small-yoruba-finetuned-ner – AI Model by Binaryy | AlphaNeural AI

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Binaryy/
small-yoruba-finetuned-ner

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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small-yoruba-finetuned-ner

This model is a fine-tuned version of bert-base-multilingual-cased on the wikiann dataset. It achieves the following results on the evaluation set:

Loss: 0.5450
Precision: 0.7748
Recall: 0.7748
F1: 0.7890
Accuracy: 0.8967

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	13	1.1087	0.5043	0.5043	0.5225	0.6713
No log	2.0	26	1.0303	0.4	0.4	0.4229	0.6297
No log	3.0	39	0.7622	0.6147	0.6147	0.6204	0.7456
No log	4.0	52	0.8148	0.5688	0.5688	0.5741	0.7103
No log	5.0	65	0.6816	0.6053	0.6053	0.6244	0.7834
No log	6.0	78	0.7372	0.5826	0.5826	0.6036	0.8048
No log	7.0	91	0.5917	0.7593	0.7593	0.7628	0.8866
No log	8.0	104	0.5758	0.7155	0.7155	0.7444	0.8829
No log	9.0	117	0.5806	0.6903	0.6903	0.7091	0.8741
No log	10.0	130	0.5254	0.7522	0.7522	0.7727	0.9005
No log	11.0	143	0.5422	0.7636	0.7636	0.7742	0.8942
No log	12.0	156	0.5469	0.75	0.75	0.7671	0.8879
No log	13.0	169	0.5410	0.7890	0.7890	0.7963	0.8942
No log	14.0	182	0.5435	0.7890	0.7890	0.7963	0.8942
No log	15.0	195	0.5450	0.7748	0.7748	0.7890	0.8967

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.12.0
Tokenizers 0.13.3