Ner Bert Base Cased Pt Lenerbr

🚀 (BERT base) 葡萄牙語法律領域命名實體識別（NER）模型

本模型是一個用於葡萄牙語法律領域的命名實體識別（NER）模型，通過在 LeNER_br 數據集上微調 pierreguillou/bert-base-cased-pt-lenerbr 模型而得到。該模型能夠有效識別法律文本中的各類實體，為法律信息處理提供了有力支持。

🚀 快速開始

本模型可直接在本頁面的小部件中進行測試，也可以使用 NER 應用程序 對兩個 BERT 模型（基礎版和大版本）在法律 LeNER-Br 數據集上的 NER 任務進行比較。

✨ 主要特性

• 專業領域適配：針對葡萄牙語法律領域進行優化，能準確識別法律文本中的實體。
• 性能表現良好：在驗證數據集上取得了較高的 F1、精確率、召回率和準確率。
• 可對比性：提供了與非專業語言模型微調後的 NER 模型的性能對比。

📦 安裝指南

# 安裝 PyTorch，請參考 https://pytorch.org/
# !pip install transformers 
from transformers import AutoModelForTokenClassification, AutoTokenizer
import torch

💻 使用示例

基礎用法

# 參數設置
model_name = "pierreguillou/ner-bert-base-cased-pt-lenerbr"
model = AutoModelForTokenClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

input_text = "Acrescento que não há de se falar em violação do artigo 114, § 3º, da Constituição Federal, posto que referido dispositivo revela-se impertinente, tratando da possibilidade de ajuizamento de dissídio coletivo pelo Ministério Público do Trabalho nos casos de greve em atividade essencial."

# 分詞
inputs = tokenizer(input_text, max_length=512, truncation=True, return_tensors="pt")
tokens = inputs.tokens()

# 獲取預測結果
outputs = model(**inputs).logits
predictions = torch.argmax(outputs, dim=2)

# 打印預測結果
for token, prediction in zip(tokens, predictions[0].numpy()):
    print((token, model.config.id2label[prediction]))

高級用法

# 可以使用 pipeline，但輸入序列的 max_length 似乎存在問題
!pip install transformers
import transformers
from transformers import pipeline

model_name = "pierreguillou/ner-bert-base-cased-pt-lenerbr"

ner = pipeline(
    "ner",
    model=model_name
) 

ner(input_text)

📚 詳細文檔

博客文章

可查看 NLP | Modelos e Web App para Reconhecimento de Entidade Nomeada (NER) no domínio jurídico brasileiro (29/12/2021) 瞭解更多信息。

模型指標

不同命名實體的驗證指標

Num examples = 1177

{'JURISPRUDENCIA': {'f1': 0.7016574585635359,
  'number': 657,
  'precision': 0.6422250316055625,
  'recall': 0.7732115677321156},
 'LEGISLACAO': {'f1': 0.8839681133746677,
  'number': 571,
  'precision': 0.8942652329749103,
  'recall': 0.8739054290718039},
 'LOCAL': {'f1': 0.8253968253968254,
  'number': 194,
  'precision': 0.7368421052631579,
  'recall': 0.9381443298969072},
 'ORGANIZACAO': {'f1': 0.8934049079754601,
  'number': 1340,
  'precision': 0.918769716088328,
  'recall': 0.8694029850746269},
 'PESSOA': {'f1': 0.982653539615565,
  'number': 1072,
  'precision': 0.9877474081055608,
  'recall': 0.9776119402985075},
 'TEMPO': {'f1': 0.9657657657657657,
  'number': 816,
  'precision': 0.9469964664310954,
  'recall': 0.9852941176470589},
 'overall_accuracy': 0.9725722644643211,
 'overall_f1': 0.8926146010186757,
 'overall_precision': 0.8810222036028488,
 'overall_recall': 0.9045161290322581}

🔧 技術細節

微調筆記本

微調的筆記本 (HuggingFace_Notebook_token_classification_NER_LeNER_Br.ipynb) 可在 GitHub 上找到。

超參數設置

批量大小、學習率等

• per_device_batch_size = 2
• gradient_accumulation_steps = 2
• learning_rate = 2e-5
• num_train_epochs = 10
• weight_decay = 0.01
• optimizer = AdamW
• betas = (0.9,0.999)
• epsilon = 1e-08
• lr_scheduler_type = linear
• seed = 7

保存模型和加載最佳模型

• save_total_limit = 2
• logging_steps = 300
• eval_steps = logging_steps
• evaluation_strategy = 'steps'
• logging_strategy = 'steps'
• save_strategy = 'steps'
• save_steps = logging_steps
• load_best_model_at_end = True
• fp16 = True

通過指標選擇最佳模型

• metric_for_best_model = 'eval_f1'
• greater_is_better = True

訓練結果

Num examples = 7828
Num Epochs = 10
Instantaneous batch size per device = 2
Total train batch size (w. parallel, distributed & accumulation) = 4
Gradient Accumulation steps = 2
Total optimization steps = 19570

