sentiment-seq_bn-0

apwic/sentiment-seq_bn-0

sentiment-seq_bn-0 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. The card lists the license as mit.

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

Downloads · 30 days

Access

Public

Updated Jul 1, 2024

Repo size

107 MB

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Public

Hugging Face

Repo makeup

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At a glance

License: mit
Access: Public
Created: Jun 9, 2024
Updated: Jul 1, 2024
SHA: 6253814e

Base models

indolem/indobert-base-uncased

Model card

From the Hugging Face model README

sentiment-seq_bn-0

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: 0.3129
Accuracy: 0.8747
Precision: 0.8479
Recall: 0.8513
F1: 0.8496

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
0.5551	1.0	122	0.4975	0.7168	0.6485	0.6271	0.6337
0.4753	2.0	244	0.4682	0.7419	0.7118	0.7474	0.7171
0.412	3.0	366	0.3882	0.8271	0.7994	0.7676	0.7804
0.3498	4.0	488	0.3691	0.8421	0.8098	0.8083	0.8091
0.3361	5.0	610	0.3795	0.8145	0.7789	0.8113	0.7897
0.3081	6.0	732	0.4142	0.7970	0.7665	0.8089	0.7761
0.2918	7.0	854	0.3555	0.8471	0.8130	0.8393	0.8235
0.2739	8.0	976	0.3317	0.8647	0.8439	0.8217	0.8315
0.2586	9.0	1098	0.3596	0.8571	0.8243	0.8464	0.8336
0.2509	10.0	1220	0.3299	0.8622	0.8326	0.8375	0.8349
0.2468	11.0	1342	0.3224	0.8672	0.8393	0.8410	0.8402
0.2372	12.0	1464	0.3294	0.8571	0.8251	0.8389	0.8314
0.2305	13.0	1586	0.3134	0.8697	0.8449	0.8378	0.8412
0.2249	14.0	1708	0.3225	0.8697	0.8399	0.8528	0.8458
0.2193	15.0	1830	0.3188	0.8747	0.8471	0.8538	0.8503
0.2061	16.0	1952	0.3392	0.8521	0.8186	0.8404	0.8278
0.21	17.0	2074	0.3122	0.8797	0.8560	0.8524	0.8541
0.2112	18.0	2196	0.3332	0.8546	0.8216	0.8422	0.8303
0.2002	19.0	2318	0.3121	0.8772	0.8524	0.8506	0.8515
0.2041	20.0	2440	0.3129	0.8747	0.8479	0.8513	0.8496

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

License: mit
Languages: id
Created: Jun 9, 2024
Updated: Jul 1, 2024