xlm-roberta-large-WNUT-ner – AI Model by dmargutierrez | AlphaNeural AI

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dmargutierrez/
xlm-roberta-large-WNUT-ner

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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xlm-roberta-large-WNUT-ner

This model is a fine-tuned version of xlm-roberta-large on the wnut_17 dataset. It achieves the following results on the evaluation set:

Loss: 0.3570
Precision: 0.7014
Recall: 0.5116
F1: 0.5916
Accuracy: 0.9570

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	0.2223	0.5588	0.4495	0.4982	0.9504
No log	2.0	426	0.2326	0.6602	0.4430	0.5302	0.9514
0.1516	3.0	639	0.2792	0.6846	0.4124	0.5147	0.9520
0.1516	4.0	852	0.2417	0.6510	0.5134	0.5741	0.9574
0.0427	5.0	1065	0.2954	0.6850	0.4856	0.5683	0.9544
0.0427	6.0	1278	0.3033	0.6761	0.4893	0.5677	0.9557
0.0427	7.0	1491	0.3502	0.7007	0.4838	0.5724	0.9563
0.0178	8.0	1704	0.3712	0.6995	0.4875	0.5745	0.9563
0.0178	9.0	1917	0.3541	0.6951	0.4986	0.5807	0.9569
0.0068	10.0	2130	0.3570	0.7014	0.5116	0.5916	0.9570

Framework versions

Transformers 4.26.0
Pytorch 1.13.1+cu117
Datasets 2.9.0
Tokenizers 0.13.2