sentiment-unipelt-1

apwic/sentiment-unipelt-1

sentiment-unipelt-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

195 MB

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Public

Hugging Face

Repo makeup

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.bin147 MB · 99%

At a glance

License: mit
Access: Public
Created: May 26, 2024
Updated: Jun 3, 2024
SHA: 3f0dcfdd

Base models

indolem/indobert-base-uncased

Model card

From the Hugging Face model README

sentiment-unipelt-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: 0.3288
Accuracy: 0.8872
Precision: 0.8606
Recall: 0.8727
F1: 0.8662

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
0.5528	1.0	122	0.5051	0.7168	0.6521	0.6396	0.6444
0.4431	2.0	244	0.4333	0.7970	0.7711	0.8189	0.7789
0.3491	3.0	366	0.3269	0.8672	0.8579	0.8110	0.8293
0.2886	4.0	488	0.2995	0.8722	0.8498	0.8371	0.8430
0.2671	5.0	610	0.2967	0.8697	0.8428	0.8428	0.8428
0.2481	6.0	732	0.3346	0.8546	0.8230	0.8647	0.8365
0.2339	7.0	854	0.2965	0.8672	0.8372	0.8485	0.8425
0.217	8.0	976	0.2929	0.8772	0.8576	0.8406	0.8484
0.1984	9.0	1098	0.2778	0.8797	0.8572	0.8499	0.8534
0.1937	10.0	1220	0.2905	0.8772	0.8628	0.8331	0.8458
0.1734	11.0	1342	0.2919	0.8872	0.8624	0.8677	0.8650
0.1561	12.0	1464	0.3126	0.8797	0.8539	0.8574	0.8556
0.1579	13.0	1586	0.3165	0.8822	0.8610	0.8517	0.8561
0.1459	14.0	1708	0.3108	0.8972	0.8715	0.8873	0.8787
0.1399	15.0	1830	0.3192	0.8922	0.8657	0.8813	0.8728
0.1377	16.0	1952	0.3200	0.8847	0.8567	0.8734	0.8642
0.1259	17.0	2074	0.3303	0.8847	0.8589	0.8659	0.8623
0.1293	18.0	2196	0.3265	0.8922	0.8657	0.8813	0.8728
0.1187	19.0	2318	0.3305	0.8847	0.8589	0.8659	0.8623
0.1302	20.0	2440	0.3288	0.8872	0.8606	0.8727	0.8662

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: Jun 3, 2024