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

• Loss: 0.1946
• Accuracy: 0.9395
• F1: 0.9398
• Recall: 0.9395
• Precision: 0.9413
• Combined Score: 0.9400

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: 0.0005
• train_batch_size: 128
• eval_batch_size: 512
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 512
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 10000
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Precision	Combined Score
1.0664	0.06	20	1.0651	0.4448	0.2739	0.4448	0.1978	0.3403
1.0423	0.12	40	1.0188	0.5034	0.4138	0.5034	0.4328	0.4633
1.0137	0.18	60	0.9871	0.5279	0.4596	0.5279	0.4219	0.4843
1.0027	0.24	80	0.9889	0.5308	0.4653	0.5308	0.4196	0.4866
0.9914	0.3	100	0.9763	0.5308	0.4666	0.5308	0.4176	0.4864
0.9826	0.36	120	0.9713	0.5388	0.4711	0.5388	0.4292	0.4945
0.9788	0.42	140	0.9766	0.5313	0.4674	0.5313	0.4186	0.4871
0.984	0.48	160	0.9590	0.5398	0.4751	0.5398	0.4243	0.4948
0.9694	0.54	180	0.9535	0.5423	0.4772	0.5423	0.4269	0.4972
0.9676	0.6	200	0.9274	0.5672	0.4991	0.5672	0.4467	0.5200
0.9753	0.66	220	0.9126	0.5736	0.5026	0.5736	0.4616	0.5279
0.9557	0.72	240	0.9053	0.5760	0.5069	0.5760	0.4532	0.5280
0.9508	0.78	260	0.9179	0.5767	0.5018	0.5767	0.4811	0.5341
0.9355	0.84	280	0.8937	0.5892	0.5183	0.5892	0.4662	0.5407
0.9	0.9	300	0.8469	0.6130	0.5579	0.6130	0.5855	0.5924
0.993	0.96	320	0.8615	0.6047	0.5352	0.6047	0.6175	0.5905
0.8527	1.02	340	0.7896	0.6439	0.6212	0.6439	0.6240	0.6333
0.966	1.08	360	1.0124	0.5316	0.4944	0.5316	0.6428	0.5501
0.8441	1.14	380	0.7911	0.6489	0.6371	0.6489	0.6584	0.6483
0.8226	1.2	400	0.7472	0.6700	0.6459	0.6700	0.6625	0.6621
0.7948	1.26	420	0.7664	0.6581	0.6095	0.6581	0.6796	0.6513
0.7428	1.32	440	0.6994	0.6992	0.6533	0.6992	0.7104	0.6905
0.7109	1.38	460	0.6511	0.7284	0.6890	0.7284	0.7345	0.7201
0.6882	1.44	480	0.5988	0.7577	0.7336	0.7577	0.7570	0.7515
0.7296	1.5	500	0.5993	0.7564	0.7669	0.7564	0.7902	0.7675
0.5677	1.57	520	0.5068	0.8126	0.7942	0.8126	0.8225	0.8105
0.5096	1.63	540	0.4273	0.8520	0.8449	0.8520	0.8511	0.8500
0.4452	1.69	560	0.3796	0.8722	0.8699	0.8722	0.8703	0.8711
0.3836	1.75	580	0.3855	0.8757	0.8758	0.8757	0.8778	0.8762
0.3783	1.81	600	0.3586	0.8894	0.8883	0.8894	0.8885	0.8889
0.3496	1.87	620	0.3210	0.8972	0.8973	0.8972	0.8974	0.8973
0.3585	1.93	640	0.3006	0.9035	0.9031	0.9035	0.9033	0.9034
0.345	1.99	660	0.3054	0.9014	0.9025	0.9014	0.9052	0.9026
0.3327	2.05	680	0.3174	0.8913	0.8866	0.8913	0.8955	0.8912
0.2962	2.11	700	0.2770	0.9122	0.9125	0.9122	0.9130	0.9125
0.3032	2.17	720	0.2979	0.9062	0.9055	0.9062	0.9093	0.9068
0.27	2.23	740	0.2973	0.8998	0.8971	0.8998	0.9045	0.9003
0.2912	2.29	760	0.2467	0.9222	0.9221	0.9222	0.9223	0.9222
0.2412	2.35	780	0.2761	0.9113	0.9128	0.9113	0.9173	0.9132
