sentiment-lora-r2a0d0.05-0

apwic/sentiment-lora-r2a0d0.05-0

sentiment-lora-r2a0d0.05-0 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 May 17, 2024

Repo size

104 MB

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Public

Hugging Face

Repo makeup

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At a glance

License: mit
Access: Public
Created: May 17, 2024
Updated: May 17, 2024
SHA: a562eb4d

Base models

indolem/indobert-base-uncased

Model card

From the Hugging Face model README

sentiment-lora-r2a0d0.05-0

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: 0.3642
Accuracy: 0.8346
Precision: 0.7993
Recall: 0.8080
F1: 0.8034

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
0.5633	1.0	122	0.5100	0.7168	0.6536	0.6446	0.6484
0.5083	2.0	244	0.4999	0.7243	0.6825	0.7049	0.6887
0.4904	3.0	366	0.4595	0.7619	0.7120	0.7065	0.7091
0.4644	4.0	488	0.4287	0.7920	0.7520	0.7253	0.7358
0.4439	5.0	610	0.4399	0.7519	0.7127	0.7395	0.7203
0.4241	6.0	732	0.4027	0.8221	0.7860	0.7816	0.7837
0.4092	7.0	854	0.4019	0.8070	0.7674	0.7835	0.7743
0.3891	8.0	976	0.3805	0.8271	0.7912	0.7926	0.7919
0.3777	9.0	1098	0.3789	0.8271	0.7912	0.7926	0.7919
0.369	10.0	1220	0.3758	0.8396	0.8071	0.8040	0.8055
0.3531	11.0	1342	0.3805	0.8296	0.7933	0.8044	0.7984
0.3486	12.0	1464	0.3801	0.8321	0.7960	0.8112	0.8027
0.3472	13.0	1586	0.3675	0.8421	0.8098	0.8083	0.8091
0.3379	14.0	1708	0.3654	0.8371	0.8032	0.8047	0.8040
0.3353	15.0	1830	0.3703	0.8421	0.8080	0.8183	0.8127
0.3213	16.0	1952	0.3709	0.8371	0.8019	0.8147	0.8077
0.3214	17.0	2074	0.3641	0.8371	0.8024	0.8097	0.8059
0.3225	18.0	2196	0.3640	0.8371	0.8024	0.8097	0.8059
0.3159	19.0	2318	0.3649	0.8346	0.7993	0.8080	0.8034
0.3195	20.0	2440	0.3642	0.8346	0.7993	0.8080	0.8034

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 17, 2024
Updated: May 17, 2024