spa-eng-pos-tagging-v1.3

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: 0.1650
• Accuracy: 0.9471
• Precision: 0.9372
• Recall: 0.8815
• F1: 0.8779
• Hamming Loss: 0.0529

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: 5e-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: 16

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Hamming Loss
0.3809	1.0	1744	0.2945	0.8919	0.8798	0.8290	0.8221	0.1081
0.2625	2.0	3488	0.2725	0.8975	0.9004	0.8279	0.8319	0.1025
0.1918	3.0	5232	0.1901	0.9317	0.9224	0.8645	0.8618	0.0683
0.1674	4.0	6976	0.1780	0.9369	0.9319	0.8695	0.8694	0.0631
0.1478	5.0	8720	0.1816	0.9385	0.9303	0.8735	0.8697	0.0615
0.1201	6.0	10464	0.1650	0.9471	0.9372	0.8815	0.8779	0.0529
0.096	7.0	12208	0.1663	0.9493	0.9390	0.8851	0.8806	0.0507
0.0844	8.0	13952	0.1715	0.9500	0.9421	0.8838	0.8815	0.0500
0.0687	9.0	15696	0.1877	0.9502	0.9433	0.8816	0.8811	0.0498
0.0573	10.0	17440	0.1949	0.9483	0.9444	0.8781	0.8799	0.0517
0.0533	11.0	19184	0.1960	0.9544	0.9450	0.8872	0.8847	0.0456
0.0399	12.0	20928	0.2012	0.9565	0.9494	0.8884	0.8876	0.0435
0.031	13.0	22672	0.2119	0.9571	0.9496	0.8889	0.8879	0.0429
0.0292	14.0	24416	0.2213	0.9587	0.9512	0.8906	0.8896	0.0413
0.024	15.0	26160	0.2274	0.9587	0.9517	0.8899	0.8895	0.0413
0.0198	16.0	27904	0.2314	0.9591	0.8894	0.8905	0.8899	0.0409

Framework versions

• Transformers 4.32.0
• Pytorch 2.0.1+cu118
• Tokenizers 0.13.3