website_classification

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

Model description

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The following hyperparameters were used during training:

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
2.404	1.0	71	1.7840	0.8865	0.8776	0.8785	0.8865
1.295	2.0	142	0.8539	0.8972	0.8871	0.8803	0.8972
0.6186	3.0	213	0.4818	0.9326	0.9263	0.9266	0.9326
0.3103	4.0	284	0.3101	0.9397	0.9343	0.9324	0.9397
0.1618	5.0	355	0.3001	0.9291	0.9251	0.9278	0.9291
0.0893	6.0	426	0.2743	0.9291	0.9251	0.9276	0.9291
0.0547	7.0	497	0.2605	0.9255	0.9236	0.9334	0.9255
0.028	8.0	568	0.2167	0.9397	0.9375	0.9403	0.9397
0.0186	9.0	639	0.2096	0.9468	0.9467	0.9499	0.9468
0.0134	10.0	710	0.2219	0.9362	0.9354	0.9402	0.9362
0.0107	11.0	781	0.2124	0.9468	0.9466	0.9507	0.9468
0.0087	12.0	852	0.2119	0.9504	0.9497	0.9534	0.9504
0.0075	13.0	923	0.2141	0.9504	0.9497	0.9534	0.9504
0.0066	14.0	994	0.2198	0.9433	0.9415	0.9442	0.9433
0.0058	15.0	1065	0.2188	0.9468	0.9454	0.9474	0.9468
0.0052	16.0	1136	0.2181	0.9468	0.9454	0.9474	0.9468
0.0047	17.0	1207	0.2220	0.9504	0.9489	0.9510	0.9504
0.0044	18.0	1278	0.2232	0.9504	0.9489	0.9510	0.9504
0.004	19.0	1349	0.2216	0.9539	0.9535	0.9565	0.9539
0.0037	20.0	1420	0.2251	0.9504	0.9489	0.9510	0.9504
0.0036	21.0	1491	0.2275	0.9468	0.9451	0.9470	0.9468
0.0034	22.0	1562	0.2264	0.9539	0.9535	0.9565	0.9539
0.0032	23.0	1633	0.2283	0.9504	0.9489	0.9510	0.9504
0.003	24.0	1704	0.2299	0.9504	0.9489	0.9510	0.9504
0.0029	25.0	1775	0.2282	0.9468	0.9451	0.9470	0.9468
0.0029	26.0	1846	0.2288	0.9468	0.9451	0.9470	0.9468
0.0028	27.0	1917	0.2286	0.9504	0.9489	0.9510	0.9504
0.0027	28.0	1988	0.2293	0.9504	0.9489	0.9510	0.9504
0.0026	29.0	2059	0.2291	0.9504	0.9489	0.9510	0.9504
0.0026	30.0	2130	0.2292	0.9504	0.9489	0.9510	0.9504