emotion_model – AI Model by envidevelopment | AlphaNeural AI

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emotion_model

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indobenchmark/indobert-base-p1

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emotion_model

This model is a fine-tuned version of indobenchmark/indobert-base-p1 on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.5360
Accuracy: 0.7457
Precision: 0.7468
Recall: 0.7457
F1: 0.7462

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	220	0.7447	0.7253	0.7362	0.7253	0.7279
No log	2.0	440	0.7712	0.7321	0.7448	0.7321	0.7260
0.8357	3.0	660	0.8327	0.7264	0.7455	0.7264	0.7310
0.8357	4.0	880	1.1408	0.7287	0.7323	0.7287	0.7285
0.18	5.0	1100	1.3507	0.7344	0.7505	0.7344	0.7383
0.18	6.0	1320	1.5023	0.7310	0.7351	0.7310	0.7321
0.0299	7.0	1540	1.5378	0.7344	0.7331	0.7344	0.7330
0.0299	8.0	1760	1.5360	0.7457	0.7468	0.7457	0.7462
0.0299	9.0	1980	1.5969	0.7423	0.7498	0.7423	0.7447
0.0043	10.0	2200	1.6154	0.7378	0.7472	0.7378	0.7407

Framework versions

Transformers 4.41.2
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.19.1