bert-base-multilingual-cased-WNUT-ner – AI Model by dmargutierrez | AlphaNeural AI

You can deploy this model and start earning money today!

dmargutierrez/
bert-base-multilingual-cased-WNUT-ner

like

0

token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

Views

No views yet

bert-base-multilingual-cased-WNUT-ner

This model is a fine-tuned version of bert-base-multilingual-cased on the wnut_17 dataset. It achieves the following results on the evaluation set:

Loss: 0.3832
Precision: 0.5914
Recall: 0.3809
F1: 0.4634
Accuracy: 0.9501

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	213	0.2791	0.6008	0.2817	0.3836	0.9427
No log	2.0	426	0.2697	0.6520	0.3299	0.4382	0.9479
0.148	3.0	639	0.2846	0.5783	0.3661	0.4484	0.9492
0.148	4.0	852	0.3032	0.6248	0.3642	0.4602	0.9500
0.0413	5.0	1065	0.3355	0.5729	0.3568	0.4397	0.9495
0.0413	6.0	1278	0.3343	0.5714	0.3892	0.4631	0.9501
0.0413	7.0	1491	0.3522	0.5877	0.3818	0.4629	0.9500
0.0182	8.0	1704	0.3844	0.6120	0.3698	0.4610	0.9499
0.0182	9.0	1917	0.3847	0.5986	0.3828	0.4669	0.9504
0.008	10.0	2130	0.3832	0.5914	0.3809	0.4634	0.9501

Framework versions

Transformers 4.26.0
Pytorch 1.13.1+cu117
Datasets 2.9.0
Tokenizers 0.13.2