spellcorrector_2510_v15_canine-s

This model is a fine-tuned version of google/canine-s on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1599
• Precision: 0.9768
• Recall: 0.9820
• F1: 0.9794
• Accuracy: 0.9786

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: 4
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1921	1.0	1951	0.1677	0.9417	0.9774	0.9592	0.9650
0.1627	2.0	3902	0.1436	0.9500	0.9779	0.9637	0.9674
0.1395	3.0	5853	0.1266	0.9545	0.9788	0.9665	0.9697
0.1266	4.0	7804	0.1172	0.9661	0.9698	0.9680	0.9702
0.1105	5.0	9755	0.1064	0.9669	0.9766	0.9717	0.9731
0.1011	6.0	11706	0.1011	0.9705	0.9757	0.9731	0.9745
0.0933	7.0	13657	0.0987	0.9718	0.9766	0.9742	0.9752
0.0851	8.0	15608	0.0973	0.9715	0.9787	0.9751	0.9755
0.0758	9.0	17559	0.0998	0.9734	0.9765	0.9750	0.9756
0.069	10.0	19510	0.0993	0.9732	0.9810	0.9771	0.9764
0.0635	11.0	21461	0.1055	0.9739	0.9808	0.9773	0.9766
0.0576	12.0	23412	0.1072	0.9751	0.9794	0.9772	0.9765
0.0493	13.0	25363	0.1078	0.9754	0.9807	0.9780	0.9776
0.0469	14.0	27314	0.1145	0.9757	0.9815	0.9786	0.9777
0.0409	15.0	29265	0.1174	0.9758	0.9806	0.9782	0.9764
0.0373	16.0	31216	0.1218	0.9763	0.9801	0.9782	0.9769
0.0338	17.0	33167	0.1239	0.9768	0.9805	0.9787	0.9773
0.0326	18.0	35118	0.1312	0.9770	0.9787	0.9779	0.9773
0.029	19.0	37069	0.1320	0.9764	0.9809	0.9786	0.9773
0.0245	20.0	39020	0.1376	0.9767	0.9802	0.9784	0.9777
0.0231	21.0	40971	0.1382	0.9763	0.9814	0.9788	0.9776
0.0212	22.0	42922	0.1473	0.9762	0.9826	0.9794	0.9780
0.0201	23.0	44873	0.1485	0.9762	0.9816	0.9789	0.9778
0.0187	24.0	46824	0.1494	0.9763	0.9818	0.9790	0.9775
0.0166	25.0	48775	0.1502	0.9769	0.9813	0.9791	0.9781
0.0163	26.0	50726	0.1560	0.9769	0.9813	0.9791	0.9785
0.0149	27.0	52677	0.1556	0.9764	0.9824	0.9794	0.9784
0.0143	28.0	54628	0.1587	0.9767	0.9818	0.9792	0.9784
0.0126	29.0	56579	0.1589	0.9766	0.9821	0.9793	0.9784
0.013	30.0	58530	0.1599	0.9768	0.9820	0.9794	0.9786

Framework versions

• Transformers 4.28.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.13.3