spa-eng-pos-tagging-v2.1 – AI Model by MateiCv | AlphaNeural AI

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MateiCv/
spa-eng-pos-tagging-v2.1

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token-classification

generated_from_trainer

google-bert/bert-base-multilingual-cased

autotrain_compatible

endpoints_compatible

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spa-eng-pos-tagging-v2.1

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

Loss: 0.1523
Accuracy: 0.9516
Precision: 0.9445
Recall: 0.8846
F1: 0.8831
Hamming Loss: 0.0484

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: 3e-05
train_batch_size: 16
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 14

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Hamming Loss
0.3806	1.0	1744	0.2730	0.8940	0.8903	0.8246	0.8252	0.1060
0.2615	2.0	3488	0.2134	0.9181	0.9058	0.8514	0.8464	0.0819
0.1864	3.0	5232	0.1895	0.9284	0.9224	0.8626	0.8608	0.0716
0.1721	4.0	6976	0.1769	0.9352	0.9287	0.8676	0.8667	0.0648
0.1462	5.0	8720	0.1840	0.9355	0.9310	0.8663	0.8670	0.0645
0.1175	6.0	10464	0.1553	0.9487	0.9399	0.8820	0.8794	0.0513
0.0949	7.0	12208	0.1523	0.9516	0.9445	0.8846	0.8831	0.0484
0.0833	8.0	13952	0.1564	0.9535	0.9439	0.8881	0.8847	0.0465
0.0722	9.0	15696	0.1566	0.9549	0.9472	0.8890	0.8867	0.0451
0.0571	10.0	17440	0.1649	0.9565	0.9513	0.8875	0.8880	0.0435
0.0516	11.0	19184	0.1669	0.9587	0.9523	0.8913	0.8905	0.0413
0.0392	12.0	20928	0.1711	0.9598	0.9524	0.8925	0.8911	0.0402
0.03	13.0	22672	0.1800	0.9603	0.9533	0.8935	0.8921	0.0397
0.0308	14.0	24416	0.1827	0.9607	0.9537	0.8938	0.8924	0.0393

Framework versions

Transformers 4.32.0
Pytorch 2.0.1+cu118
Tokenizers 0.13.3