bert_sentence_classifier

This model is a fine-tuned version of bert-large-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 3.0040
F1: 0.6123
Precision: 0.6123
Recall: 0.6123

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Precision	Recall
2.0049	0.04	500	1.5854	0.5693	0.5693	0.5693
1.552	0.07	1000	1.4428	0.6131	0.6131	0.6131
1.502	0.11	1500	1.3977	0.6213	0.6213	0.6213
1.4515	0.14	2000	1.3926	0.6200	0.6200	0.6200
1.43	0.18	2500	1.3553	0.6350	0.6350	0.6350
1.413	0.21	3000	1.3461	0.6346	0.6346	0.6346
1.4109	0.25	3500	1.3199	0.6496	0.6496	0.6496
1.3853	0.28	4000	1.3338	0.6406	0.6406	0.6406
1.3788	0.32	4500	1.3306	0.6471	0.6471	0.6471
1.3585	0.35	5000	1.3295	0.6410	0.6410	0.6410
1.356	0.39	5500	1.3025	0.6441	0.6441	0.6441
1.3534	0.42	6000	1.3197	0.6406	0.6406	0.6406
1.3324	0.46	6500	1.2932	0.6436	0.6436	0.6436
1.3563	0.49	7000	1.3202	0.6488	0.6488	0.6488
1.3121	0.53	7500	1.3024	0.6428	0.6428	0.6428
1.3092	0.56	8000	1.3142	0.6419	0.6419	0.6419
1.3769	0.6	8500	1.2974	0.6441	0.6441	0.6441
1.3487	0.63	9000	1.2882	0.6556	0.6556	0.6556
1.3475	0.67	9500	1.2928	0.6441	0.6441	0.6441
1.3038	0.7	10000	1.2846	0.6488	0.6488	0.6488
1.3371	0.74	10500	1.2894	0.6591	0.6591	0.6591
1.3222	0.77	11000	1.2745	0.6535	0.6535	0.6535
1.2983	0.81	11500	1.2832	0.6526	0.6526	0.6526
1.3505	0.84	12000	1.2812	0.6531	0.6531	0.6531
1.2752	0.88	12500	1.2629	0.6578	0.6578	0.6578
1.3115	0.91	13000	1.2787	0.6453	0.6453	0.6453
1.3353	0.95	13500	1.2707	0.6539	0.6539	0.6539
1.2982	0.98	14000	1.2618	0.6569	0.6569	0.6569
1.1885	1.02	14500	1.2999	0.6544	0.6544	0.6544
1.1339	1.05	15000	1.3086	0.6458	0.6458	0.6458
1.0661	1.09	15500	1.2871	0.6582	0.6582	0.6582
1.109	1.12	16000	1.2800	0.6608	0.6608	0.6608
1.0305	1.16	16500	1.3098	0.6604	0.6604	0.6604
1.0855	1.19	17000	1.2968	0.6587	0.6587	0.6587
1.0933	1.23	17500	1.3075	0.6509	0.6509	0.6509
1.1229	1.26	18000	1.3018	0.6496	0.6496	0.6496
1.1043	1.3	18500	1.2832	0.6565	0.6565	0.6565
1.1344	1.33	19000	1.2825	0.6591	0.6591	0.6591
1.1467	1.37	19500	1.2797	0.6642	0.6642	0.6642
1.0596	1.4	20000	1.2841	0.6522	0.6522	0.6522
1.1286	1.44	20500	1.2912	0.6544	0.6544	0.6544
1.1219	1.47	21000	1.3143	0.6509	0.6509	0.6509
1.1339	1.51	21500	1.3021	0.6539	0.6539	0.6539
1.1091	1.54	22000	1.2738	0.6625	0.6625	0.6625
1.1403	1.58	22500	1.2822	0.6548	0.6548	0.6548
1.146	1.61	23000	1.2724	0.6587	0.6587	0.6587
1.1237	1.65	23500	1.2757	0.6569	0.6569	0.6569
1.1453	1.68	24000	1.2985	0.6535	0.6535	0.6535
1.1309	1.72	24500	1.2876	0.6578	0.6578	0.6578
1.1494	1.75	25000	1.2892	0.6552	0.6552	0.6552
1.1571	1.79	25500	1.2806	0.6548	0.6548	0.6548
1.0766	1.82	26000	1.2889	0.6509	0.6509	0.6509
1.1416	1.86	26500	1.2673	0.6599	0.6599	0.6599
1.1179	1.89	27000	1.2919	0.6501	0.6501	0.6501