Step	Training Loss Validation Loss      Precision     Recall  	 F1      	Accuracy
300	  0.127600	  0.178613	        0.722909	  0.741720	 0.732194	0.948802
600	  0.088200	  0.136965	        0.733636	  0.867742	 0.795074	0.963079
900	  0.078000	  0.128858	        0.791912	  0.838065	 0.814335	0.965243
1200 	0.077800      0.126345	        0.815400	  0.865376	 0.839645	0.967849
1500 	0.074100      0.148207	        0.779274	  0.895914	 0.833533	0.960184
1800 	0.059500      0.116634	        0.830829	  0.868172	 0.849090	0.969342
2100 	0.044500      0.208459	        0.887150	  0.816559	 0.850392	0.960535
2400 	0.029400      0.136352	        0.867821	  0.851398	 0.859531	0.970271
2700 	0.025000      0.165837	        0.814881	  0.878495	 0.845493	0.961235
3000 	0.038400      0.120629	        0.811719	  0.893763	 0.850768	0.971506
3300 	0.026200      0.175094	        0.823435	  0.882581	 0.851983	0.962957
3600 	0.025600      0.178438	        0.881095	  0.886022	 0.883551	0.963689
3900 	0.041000      0.134648	        0.789035	  0.916129	 0.847846	0.967681
4200 	0.026700      0.130178	        0.821275	  0.903226	 0.860303	0.972313
4500 	0.018500      0.139294	        0.844016	  0.875054	 0.859255	0.971140
4800 	0.020800      0.197811	        0.892504	  0.873118	 0.882705	0.965883
5100 	0.019300      0.161239	        0.848746	  0.888172	 0.868012	0.967849
5400 	0.024000      0.139131	        0.837507	  0.913333	 0.873778	0.970591
5700 	0.018400      0.157223	        0.899754	  0.864731	 0.881895	0.970210
6000 	0.023500      0.137022	        0.883018	  0.873333	 0.878149	0.973243
6300 	0.009300      0.181448	        0.840490	  0.900860	 0.869628	0.968290
6600 	0.019200      0.173125	        0.821316	  0.896559	 0.857290	0.966736
6900 	0.016100      0.143160	        0.789938	  0.904946	 0.843540	0.968245
7200 	0.017000      0.145755	        0.823274	  0.897634	 0.858848	0.969037
7500 	0.012100      0.159342	        0.825694	  0.883226	 0.853491	0.967468
7800 	0.013800      0.194886            0.861237	  0.859570	 0.860403	0.964771
8100 	0.008000      0.140271	        0.829914	  0.896129	 0.861752	0.971567
8400 	0.010300      0.143318	        0.826844	  0.908817	 0.865895	0.973578
8700 	0.015000      0.143392	        0.847336	  0.889247	 0.867786	0.973365
9000 	0.006000      0.143512	        0.847795	  0.905591	 0.875741	0.972892
9300 	0.011800      0.138747	        0.827133	  0.894194	 0.859357	0.971673
9600 	0.008500      0.159490	        0.837030	  0.909032	 0.871546	0.970028
9900 	0.010700      0.159249	        0.846692	  0.910968	 0.877655	0.970546
10200	0.008100	  0.170069  	      0.848288	  0.900645	 0.873683	0.969113
10500	0.004800	  0.183795	        0.860317	  0.899355	 0.879403	0.969570
10800	0.010700	  0.157024	        0.837838	  0.906667	 0.870894	0.971094
11100	0.003800	  0.164286	        0.845312	  0.880215	 0.862410	0.970744
11400	0.009700	  0.204025	        0.884294	  0.887527	 0.885907	0.968854
11700	0.008900	  0.162819	        0.829415	  0.887742	 0.857588	0.970530
12000	0.006400	  0.164296	        0.852666	  0.901075	 0.876202	0.971414
12300	0.007100	  0.143367	        0.852959	  0.895699	 0.873807	0.973669
12600	0.015800	  0.153383	        0.859224	  0.900430	 0.879345	0.972679
12900	0.006600	  0.173447	        0.869954	  0.899140	 0.884306	0.970927
13200	0.006800	  0.163234  	      0.856849	  0.897204	 0.876563	0.971795
13500	0.003200	  0.167164	        0.850867	  0.907957	 0.878485	0.971231
13800	0.003600	  0.148950  	      0.867801	  0.910538	 0.888656	0.976961
14100	0.003500	  0.155691  	      0.847621	  0.907957	 0.876752	0.974127
14400	0.003300	  0.157672	        0.846553	  0.911183	 0.877680	0.974584
14700	0.002500	  0.169965	        0.847804	  0.917634	 0.881338	0.973045
15000	0.003400	  0.177099  	      0.842199	  0.912473	 0.875929	0.971155
15300	0.006000	  0.164151  	      0.848928	  0.911183	 0.878954	0.973258
15600	0.002400	  0.174305	        0.847437	  0.906667	 0.876052	0.971765
15900	0.004100	  0.174561  	      0.852929	  0.907957	 0.879583	0.972907
16200	0.002600	  0.172626	        0.843263	  0.907097	 0.874016	0.972100
16500	0.002100	  0.185302	        0.841108	  0.907312	 0.872957	0.970485
16800	0.002900	  0.175638	        0.840557	  0.909247	 0.873554	0.971704
17100	0.001600	  0.178750	        0.857056	  0.906452	 0.881062	0.971765
17400	0.003900	  0.188910	        0.853619	  0.907957	 0.879950	0.970835
17700	0.002700	  0.180822	        0.864699	  0.907097	 0.885390	0.972283
18000	0.001300	  0.179974	        0.868150	  0.906237	 0.886785	0.973060

18300	0.000800	  0.188032	        0.881022	  0.904516	 0.892615	0.972572

18600	0.002700	  0.183266	        0.868601	  0.901290	 0.884644	0.972298
18900	0.001600	  0.180301	        0.862041	  0.903011	 0.882050	0.972344
19200	0.002300	  0.183432	        0.855370	  0.904301	 0.879155	0.971109
19500	0.001800	  0.183381	        0.854501	  0.904301	 0.878696	0.971186

📄 許可證

文檔未提及相關許可證信息。