sentiment-pt-pl30-3

apwic/sentiment-pt-pl30-3

sentiment-pt-pl30-3 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

185 MB

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Public

Hugging Face

Repo makeup

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.bin142 MB · 99%

At a glance

License: mit
Access: Public
Created: May 25, 2024
Updated: Jun 3, 2024
SHA: f835a209

Base models

indolem/indobert-base-uncased

Model card

From the Hugging Face model README

sentiment-pt-pl30-3

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: 0.3054
Accuracy: 0.8872
Precision: 0.8614
Recall: 0.8702
F1: 0.8656

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
0.5456	1.0	122	0.4916	0.7469	0.6913	0.6534	0.6642
0.4369	2.0	244	0.4108	0.8120	0.7803	0.8220	0.7912
0.3316	3.0	366	0.3294	0.8571	0.8463	0.7964	0.8152
0.2909	4.0	488	0.3019	0.8772	0.8547	0.8456	0.8500
0.2584	5.0	610	0.3023	0.8697	0.8428	0.8428	0.8428
0.237	6.0	732	0.3020	0.8647	0.8359	0.8392	0.8376
0.2186	7.0	854	0.2989	0.8722	0.8425	0.8571	0.8491
0.2108	8.0	976	0.2961	0.8872	0.8687	0.8552	0.8615
0.1898	9.0	1098	0.3013	0.8747	0.8499	0.8463	0.8481
0.1894	10.0	1220	0.3231	0.8747	0.8537	0.8388	0.8457
0.1817	11.0	1342	0.3012	0.8772	0.8524	0.8506	0.8515
0.1723	12.0	1464	0.2979	0.8647	0.8377	0.8342	0.8359
0.1547	13.0	1586	0.2937	0.8697	0.8449	0.8378	0.8412
0.1569	14.0	1708	0.3065	0.8697	0.8383	0.8628	0.8486
0.1442	15.0	1830	0.2884	0.8772	0.8504	0.8556	0.8530
0.1435	16.0	1952	0.3016	0.8797	0.8530	0.8599	0.8563
0.1378	17.0	2074	0.3114	0.8772	0.8478	0.8656	0.8557
0.1377	18.0	2196	0.3096	0.8772	0.8483	0.8631	0.8550
0.1307	19.0	2318	0.3065	0.8797	0.8539	0.8574	0.8556
0.126	20.0	2440	0.3054	0.8872	0.8614	0.8702	0.8656

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 25, 2024
Updated: Jun 3, 2024