1.0838	1.93	27500	1.3198	0.6488	0.6488	0.6488
1.1426	1.96	28000	1.2766	0.6561	0.6561	0.6561
1.1559	2.0	28500	1.2839	0.6561	0.6561	0.6561
0.8783	2.03	29000	1.3377	0.6509	0.6509	0.6509
0.8822	2.07	29500	1.3813	0.6501	0.6501	0.6501
0.8823	2.1	30000	1.3738	0.6514	0.6514	0.6514
0.9094	2.14	30500	1.3667	0.6522	0.6522	0.6522
0.8828	2.17	31000	1.3654	0.6582	0.6582	0.6582
0.8489	2.21	31500	1.3404	0.6556	0.6556	0.6556
0.8719	2.24	32000	1.4173	0.6393	0.6393	0.6393
0.8926	2.28	32500	1.4026	0.6535	0.6535	0.6535
0.871	2.31	33000	1.4133	0.6428	0.6428	0.6428
0.9047	2.35	33500	1.3915	0.6449	0.6449	0.6449
0.8621	2.38	34000	1.4109	0.6483	0.6483	0.6483
0.8978	2.42	34500	1.3675	0.6471	0.6471	0.6471
0.8808	2.45	35000	1.3826	0.6522	0.6522	0.6522
0.9299	2.49	35500	1.3673	0.6535	0.6535	0.6535
0.8546	2.52	36000	1.4034	0.6518	0.6518	0.6518
0.8855	2.56	36500	1.3763	0.6458	0.6458	0.6458
0.8996	2.59	37000	1.3930	0.6539	0.6539	0.6539
0.8889	2.63	37500	1.3966	0.6471	0.6471	0.6471
0.8811	2.66	38000	1.4131	0.6475	0.6475	0.6475
0.9129	2.7	38500	1.3816	0.6445	0.6445	0.6445
0.8708	2.73	39000	1.4354	0.6492	0.6492	0.6492
0.8667	2.77	39500	1.4076	0.6380	0.6380	0.6380
0.9139	2.8	40000	1.4200	0.6423	0.6423	0.6423
0.9035	2.84	40500	1.3913	0.6462	0.6462	0.6462
0.9312	2.87	41000	1.3806	0.6449	0.6449	0.6449
0.9382	2.91	41500	1.4064	0.6522	0.6522	0.6522
0.8765	2.95	42000	1.4146	0.6380	0.6380	0.6380
0.8801	2.98	42500	1.3898	0.6445	0.6445	0.6445
0.7988	3.02	43000	1.4740	0.6436	0.6436	0.6436
0.6752	3.05	43500	1.5622	0.6372	0.6372	0.6372
0.649	3.09	44000	1.6055	0.6359	0.6359	0.6359
0.669	3.12	44500	1.5736	0.6380	0.6380	0.6380
0.7189	3.16	45000	1.5832	0.6346	0.6346	0.6346
0.6724	3.19	45500	1.6194	0.6260	0.6260	0.6260
0.7139	3.23	46000	1.5966	0.6359	0.6359	0.6359
0.6985	3.26	46500	1.5803	0.6342	0.6342	0.6342
0.6503	3.3	47000	1.6485	0.6376	0.6376	0.6376
0.6879	3.33	47500	1.5959	0.6325	0.6325	0.6325
0.7342	3.37	48000	1.5534	0.6389	0.6389	0.6389
0.6838	3.4	48500	1.5807	0.6337	0.6337	0.6337
0.7295	3.44	49000	1.6192	0.6372	0.6372	0.6372
0.7044	3.47	49500	1.6618	0.6346	0.6346	0.6346
0.7071	3.51	50000	1.6255	0.6342	0.6342	0.6342
0.7055	3.54	50500	1.5584	0.6363	0.6363	0.6363
0.6781	3.58	51000	1.5948	0.6376	0.6376	0.6376
0.7004	3.61	51500	1.6311	0.6320	0.6320	0.6320
0.715	3.65	52000	1.5972	0.6423	0.6423	0.6423
0.7399	3.68	52500	1.6402	0.6325	0.6325	0.6325
0.6972	3.72	53000	1.6186	0.6406	0.6406	0.6406
0.7219	3.75	53500	1.5945	0.6359	0.6359	0.6359
0.763	3.79	54000	1.5900	0.6380	0.6380	0.6380
0.7196	3.82	54500	1.6218	0.6320	0.6320	0.6320
0.7682	3.86	55000	1.5538	0.6372	0.6372	0.6372
0.6949	3.89	55500	1.6209	0.6295	0.6295	0.6295
0.7461	3.93	56000	1.6237	0.6316	0.6316	0.6316
0.7295	3.96	56500	1.6011	0.6333	0.6333	0.6333
0.6846	4.0	57000	1.6899	0.6312	0.6312	0.6312
