metadata

license: mit
base_model: indobenchmark/indobert-base-p2
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: performa_model
    results: []

performa_model

This model is a fine-tuned version of indobenchmark/indobert-base-p2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5465
Accuracy: 0.8122
F1: 0.8102
Precision: 0.8105
Recall: 0.8100

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
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	0.14	50	0.4595	0.7946	0.7926	0.7926	0.7926
No log	0.27	100	0.4523	0.7946	0.7946	0.7995	0.8009
No log	0.41	150	0.4501	0.8122	0.8098	0.8110	0.8089
No log	0.54	200	0.4676	0.7811	0.7709	0.7965	0.7678
No log	0.68	250	0.4551	0.8135	0.8099	0.8149	0.8077
No log	0.81	300	0.4422	0.8162	0.8152	0.8146	0.8168
No log	0.95	350	0.4336	0.8162	0.8137	0.8154	0.8126
No log	1.08	400	0.4645	0.8189	0.8164	0.8182	0.8153
No log	1.22	450	0.4805	0.8243	0.8236	0.8231	0.8258
0.4139	1.35	500	0.4984	0.8068	0.8053	0.8048	0.8061
0.4139	1.49	550	0.4506	0.8149	0.8137	0.8131	0.8148
0.4139	1.62	600	0.4364	0.8216	0.8201	0.8198	0.8204
0.4139	1.76	650	0.4889	0.7892	0.7892	0.7992	0.7978
0.4139	1.89	700	0.4348	0.8108	0.8105	0.8114	0.8143
0.4139	2.03	750	0.4537	0.8068	0.8056	0.8050	0.8069
0.4139	2.16	800	0.5296	0.7905	0.7905	0.7947	0.7964
0.4139	2.3	850	0.5819	0.7946	0.7943	0.7955	0.7982
0.4139	2.43	900	0.5868	0.8122	0.8110	0.8104	0.8124
0.4139	2.57	950	0.5613	0.8081	0.8050	0.8081	0.8034
0.2978	2.7	1000	0.5465	0.8122	0.8102	0.8105	0.8100
0.2978	2.84	1050	0.5665	0.8041	0.8022	0.8022	0.8023
0.2978	2.97	1100	0.5876	0.7932	0.7924	0.7921	0.7946
0.2978	3.11	1150	0.7388	0.8014	0.8000	0.7994	0.8009

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0