sentiment-lora-r16

apwic/sentiment-lora-r16

sentiment-lora-r16 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

126 MB

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Public

Hugging Face

Repo makeup

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.bin105 MB · 99%

At a glance

License: mit
Access: Public
Created: May 26, 2024
Updated: May 27, 2024
SHA: 4244da6d

Base models

indolem/indobert-base-uncased

Model card

From the Hugging Face model README

sentiment-lora-r16

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: 0.2631
Accuracy: 0.8872
Precision: 0.8658
Recall: 0.8602
F1: 0.8629

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
0.5544	1.0	122	0.5252	0.7218	0.6705	0.6807	0.6746
0.4974	2.0	244	0.4521	0.7845	0.7792	0.6650	0.6840
0.4206	3.0	366	0.3642	0.8296	0.8034	0.7694	0.7829
0.3657	4.0	488	0.3376	0.8471	0.8184	0.8068	0.8122
0.3188	5.0	610	0.3198	0.8496	0.8202	0.8136	0.8167
0.3069	6.0	732	0.3127	0.8546	0.8239	0.8272	0.8255
0.2838	7.0	854	0.3053	0.8672	0.8449	0.8285	0.8360
0.2699	8.0	976	0.2976	0.8747	0.8647	0.8238	0.8404
0.2614	9.0	1098	0.2887	0.8647	0.8398	0.8292	0.8342
0.2515	10.0	1220	0.2852	0.8596	0.8316	0.8282	0.8298
0.2453	11.0	1342	0.2800	0.8697	0.8411	0.8478	0.8443
0.236	12.0	1464	0.2718	0.8797	0.8633	0.8399	0.8502
0.227	13.0	1586	0.2712	0.8797	0.8560	0.8524	0.8541
0.227	14.0	1708	0.2757	0.8697	0.8386	0.8603	0.8479
0.2171	15.0	1830	0.2708	0.8822	0.8530	0.8742	0.8622
0.214	16.0	1952	0.2632	0.8872	0.8658	0.8602	0.8629
0.2124	17.0	2074	0.2639	0.8822	0.8574	0.8592	0.8583
0.2166	18.0	2196	0.2632	0.8872	0.8645	0.8627	0.8636
0.2086	19.0	2318	0.2630	0.8822	0.8585	0.8567	0.8575
0.2113	20.0	2440	0.2631	0.8872	0.8658	0.8602	0.8629

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