sentiment-unipelt-4

apwic/sentiment-unipelt-4

sentiment-unipelt-4 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 Jun 3, 2024

Repo size

195 MB

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Public

Hugging Face

Repo makeup

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At a glance

License: mit
Access: Public
Created: May 26, 2024
Updated: Jun 3, 2024
SHA: 7835eda2

Base models

indolem/indobert-base-uncased

Model card

From the Hugging Face model README

sentiment-unipelt-4

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: 0.2986
Accuracy: 0.8772
Precision: 0.8561
Recall: 0.8431
F1: 0.8492

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
0.552	1.0	122	0.5144	0.7293	0.6759	0.6810	0.6782
0.4663	2.0	244	0.4076	0.8170	0.8013	0.7330	0.7540
0.37	3.0	366	0.3749	0.8346	0.8484	0.7405	0.7687
0.3284	4.0	488	0.3257	0.8571	0.8273	0.8289	0.8281
0.3035	5.0	610	0.3099	0.8546	0.8246	0.8246	0.8246
0.2705	6.0	732	0.2940	0.8697	0.8474	0.8328	0.8395
0.2439	7.0	854	0.2855	0.8697	0.8488	0.8303	0.8386
0.2232	8.0	976	0.3066	0.8722	0.8528	0.8321	0.8413
0.2142	9.0	1098	0.2912	0.8747	0.8625	0.8263	0.8413
0.2009	10.0	1220	0.2746	0.8546	0.8254	0.8221	0.8238
0.1825	11.0	1342	0.2736	0.8647	0.8359	0.8392	0.8376
0.1717	12.0	1464	0.2938	0.8622	0.8399	0.8200	0.8289
0.1634	13.0	1586	0.2855	0.8747	0.8457	0.8588	0.8517
0.158	14.0	1708	0.2812	0.8772	0.8514	0.8531	0.8522
0.1528	15.0	1830	0.2863	0.8747	0.8479	0.8513	0.8496
0.1486	16.0	1952	0.2951	0.8722	0.8512	0.8346	0.8422
0.1455	17.0	2074	0.2931	0.8822	0.8596	0.8542	0.8568
0.1318	18.0	2196	0.2960	0.8772	0.8535	0.8481	0.8507
0.1287	19.0	2318	0.2975	0.8772	0.8561	0.8431	0.8492
0.1243	20.0	2440	0.2986	0.8772	0.8561	0.8431	0.8492

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2

License: mit
Languages: id
Created: May 26, 2024
Updated: Jun 3, 2024