0.556	4.03	57500	1.7783	0.6303	0.6303	0.6303
0.5276	4.07	58000	1.8985	0.6260	0.6260	0.6260
0.5576	4.1	58500	1.8263	0.6264	0.6264	0.6264
0.5303	4.14	59000	1.8411	0.6316	0.6316	0.6316
0.5574	4.17	59500	1.8353	0.6286	0.6286	0.6286
0.5468	4.21	60000	1.9252	0.6286	0.6286	0.6286
0.532	4.24	60500	1.8903	0.6295	0.6295	0.6295
0.5329	4.28	61000	1.9416	0.6252	0.6252	0.6252
0.5539	4.31	61500	1.9149	0.6260	0.6260	0.6260
0.5661	4.35	62000	1.9074	0.6286	0.6286	0.6286
0.5502	4.38	62500	2.0259	0.6316	0.6316	0.6316
0.5658	4.42	63000	1.9049	0.6256	0.6256	0.6256
0.5958	4.45	63500	1.9252	0.6166	0.6166	0.6166
0.5972	4.49	64000	1.8518	0.6286	0.6286	0.6286
0.5964	4.52	64500	1.8793	0.6234	0.6234	0.6234
0.5506	4.56	65000	1.9218	0.6346	0.6346	0.6346
0.5516	4.59	65500	1.8957	0.6389	0.6389	0.6389
0.5777	4.63	66000	1.9603	0.6295	0.6295	0.6295
0.5953	4.66	66500	1.8605	0.6252	0.6252	0.6252
0.5797	4.7	67000	1.8797	0.6320	0.6320	0.6320
0.5836	4.73	67500	1.9320	0.6260	0.6260	0.6260
0.6019	4.77	68000	1.8465	0.6239	0.6239	0.6239
0.6099	4.8	68500	1.9481	0.6299	0.6299	0.6299
0.6064	4.84	69000	1.9033	0.6307	0.6307	0.6307
0.5836	4.87	69500	1.8878	0.6234	0.6234	0.6234
0.5766	4.91	70000	1.8860	0.6277	0.6277	0.6277
0.623	4.94	70500	1.8033	0.6303	0.6303	0.6303
0.596	4.98	71000	1.9038	0.6333	0.6333	0.6333
0.537	5.01	71500	2.0795	0.6234	0.6234	0.6234
0.4663	5.05	72000	2.0325	0.6217	0.6217	0.6217
0.4173	5.08	72500	2.2377	0.6273	0.6273	0.6273
0.4521	5.12	73000	2.1218	0.6217	0.6217	0.6217
0.4243	5.15	73500	2.2731	0.6204	0.6204	0.6204
0.4672	5.19	74000	2.2111	0.6247	0.6247	0.6247
0.4884	5.22	74500	2.1027	0.6226	0.6226	0.6226
0.4314	5.26	75000	2.2218	0.6230	0.6230	0.6230
0.4581	5.29	75500	2.2036	0.6264	0.6264	0.6264
0.4245	5.33	76000	2.2419	0.6200	0.6200	0.6200
0.4391	5.36	76500	2.1762	0.6187	0.6187	0.6187
0.4672	5.4	77000	2.2779	0.6179	0.6179	0.6179
0.4821	5.43	77500	2.2881	0.6187	0.6187	0.6187
0.4872	5.47	78000	2.2406	0.6119	0.6119	0.6119
0.4584	5.5	78500	2.3521	0.6209	0.6209	0.6209
0.4774	5.54	79000	2.2522	0.6174	0.6174	0.6174
0.5151	5.57	79500	2.2233	0.6140	0.6140	0.6140
0.493	5.61	80000	2.2333	0.6256	0.6256	0.6256
0.4846	5.64	80500	2.1891	0.6200	0.6200	0.6200
0.478	5.68	81000	2.3159	0.6196	0.6196	0.6196
0.4851	5.71	81500	2.2356	0.6234	0.6234	0.6234
0.4902	5.75	82000	2.3525	0.6222	0.6222	0.6222
0.4992	5.79	82500	2.2111	0.6067	0.6067	0.6067
0.4799	5.82	83000	2.2650	0.6131	0.6131	0.6131
0.4849	5.86	83500	2.2628	0.6204	0.6204	0.6204
0.4772	5.89	84000	2.2711	0.6174	0.6174	0.6174
0.5465	5.93	84500	2.2793	0.6144	0.6144	0.6144
0.4466	5.96	85000	2.2369	0.6166	0.6166	0.6166
0.4885	6.0	85500	2.1963	0.6217	0.6217	0.6217
0.3862	6.03	86000	2.4233	0.6174	0.6174	0.6174
0.3738	6.07	86500	2.4405	0.6191	0.6191	0.6191
