sentiment-lora-r2a0d0.1-0

apwic/sentiment-lora-r2a0d0.1-0

sentiment-lora-r2a0d0.1-0 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: ec855a0f

Base models

indolem/indobert-base-uncased

Model card

From the Hugging Face model README

sentiment-lora-r2a0d0.1-0

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: 0.3608
Accuracy: 0.8471
Precision: 0.8138
Recall: 0.8243
F1: 0.8187

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
0.5634	1.0	122	0.5108	0.7193	0.6572	0.6489	0.6524
0.5081	2.0	244	0.5049	0.7218	0.6829	0.7082	0.6888
0.4924	3.0	366	0.4667	0.7494	0.6977	0.6977	0.6977
0.4698	4.0	488	0.4392	0.7794	0.7349	0.7114	0.7207
0.4519	5.0	610	0.4548	0.7469	0.7169	0.7534	0.7226
0.4356	6.0	732	0.4111	0.8145	0.7770	0.7713	0.7740
0.421	7.0	854	0.4101	0.7945	0.7538	0.7721	0.7612
0.4039	8.0	976	0.3829	0.8296	0.7949	0.7919	0.7934
0.3887	9.0	1098	0.3800	0.8321	0.7972	0.7987	0.7979
0.3797	10.0	1220	0.3768	0.8371	0.8044	0.7997	0.8020
0.368	11.0	1342	0.3842	0.8221	0.7846	0.8016	0.7918
0.3598	12.0	1464	0.3778	0.8271	0.7902	0.8051	0.7968
0.3548	13.0	1586	0.3624	0.8471	0.8167	0.8118	0.8142
0.3469	14.0	1708	0.3637	0.8446	0.8120	0.8151	0.8135
0.3431	15.0	1830	0.3685	0.8396	0.8049	0.8165	0.8102
0.3275	16.0	1952	0.3664	0.8371	0.8017	0.8172	0.8086
0.3288	17.0	2074	0.3590	0.8396	0.8055	0.8115	0.8084
0.3335	18.0	2196	0.3607	0.8471	0.8138	0.8243	0.8187
0.3239	19.0	2318	0.3613	0.8446	0.8107	0.8226	0.8161
0.327	20.0	2440	0.3608	0.8471	0.8138	0.8243	0.8187

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