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Mayilde/
test

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test

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

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The following hyperparameters were used during training:

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.7815	1.0	250	0.2583	0.914	0.9123
0.2034	2.0	500	0.1686	0.928	0.9280
0.1371	3.0	750	0.1487	0.9335	0.9342
0.1061	4.0	1000	0.1592	0.932	0.9317
0.0858	5.0	1250	0.1462	0.937	0.9368
0.0716	6.0	1500	0.1446	0.937	0.9371
0.0611	7.0	1750	0.1559	0.939	0.9393
0.0535	8.0	2000	0.1529	0.939	0.9391