ner-portuguese-br-bert-cased

This model aims to help reduce the need for models in Portuguese.

How to use:

from transformers import BertForTokenClassification, DistilBertTokenizerFast, pipeline

model = BertForTokenClassification.from_pretrained('rhaymison/ner-portuguese-br-bert-cased')
tokenizer = DistilBertTokenizerFast.from_pretrained('rhaymison/ner-portuguese-br-bert-cased'
                                                    , model_max_length=512
                                                    , do_lower_case=False
                                                    )

nlp = pipeline('ner', model=model, tokenizer=tokenizer, grouped_entities=True)

result = nlp(f"""
A passagem de uma frente fria pelo Rio Grande do Sul e Santa Catarina mantém o tempo instável,
e chove a qualquer hora nos dois estados. Há risco de temporais no sul e leste gaúcho.
No Paraná segue quente, e pancadas de chuva ocorrem a partir da tarde, também com risco de temporais.
""")

###output

[{'entity_group': 'LOC',
  'score': 0.99812114,
  'word': 'Rio Grande do Sul',
  'start': 36,
  'end': 53},
 {'entity_group': 'LOC',
  'score': 0.99795854,
  'word': 'Santa Catarina',
  'start': 56,
  'end': 70},
 {'entity_group': 'LOC',
  'score': 0.997009,
  'word': 'Paraná',
  'start': 186,
  'end': 192}]

He has various named classes. Follow the list below:

• O: 0
• B-ANIM: 1
• B-BIO: 2
• B-CEL: 3
• B-DIS: 4
• B-EVE: 5
• B-FOOD: 6
• B-INST: 7
• B-LOC: 8
• B-MEDIA: 9
• B-MYTH: 10
• B-ORG: 11
• B-PER: 12
• B-PLANT: 13
• B-TIME: 14
• B-VEHI: 15
• I-ANIM: 16
• I-BIO: 17
• I-CEL: 18
• I-DIS: 19
• I-EVE: 20
• I-FOOD: 21
• I-INST: 22
• I-LOC: 23
• I-MEDIA: 24
• I-MYTH: 25
• I-ORG: 26
• I-PER: 27
• I-PLANT: 28
• I-TIME: 29
• I-VEHI: 30

This model is a fine-tuned version of google-bert/bert-base-cased on the MultNERD dataset. It achieves the following results on the evaluation set:

• Loss: 0.0618
• Precision: 0.8965
• Recall: 0.8815
• F1: 0.8889
• Accuracy: 0.9810

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: 4
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3792	0.03	500	0.2062	0.6752	0.6537	0.6642	0.9522
0.1822	0.06	1000	0.1587	0.7685	0.7267	0.7470	0.9618
0.152	0.08	1500	0.1407	0.7932	0.7675	0.7802	0.9663
0.1385	0.11	2000	0.1240	0.8218	0.7863	0.8037	0.9693
0.1216	0.14	2500	0.1129	0.8529	0.7850	0.8175	0.9710
0.1192	0.17	3000	0.1059	0.8520	0.7917	0.8208	0.9717
0.1165	0.2	3500	0.1053	0.8373	0.8071	0.8220	0.9717
0.0997	0.23	4000	0.0978	0.8434	0.8212	0.8322	0.9729
0.0938	0.25	4500	0.0963	0.8393	0.8313	0.8353	0.9736
0.0921	0.28	5000	0.0867	0.8593	0.8365	0.8478	0.9750
0.0943	0.31	5500	0.0846	0.8704	0.8268	0.8480	0.9754
0.0921	0.34	6000	0.0832	0.8556	0.8384	0.8469	0.9750
0.0936	0.37	6500	0.0802	0.8726	0.8361	0.8540	0.9760
0.0854	0.39	7000	0.0780	0.8749	0.8452	0.8598	0.9767
0.082	0.42	7500	0.0751	0.8812	0.8472	0.8639	0.9773
0.0761	0.45	8000	0.0745	0.8752	0.8571	0.8660	0.9772
0.0799	0.48	8500	0.0752	0.8635	0.8530	0.8582	0.9767
0.0728	0.51	9000	0.0746	0.8938	0.8398	0.8660	0.9780
0.0787	0.54	9500	0.0715	0.8791	0.8552	0.8670	0.9780
0.0721	0.56	10000	0.0707	0.8822	0.8598	0.8709	0.9785
0.0729	0.59	10500	0.0682	0.8775	0.8743	0.8759	0.9790
0.0707	0.62	11000	0.0686	0.8797	0.8696	0.8746	0.9789
0.0726	0.65	11500	0.0683	0.8944	0.8497	0.8715	0.9788
0.0689	0.68	12000	0.0667	0.8931	0.8609	0.8767	0.9795
0.0735	0.7	12500	0.0673	0.8742	0.8815	0.8779	0.9791
0.0725	0.73	13000	0.0666	0.8849	0.8713	0.8781	0.9796
0.0684	0.76	13500	0.0656	0.8881	0.8728	0.8804	0.9799
0.0736	0.79	14000	0.0644	0.8948	0.8677	0.8811	0.9800
0.0663	0.82	14500	0.0644	0.8844	0.8764	0.8803	0.9798
0.0652	0.85	15000	0.0645	0.8778	0.8845	0.8812	0.9797
0.0672	0.87	15500	0.0644	0.8788	0.8807	0.8797	0.9796
0.0625	0.9	16000	0.0630	0.8889	0.8819	0.8854	0.9804
0.0712	0.93	16500	0.0621	0.8913	0.8818	0.8866	0.9806
0.0629	0.96	17000	0.0618	0.8965	0.8815	0.8889	0.9810
0.0649	0.99	17500	0.0618	0.8953	0.8806	0.8879	0.9809

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2

Comments

Any idea, help or report will always be welcome.

email: [email protected]