0.2746	2.41	800	0.2410	0.9260	0.9257	0.9260	0.9260	0.9259
0.2637	2.47	820	0.2447	0.9221	0.9213	0.9221	0.9228	0.9221
0.2605	2.53	840	0.2475	0.9237	0.9232	0.9237	0.9254	0.9240
0.2517	2.59	860	0.2590	0.9265	0.9259	0.9265	0.9272	0.9265
0.2453	2.65	880	0.2248	0.9300	0.9305	0.9300	0.9315	0.9305
0.2247	2.71	900	0.2285	0.9273	0.9281	0.9273	0.9299	0.9282
0.2402	2.77	920	0.2304	0.9306	0.9310	0.9306	0.9317	0.9310
0.2033	2.83	940	0.2228	0.9319	0.9316	0.9319	0.9325	0.9320
0.2315	2.89	960	0.2275	0.9271	0.9281	0.9271	0.9311	0.9283
0.2231	2.95	980	0.2115	0.9343	0.9345	0.9343	0.9350	0.9345
0.2061	3.01	1000	0.2156	0.9355	0.9356	0.9355	0.9361	0.9357
0.238	3.07	1020	0.2359	0.9252	0.9262	0.9252	0.9294	0.9265
0.1959	3.13	1040	0.2052	0.9343	0.9343	0.9343	0.9351	0.9345
0.2013	3.19	1060	0.2114	0.9337	0.9336	0.9337	0.9349	0.9340
0.2055	3.25	1080	0.1985	0.9354	0.9354	0.9354	0.9361	0.9356
0.1851	3.31	1100	0.2098	0.9340	0.9338	0.9340	0.9352	0.9342
0.2091	3.37	1120	0.2002	0.9350	0.9347	0.9350	0.9355	0.9350
0.1933	3.43	1140	0.2045	0.9367	0.9371	0.9367	0.9377	0.9370
0.2011	3.49	1160	0.2072	0.9350	0.9345	0.9350	0.9353	0.9349
0.2085	3.55	1180	0.2124	0.9327	0.9337	0.9327	0.9362	0.9338
0.1991	3.61	1200	0.1880	0.9396	0.9398	0.9396	0.9406	0.9399
0.1972	3.67	1220	0.2123	0.9347	0.9344	0.9347	0.9351	0.9347
0.1905	3.73	1240	0.2056	0.9351	0.9352	0.9351	0.9368	0.9355
0.1974	3.79	1260	0.2432	0.9285	0.9280	0.9285	0.9308	0.9289
0.1831	3.85	1280	0.1881	0.9392	0.9390	0.9392	0.9402	0.9394
0.1883	3.91	1300	0.1948	0.9413	0.9415	0.9413	0.9423	0.9416
0.2023	3.97	1320	0.2041	0.9335	0.9343	0.9335	0.9369	0.9346
0.1694	4.03	1340	0.2007	0.9360	0.9360	0.9360	0.9372	0.9363
0.1727	4.09	1360	0.1990	0.9411	0.9414	0.9411	0.9422	0.9414
0.1666	4.15	1380	0.1952	0.9397	0.9399	0.9397	0.9403	0.9399
0.2011	4.21	1400	0.1953	0.9384	0.9388	0.9384	0.9401	0.9389
0.1798	4.27	1420	0.2032	0.9325	0.9318	0.9325	0.9345	0.9328
0.1808	4.33	1440	0.2085	0.9308	0.9319	0.9308	0.9357	0.9323
0.1552	4.39	1460	0.2010	0.9371	0.9377	0.9371	0.9391	0.9377
0.1608	4.45	1480	0.2090	0.9342	0.9349	0.9342	0.9372	0.9351
0.1683	4.51	1500	0.2005	0.9374	0.9371	0.9374	0.9384	0.9376
0.1484	4.57	1520	0.1893	0.9380	0.9386	0.9380	0.9402	0.9387
0.1561	4.64	1540	0.1922	0.9401	0.9405	0.9401	0.9417	0.9406
0.1753	4.7	1560	0.1905	0.9416	0.9418	0.9416	0.9428	0.9419
0.1668	4.76	1580	0.1767	0.9420	0.9419	0.9420	0.9426	0.9421
0.1651	4.82	1600	0.1772	0.9429	0.9429	0.9429	0.9431	0.9429
0.1601	4.88	1620	0.1778	0.9433	0.9434	0.9433	0.9436	0.9434
0.1686	4.94	1640	0.1943	0.9414	0.9415	0.9414	0.9423	0.9416
0.1342	5.0	1660	0.1946	0.9395	0.9398	0.9395	0.9413	0.9400

Framework versions

• Transformers 4.34.1
• Pytorch 2.1.0
• Datasets 2.14.5
• Tokenizers 0.14.1