sentiment-lora-r16-1

apwic/sentiment-lora-r16-1

sentiment-lora-r16-1 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

106 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: Jun 3, 2024
Updated: Jun 3, 2024
SHA: 4f5cc169

Base models

indolem/indobert-base-uncased

Model card

From the Hugging Face model README

sentiment-lora-r16-1

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: 0.2834
Accuracy: 0.8822
Precision: 0.8574
Recall: 0.8592
F1: 0.8583

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
0.5612	1.0	122	0.5258	0.7268	0.6614	0.6317	0.6399
0.4935	2.0	244	0.4827	0.7494	0.7127	0.7427	0.7201
0.428	3.0	366	0.3863	0.8246	0.7874	0.7984	0.7924
0.363	4.0	488	0.3415	0.8446	0.8207	0.7926	0.8043
0.3321	5.0	610	0.3417	0.8521	0.8186	0.8429	0.8285
0.3086	6.0	732	0.3376	0.8496	0.8158	0.8386	0.8253
0.2899	7.0	854	0.3156	0.8722	0.8453	0.8471	0.8462
0.2828	8.0	976	0.3073	0.8722	0.8463	0.8446	0.8454
0.2638	9.0	1098	0.3156	0.8622	0.8300	0.8525	0.8395
0.2628	10.0	1220	0.3002	0.8797	0.8522	0.8624	0.8570
0.249	11.0	1342	0.2935	0.8797	0.8572	0.8499	0.8534
0.2429	12.0	1464	0.2938	0.8772	0.8514	0.8531	0.8522
0.2406	13.0	1586	0.2902	0.8797	0.8585	0.8474	0.8526
0.2377	14.0	1708	0.2889	0.8722	0.8437	0.8521	0.8477
0.2257	15.0	1830	0.2848	0.8797	0.8530	0.8599	0.8563
0.2215	16.0	1952	0.2862	0.8747	0.8451	0.8613	0.8524
0.2297	17.0	2074	0.2833	0.8822	0.8610	0.8517	0.8561
0.2263	18.0	2196	0.2854	0.8772	0.8483	0.8631	0.8550
0.2194	19.0	2318	0.2833	0.8797	0.8539	0.8574	0.8556
0.214	20.0	2440	0.2834	0.8822	0.8574	0.8592	0.8583

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2

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