ijelid-bert-base-multilingual

fathan/ijelid-bert-base-multilingual

ijelid-bert-base-multilingual is a token classification model from fathan. Use it when you need labels on individual words, such as names. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
Model type: bert
Access: Public
Created: Oct 5, 2022
Updated: Oct 13, 2022
SHA: 891d7675

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Model card

From the Hugging Face model README

ijelid-bert-base-multilingual

This model is a fine-tuned version of BERT multilingual base model (cased) on the Indonesian-Javanese-English code-mixed Twitter dataset.

Label ID and its corresponding name:

Label ID	Label Name
LABEL_0	English (EN)
LABEL_1	Indonesian (ID)
LABEL_2	Javanese (JV)
LABEL_3	Mixed Indonesian-English (MIX-ID-EN)
LABEL_4	Mixed Indonesian-Javanese (MIX-ID-JV)
LABEL_5	Mixed Javanese-English (MIX-JV-EN)
LABEL_6	Other (O)

It achieves the following results on the evaluation set:

Loss: 0.3553
Precision: 0.9189
Recall: 0.9188
F1: 0.9187
Accuracy: 0.9451

It achieves the following results on the test set:

Overall Precision: 0.9249
Overall Recall: 0.9251
Overall F1: 0.925
Overall Accuracy: 0.951

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	386	0.2340	0.8956	0.8507	0.8715	0.9239
0.3379	2.0	772	0.2101	0.9057	0.8904	0.8962	0.9342
0.1603	3.0	1158	0.2231	0.9252	0.8896	0.9065	0.9367
0.1079	4.0	1544	0.2013	0.9272	0.8902	0.9070	0.9420
0.1079	5.0	1930	0.2179	0.9031	0.9179	0.9103	0.9425
0.0701	6.0	2316	0.2330	0.9075	0.9165	0.9114	0.9435
0.051	7.0	2702	0.2433	0.9117	0.9190	0.9150	0.9432
0.0384	8.0	3088	0.2545	0.9001	0.9167	0.9078	0.9439
0.0384	9.0	3474	0.2629	0.9164	0.9159	0.9158	0.9444
0.0293	10.0	3860	0.2881	0.9263	0.9096	0.9178	0.9427
0.022	11.0	4246	0.2882	0.9167	0.9222	0.9191	0.9450
0.0171	12.0	4632	0.3028	0.9203	0.9152	0.9177	0.9447
0.0143	13.0	5018	0.3236	0.9155	0.9167	0.9158	0.9440
0.0143	14.0	5404	0.3301	0.9237	0.9163	0.9199	0.9444
0.0109	15.0	5790	0.3290	0.9187	0.9154	0.9169	0.9442
0.0092	16.0	6176	0.3308	0.9213	0.9178	0.9194	0.9448
0.0075	17.0	6562	0.3501	0.9273	0.9142	0.9206	0.9445
0.0075	18.0	6948	0.3520	0.9200	0.9184	0.9190	0.9447
0.0062	19.0	7334	0.3524	0.9238	0.9183	0.9210	0.9458
0.0051	20.0	7720	0.3553	0.9189	0.9188	0.9187	0.9451

Framework versions

Transformers 4.21.2
Pytorch 1.7.1
Datasets 2.5.1
Tokenizers 0.12.1

Task: Token Classification
Library: transformers
Type: bert
Created: Oct 5, 2022
Updated: Oct 13, 2022