metadata

license: mit
base_model: xlm-roberta-large
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: xlm-roberta-large-finetuned-wikiner-fr
    results: []

xlm-roberta-large-finetuned-wikiner-fr

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

Loss: 0.0518
Precision: 0.8881
Recall: 0.9014
F1: 0.8947
Accuracy: 0.9855

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: 1.5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.02
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1032	0.1	374	0.0853	0.7645	0.8170	0.7899	0.9742
0.0767	0.2	748	0.0721	0.8111	0.8423	0.8264	0.9785
0.074	0.3	1122	0.0655	0.8252	0.8502	0.8375	0.9797
0.0634	0.4	1496	0.0629	0.8423	0.8694	0.8556	0.9809
0.0605	0.5	1870	0.0610	0.8515	0.8711	0.8612	0.9808
0.0578	0.6	2244	0.0594	0.8633	0.8744	0.8688	0.9822
0.0592	0.7	2618	0.0555	0.8624	0.8833	0.8727	0.9825
0.0567	0.8	2992	0.0534	0.8626	0.8838	0.8731	0.9830
0.0522	0.9	3366	0.0563	0.8560	0.8771	0.8664	0.9818
0.0516	1.0	3739	0.0556	0.8702	0.8869	0.8785	0.9831
0.0438	1.0	3740	0.0558	0.8712	0.8873	0.8792	0.9831
0.0395	1.1	4114	0.0565	0.8696	0.8856	0.8775	0.9830
0.0371	1.2	4488	0.0536	0.8762	0.8910	0.8835	0.9838
0.0403	1.3	4862	0.0531	0.8709	0.8887	0.8797	0.9835
0.0366	1.4	5236	0.0517	0.8791	0.8912	0.8851	0.9843
0.037	1.5	5610	0.0510	0.8830	0.8936	0.8883	0.9847
0.0368	1.6	5984	0.0492	0.8795	0.8940	0.8867	0.9845
0.0359	1.7	6358	0.0501	0.8833	0.8986	0.8909	0.9850
0.034	1.8	6732	0.0496	0.8852	0.8986	0.8918	0.9852
0.0327	1.9	7106	0.0512	0.8762	0.8948	0.8854	0.9843
0.0325	2.0	7478	0.0512	0.8829	0.8945	0.8887	0.9844
0.01	2.0	7480	0.0512	0.8836	0.8945	0.8890	0.9843
0.0232	2.1	7854	0.0526	0.8870	0.9002	0.8936	0.9852
0.0235	2.2	8228	0.0530	0.8841	0.8983	0.8911	0.9848
0.0211	2.3	8602	0.0542	0.8875	0.9008	0.8941	0.9852
0.0235	2.4	8976	0.0525	0.8883	0.9008	0.8945	0.9855
0.0232	2.5	9350	0.0525	0.8874	0.9013	0.8943	0.9855
0.0238	2.6	9724	0.0517	0.8861	0.9011	0.8935	0.9854
0.0223	2.7	10098	0.0513	0.8893	0.9016	0.8954	0.9856
0.0226	2.8	10472	0.0517	0.8892	0.9017	0.8954	0.9856
0.0228	2.9	10846	0.0517	0.8879	0.9013	0.8945	0.9855
0.0235	3.0	11217	0.0518	0.8881	0.9014	0.8947	0.9855

Framework versions

Transformers 4.36.2
Pytorch 2.0.1
Datasets 2.16.1
Tokenizers 0.15.0