sentiment-lora-r8

apwic/sentiment-lora-r8

sentiment-lora-r8 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 May 27, 2024

Repo size

120 MB

Likes

Public

Hugging Face

Repo makeup

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.bin104 MB · 99%

At a glance

License: mit
Access: Public
Created: May 26, 2024
Updated: May 27, 2024
SHA: 99f9f748

Base models

indolem/indobert-base-uncased

Model card

From the Hugging Face model README

sentiment-lora-r8

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: 0.2908
Accuracy: 0.8772
Precision: 0.8535
Recall: 0.8481
F1: 0.8507

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
0.556	1.0	122	0.5325	0.7168	0.6617	0.6671	0.6641
0.5103	2.0	244	0.4822	0.7719	0.7715	0.6386	0.6524
0.4637	3.0	366	0.4245	0.8045	0.7715	0.7342	0.7480
0.4173	4.0	488	0.3898	0.8246	0.7888	0.7859	0.7873
0.3674	5.0	610	0.3571	0.8371	0.8059	0.7947	0.7999
0.3484	6.0	732	0.3432	0.8371	0.8038	0.8022	0.8030
0.3247	7.0	854	0.3299	0.8521	0.8271	0.8079	0.8164
0.3102	8.0	976	0.3260	0.8622	0.8510	0.8050	0.8228
0.2991	9.0	1098	0.3138	0.8571	0.8349	0.8114	0.8216
0.29	10.0	1220	0.3123	0.8546	0.8324	0.8071	0.8180
0.2778	11.0	1342	0.3065	0.8672	0.8423	0.8335	0.8377
0.2702	12.0	1464	0.3006	0.8571	0.8349	0.8114	0.8216
0.2664	13.0	1586	0.2996	0.8596	0.8316	0.8282	0.8298
0.264	14.0	1708	0.2987	0.8722	0.8437	0.8521	0.8477
0.254	15.0	1830	0.2951	0.8772	0.8514	0.8531	0.8522
0.2571	16.0	1952	0.2945	0.8672	0.8463	0.8260	0.8351
0.2511	17.0	2074	0.2918	0.8722	0.8463	0.8446	0.8454
0.2574	18.0	2196	0.2909	0.8747	0.8510	0.8438	0.8473
0.2508	19.0	2318	0.2907	0.8772	0.8535	0.8481	0.8507
0.2536	20.0	2440	0.2908	0.8772	0.8535	0.8481	0.8507

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: May 27, 2024