0.349	6.1	87000	2.4512	0.6161	0.6161	0.6161
0.3659	6.14	87500	2.5251	0.6226	0.6226	0.6226
0.3365	6.17	88000	2.5326	0.6217	0.6217	0.6217
0.3336	6.21	88500	2.4413	0.6179	0.6179	0.6179
0.3632	6.24	89000	2.6415	0.6114	0.6114	0.6114
0.3584	6.28	89500	2.5388	0.6179	0.6179	0.6179
0.3891	6.31	90000	2.6418	0.6123	0.6123	0.6123
0.3805	6.35	90500	2.6223	0.6127	0.6127	0.6127
0.363	6.38	91000	2.5399	0.6131	0.6131	0.6131
0.3723	6.42	91500	2.6033	0.6187	0.6187	0.6187
0.3808	6.45	92000	2.5281	0.6243	0.6243	0.6243
0.3921	6.49	92500	2.5814	0.6007	0.6007	0.6007
0.3763	6.52	93000	2.6656	0.6058	0.6058	0.6058
0.3921	6.56	93500	2.4935	0.6084	0.6084	0.6084
0.3737	6.59	94000	2.7270	0.6166	0.6166	0.6166
0.3766	6.63	94500	2.5289	0.6217	0.6217	0.6217
0.4439	6.66	95000	2.6161	0.6222	0.6222	0.6222
0.4166	6.7	95500	2.5298	0.6123	0.6123	0.6123
0.4064	6.73	96000	2.5952	0.6183	0.6183	0.6183
0.4253	6.77	96500	2.4567	0.6127	0.6127	0.6127
0.3754	6.8	97000	2.5473	0.6131	0.6131	0.6131
0.3993	6.84	97500	2.5563	0.6161	0.6161	0.6161
0.3802	6.87	98000	2.6585	0.6076	0.6076	0.6076
0.4504	6.91	98500	2.5700	0.6127	0.6127	0.6127
0.3832	6.94	99000	2.5983	0.6174	0.6174	0.6174
0.4212	6.98	99500	2.6137	0.6110	0.6110	0.6110
0.3253	7.01	100000	2.8467	0.6024	0.6024	0.6024
0.2553	7.05	100500	2.7412	0.6063	0.6063	0.6063
0.2771	7.08	101000	2.8670	0.6101	0.6101	0.6101
0.2733	7.12	101500	2.8536	0.6166	0.6166	0.6166
0.2972	7.15	102000	2.8254	0.6161	0.6161	0.6161
0.2893	7.19	102500	3.0228	0.6058	0.6058	0.6058
0.3104	7.22	103000	2.8617	0.6011	0.6011	0.6011
0.3019	7.26	103500	3.0106	0.6131	0.6131	0.6131
0.3143	7.29	104000	3.0189	0.6088	0.6088	0.6088
0.3054	7.33	104500	3.0291	0.6063	0.6063	0.6063
0.3145	7.36	105000	3.0166	0.6106	0.6106	0.6106
0.2913	7.4	105500	3.0480	0.6174	0.6174	0.6174
0.3159	7.43	106000	2.9714	0.6084	0.6084	0.6084
0.3216	7.47	106500	2.9359	0.6187	0.6187	0.6187
0.2982	7.5	107000	3.0509	0.6084	0.6084	0.6084
0.2952	7.54	107500	2.9428	0.6076	0.6076	0.6076
0.304	7.57	108000	3.0155	0.6071	0.6071	0.6071
0.2896	7.61	108500	3.0276	0.6196	0.6196	0.6196
0.3226	7.64	109000	2.9331	0.6097	0.6097	0.6097
0.299	7.68	109500	2.9671	0.6050	0.6050	0.6050
0.3079	7.71	110000	2.9394	0.6093	0.6093	0.6093
0.3064	7.75	110500	2.8690	0.6110	0.6110	0.6110
0.3423	7.78	111000	2.9095	0.6183	0.6183	0.6183
0.3085	7.82	111500	2.9967	0.6260	0.6260	0.6260
0.3071	7.85	112000	2.9429	0.6127	0.6127	0.6127
0.3197	7.89	112500	3.0123	0.6157	0.6157	0.6157
0.3361	7.92	113000	2.9832	0.6170	0.6170	0.6170
0.3252	7.96	113500	3.0174	0.6071	0.6071	0.6071
0.2802	7.99	114000	3.0040	0.6123	0.6123	0.6123

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1

juancavallotti/bert_sentence_classifier

bert_sentence_classifier

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Seed this model