098	0.5833	0.8922	0.8873	0.8462	0.8631
0.0123	10.0	1220	0.6611	0.9023	0.8858	0.8758	0.8806
0.0088	11.0	1342	0.6936	0.8947	0.8847	0.8555	0.8682
0.0074	12.0	1464	0.6790	0.9023	0.8858	0.8758	0.8806
0.0141	13.0	1586	0.6981	0.8972	0.8830	0.8648	0.8731
0.0034	14.0	1708	0.7145	0.8972	0.8781	0.8723	0.8751
0.0059	15.0	1830	0.7304	0.8997	0.8871	0.8666	0.8759
0.0056	16.0	1952	0.7518	0.8997	0.8778	0.8816	0.8797
0.0039	17.0	2074	0.7390	0.9023	0.8893	0.8708	0.8793
0.004	18.0	2196	0.7641	0.9023	0.8875	0.8733	0.8799
0.007	19.0	2318	0.7848	0.9023	0.8875	0.8733	0.8799
0.0042	20.0	2440	0.7908	0.9023	0.8875	0.8733	0.8799

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2

Library: transformers
Type: bert
License: mit
Languages: id
Created: May 18, 2024
Updated: Jun 3, 2024