sentiment-pt-pl10-3

apwic/sentiment-pt-pl10-3

sentiment-pt-pl10-3 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: 305daf6f

Base models

indolem/indobert-base-uncased

Model card

From the Hugging Face model README

sentiment-pt-pl10-3

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: 0.3389
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
0.5509	1.0	122	0.4983	0.7393	0.6801	0.6406	0.6507
0.4511	2.0	244	0.4377	0.7769	0.7547	0.8022	0.7593
0.368	3.0	366	0.3260	0.8571	0.8381	0.8064	0.8196
0.3019	4.0	488	0.3036	0.8647	0.8410	0.8267	0.8333
0.2668	5.0	610	0.3192	0.8672	0.8372	0.8485	0.8425
0.2471	6.0	732	0.3059	0.8622	0.8305	0.8475	0.8380
0.2422	7.0	854	0.2950	0.8747	0.8451	0.8613	0.8524
0.2258	8.0	976	0.2928	0.8722	0.8463	0.8446	0.8454
0.2054	9.0	1098	0.3049	0.8797	0.8572	0.8499	0.8534
0.2009	10.0	1220	0.3013	0.8747	0.8488	0.8488	0.8488
0.1755	11.0	1342	0.3070	0.8822	0.8574	0.8592	0.8583
0.1821	12.0	1464	0.2995	0.8822	0.8596	0.8542	0.8568
0.1652	13.0	1586	0.3272	0.8847	0.8553	0.8809	0.8660
0.1566	14.0	1708	0.3336	0.8897	0.8609	0.8870	0.8719
0.1634	15.0	1830	0.3150	0.8847	0.8589	0.8659	0.8623
0.1496	16.0	1952	0.3321	0.8922	0.8706	0.8687	0.8697
0.1355	17.0	2074	0.3276	0.8847	0.8599	0.8634	0.8616
0.1477	18.0	2196	0.3365	0.8797	0.8530	0.8599	0.8563
0.1317	19.0	2318	0.3385	0.8822	0.8574	0.8592	0.8583
0.1267	20.0	2440	0.3389	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: May 25, 2024
Updated: Jun 3, 2024