sentiment-seq_bn-2

apwic/sentiment-seq_bn-2

sentiment-seq_bn-2 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 Jul 1, 2024

Repo size

107 MB

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Public

Hugging Face

Repo makeup

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At a glance

License: mit
Access: Public
Created: Jun 9, 2024
Updated: Jul 1, 2024
SHA: a5fed5d8

Base models

indolem/indobert-base-uncased

Model card

From the Hugging Face model README

sentiment-seq_bn-2

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: 0.3205
Accuracy: 0.8772
Precision: 0.8609
Recall: 0.8356
F1: 0.8467

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
0.5517	1.0	122	0.5131	0.7168	0.6513	0.6371	0.6424
0.4833	2.0	244	0.4657	0.7519	0.7088	0.7295	0.7159
0.4318	3.0	366	0.4056	0.8120	0.7729	0.7845	0.7781
0.3905	4.0	488	0.3811	0.8421	0.8092	0.8108	0.8100
0.3626	5.0	610	0.3652	0.8496	0.8186	0.8186	0.8186
0.3331	6.0	732	0.3646	0.8546	0.8214	0.8497	0.8325
0.3134	7.0	854	0.3440	0.8672	0.8412	0.8360	0.8385
0.2927	8.0	976	0.3412	0.8647	0.8359	0.8392	0.8376
0.2833	9.0	1098	0.3353	0.8647	0.8352	0.8417	0.8383
0.2672	10.0	1220	0.3296	0.8672	0.8367	0.8510	0.8432
0.2641	11.0	1342	0.3270	0.8772	0.8576	0.8406	0.8484
0.2549	12.0	1464	0.3352	0.8697	0.8558	0.8203	0.8350
0.2534	13.0	1586	0.3402	0.8697	0.8602	0.8153	0.8330
0.2389	14.0	1708	0.3208	0.8822	0.8574	0.8592	0.8583
0.2203	15.0	1830	0.3279	0.8747	0.8605	0.8288	0.8422
0.2298	16.0	1952	0.3175	0.8747	0.8552	0.8363	0.8448
0.2227	17.0	2074	0.3218	0.8747	0.8586	0.8313	0.8431
0.2225	18.0	2196	0.3178	0.8772	0.8524	0.8506	0.8515
0.2192	19.0	2318	0.3199	0.8772	0.8609	0.8356	0.8467
0.2229	20.0	2440	0.3205	0.8772	0.8609	0.8356	0.8467

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

License: mit
Languages: id
Created: Jun 9, 2024
Updated: Jul 1, 2024