finetuned-bert-categories-estimation

diegoale1248/finetuned-bert-categories-estimation

finetuned-bert-categories-estimation is a text classification model from diegoale1248. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Dec 11, 2023
Updated: Feb 22, 2024
SHA: c3117f2b

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Base models

neuralmind/bert-base-portuguese-cased

Model card

From the Hugging Face model README

finetuned-bert-categories-estimation

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

Loss: 0.4080
F1: 0.9054
Accuracy: 0.9277

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: 5e-05
train_batch_size: 32
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Accuracy
4.3517	0.13	100	3.6120	0.0934	0.3599
3.2593	0.25	200	2.7209	0.1848	0.5085
2.584	0.38	300	2.1874	0.2784	0.5952
2.1208	0.51	400	1.8393	0.3656	0.6504
1.7726	0.63	500	1.5560	0.4633	0.7121
1.5799	0.76	600	1.3686	0.5185	0.7442
1.3384	0.89	700	1.2079	0.5896	0.7759
1.2751	1.01	800	1.0736	0.6044	0.7941
1.0223	1.14	900	0.9892	0.6353	0.8089
0.9095	1.27	1000	0.9277	0.6699	0.8157
0.8496	1.39	1100	0.8370	0.6973	0.8311
0.7735	1.52	1200	0.7878	0.7093	0.8349
0.7058	1.65	1300	0.7299	0.7239	0.8481
0.6545	1.77	1400	0.6823	0.7444	0.8563
0.6652	1.9	1500	0.6623	0.7547	0.8609
0.5905	2.03	1600	0.6079	0.7660	0.8663
0.4679	2.15	1700	0.5910	0.7867	0.8696
0.4415	2.28	1800	0.5668	0.8034	0.8785
0.4377	2.41	1900	0.5580	0.8068	0.8796
0.4262	2.53	2000	0.5366	0.8054	0.8815
0.4272	2.66	2100	0.5094	0.8189	0.8880
0.3979	2.79	2200	0.4966	0.8229	0.8898
0.3763	2.92	2300	0.4838	0.8349	0.8950
0.366	3.04	2400	0.4742	0.8340	0.8950
0.2686	3.17	2500	0.4591	0.8365	0.8966
0.2735	3.3	2600	0.4676	0.8393	0.8958
0.2582	3.42	2700	0.4263	0.8580	0.9025
0.2451	3.55	2800	0.4383	0.8526	0.8988
0.2626	3.68	2900	0.4420	0.8554	0.9018
0.248	3.8	3000	0.4153	0.8658	0.9080
0.2634	3.93	3100	0.4082	0.8666	0.9088
0.2	4.06	3200	0.4162	0.8716	0.9090
0.1717	4.18	3300	0.4032	0.8748	0.9117
0.19	4.31	3400	0.4019	0.8747	0.9117
0.1507	4.44	3500	0.4118	0.8789	0.9139
0.16	4.56	3600	0.4107	0.8815	0.9139
0.1716	4.69	3700	0.4105	0.8826	0.9132
0.1545	4.82	3800	0.3945	0.8850	0.9180
0.1628	4.94	3900	0.3974	0.8907	0.9194
0.1123	5.07	4000	0.4060	0.8828	0.9166
0.0988	5.2	4100	0.4037	0.8847	0.9167
0.1065	5.32	4200	0.3959	0.8895	0.9201
0.1018	5.45	4300	0.4040	0.8875	0.9183
0.1091	5.58	4400	0.4044	0.8908	0.9199
0.1041	5.7	4500	0.3937	0.8943	0.9218
0.1154	5.83	4600	0.3981	0.8956	0.9205
0.0932	5.96	4700	0.3940	0.8967	0.9223
0.0835	6.08	4800	0.3914	0.8967	0.9224
0.065	6.21	4900	0.3905	0.8922	0.9215
0.0634	6.34	5000	0.3999	0.8924	0.9215
0.0618	6.46	5100	0.4013	0.8966	0.9226
0.0678	6.59	5200	0.3985	0.9004	0.9242
0.0666	6.72	5300	0.3892	0.8993	0.9239
0.0564	6.84	5400	0.4026	0.8986	0.9228
0.0704	6.97	5500	0.4011	0.9004	0.9236
0.0508	7.1	5600	0.4035	0.8987	0.9234
0.0395	7.22	5700	0.4131	0.8979	0.9216
0.0363	7.35	5800	0.4112	0.9022	0.9243
0.0443	7.48	5900	0.4079	0.9039	0.9251
0.0383	7.6	6000	0.4152	0.9031	0.9248
0.0384	7.73	6100	0.4075	0.9037	0.9258
0.0414	7.86	6200	0.4087	0.9035	0.9256
0.0449	7.98	6300	0.4066	0.9060	0.9262
0.0246	8.11	6400	0.4091	0.9041	0.9258
0.0275	8.24	6500	0.4085	0.9035	0.9262
0.0256	8.37	6600	0.4077	0.9048	0.9269
0.0307	8.49	6700	0.4040	0.9082	0.9285
0.0294	8.62	6800	0.4057	0.9067	0.9283
0.0226	8.75	6900	0.4069	0.9054	0.9274
0.0218	8.87	7000	0.4090	0.9053	0.9278
0.0333	9.0	7100	0.4053	0.9075	0.9286
0.0182	9.13	7200	0.4071	0.9063	0.9277
0.0176	9.25	7300	0.4058	0.9053	0.9278
0.0187	9.38	7400	0.4074	0.9060	0.9280
0.0185	9.51	7500	0.4069	0.9059	0.9278
0.0135	9.63	7600	0.4067	0.9049	0.9275
0.0118	9.76	7700	0.4076	0.9039	0.9267
0.0163	9.89	7800	0.4081	0.9050	0.9275

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0

Task: Text Classification
Library: transformers
Type: bert
License: mit
Created: Dec 11, 2023
Updated: Feb 22, 2024