dominguesm
commited on
Commit
•
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Parent(s):
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Initial
Browse files- README.md +83 -0
- config.json +62 -0
- pytorch_model.bin +3 -0
- runs/e3_lr2e-05/1665668964.0356135/events.out.tfevents.1665668964.9dd23284ee7b.94.1 +3 -0
- runs/e3_lr2e-05/1665669018.9886503/events.out.tfevents.1665669018.9dd23284ee7b.94.3 +3 -0
- runs/e3_lr2e-05/1665669243.3965552/events.out.tfevents.1665669243.9dd23284ee7b.798.1 +3 -0
- runs/e3_lr2e-05/1665669913.5734515/events.out.tfevents.1665669913.9dd23284ee7b.1054.1 +3 -0
- runs/e3_lr2e-05/1665670039.231488/events.out.tfevents.1665670039.9dd23284ee7b.1181.1 +3 -0
- runs/e3_lr2e-05/events.out.tfevents.1665668964.9dd23284ee7b.94.0 +3 -0
- runs/e3_lr2e-05/events.out.tfevents.1665669018.9dd23284ee7b.94.2 +3 -0
- runs/e3_lr2e-05/events.out.tfevents.1665669243.9dd23284ee7b.798.0 +3 -0
- runs/e3_lr2e-05/events.out.tfevents.1665669913.9dd23284ee7b.1054.0 +3 -0
- runs/e3_lr2e-05/events.out.tfevents.1665670039.9dd23284ee7b.1181.0 +3 -0
- runs/e3_lr2e-05/events.out.tfevents.1665705195.9dd23284ee7b.1181.2 +3 -0
- special_tokens_map.json +7 -0
- tokenizer.json +0 -0
- tokenizer_config.json +15 -0
- training_args.bin +3 -0
- vocab.txt +0 -0
README.md
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---
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license: apache-2.0
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---
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---
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thumbnail: "https://i.imgur.com/tpI1iT5.jpg"
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license: apache-2.0
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language:
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- pt
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: checkpoints
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results:
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- task:
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name: Token Classification
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type: token-classification
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metrics:
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- name: F1
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type: f1
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value: 0.9525622169191057
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- name: Precision
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type: precision
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value: 0.9438680702115613
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- name: Recall
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type: recall
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value: 0.961418019517758
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- name: Accuracy
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type: accuracy
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value: 0.9894253721279602
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- name: Loss
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type: loss
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value: 0.030161771923303604
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widget:
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- text: "Ao Instituto Médico Legal da jurisdição do acidente ou da residência cumpre fornecer, no prazo de 90 dias, laudo à vítima (art. 5, § 5, Lei n. 6.194/74 de 19 de dezembro de 1974), função técnica que pode ser suprida por prova pericial realizada por ordem do juízo da causa, ou por prova técnica realizada no âmbito administrativo que se mostre coerente com os demais elementos de prova constante dos autos."
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- 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."
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- text: "Dispõe sobre o estágio de estudantes; altera a redação do art. 428 da Consolidação das Leis do Trabalho – CLT, aprovada pelo Decreto-Lei no 5.452, de 1o de maio de 1943, e a Lei no 9.394, de 20 de dezembro de 1996; revoga as Leis nos 6.494, de 7 de dezembro de 1977, e 8.859, de 23 de março de 1994, o parágrafo único do art. 82 da Lei no 9.394, de 20 de dezembro de 1996, e o art. 6o da Medida Provisória no 2.164-41, de 24 de agosto de 2001; e dá outras providências."
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---
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## (BERT base) NER model in the legal domain in Portuguese
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**README under construction**
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**ner-legal-bert-base-cased-ptbr** is a NER model (token classification) in the legal domain in Portuguese that was finetuned from the model [dominguesm/legal-bert-base-cased-ptbr](https://huggingface.co/dominguesm/legal-bert-base-cased-ptbr) by using a NER objective.
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The model is intended to assist NLP research in the legal field, computer law and legal technology applications. Several legal texts in Portuguese were used (more information below).
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## Training procedure
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### Training results
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```
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Num examples = 971932
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Num Epochs = 3
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Instantaneous batch size per device = 64
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Total train batch size (w. parallel, distributed & accumulation) = 128
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Gradient Accumulation steps = 2
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Total optimization steps = 22779
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```
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| Step | Training Loss | Validation Loss | Precision | Recall | F1 Accuracy |
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| ---- | ------------- | --------------- | --------- | ------ | ----------- |
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|1000 |0.113900 | 0.057008 | 0.898600| 0.938444| 0.918090| 0.980961|
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|2000 |0.052800 | 0.048254 | 0.917243 | 0.941188 | 0.929062 | 0.983854|
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|3000 |0.046200 | 0.043833 | 0.919706 | 0.948411 | 0.933838 | 0.984931|
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|4000 |0.043500 | 0.039796 | 0.928439 | 0.947058 | 0.937656 | 0.985891|
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|5000 |0.041400 | 0.039421 | 0.926103 | 0.952857 | 0.939290 | 0.986130|
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|6000 |0.039700 | 0.038599 | 0.922376 | 0.956257 | 0.939011 | 0.986093|
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|7000 |0.037800 | 0.036463 | 0.935125 | 0.950937 | 0.942964 | 0.987030|
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|8000 |0.035900 | 0.035706 | 0.934638 | 0.954147 | 0.944292 | 0.987433|
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|9000 |0.033800 | 0.034518 | 0.940354 | 0.951991 | 0.946136 | 0.987866|
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|10000 |0.033600 | 0.033454 | 0.938170 | 0.956097 | 0.947049 | 0.988066|
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|11000 |0.032700 | 0.032899 | 0.934130 | 0.959491 | 0.946641 | 0.988092|
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|12000 |0.032200 | 0.032477 | 0.937400 | 0.959150 | 0.948151 | 0.988305|
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|13000 |0.031200 | 0.033207 | 0.937058 | 0.960506 | 0.948637 | 0.988340|
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|14000 |0.031400 | 0.031711 | 0.938765 | 0.959711 | 0.949123 | 0.988635|
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|15000 |0.030600 | 0.031519 | 0.940488 | 0.959413 | 0.949856 | 0.988709|
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|16000 |0.028500 | 0.031618 | 0.943643 | 0.957693 | 0.950616 | |0.988891|
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|17000 |0.028000 | 0.031106 | 0.941109 | 0.960687 | 0.950797 | |0.989016|
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|18000 |0.027800 | 0.030712 | 0.942821 | 0.960528 | 0.951592 | |0.989198|
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|19000 |0.027500 | 0.030523 | 0.942950 | 0.960947 | 0.951864 | |0.989348|
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|20000 |0.027400 | 0.030577 | 0.942462 | 0.961754 | 0.952010 | |0.989295|
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|21000 |0.027000 | 0.030025 | 0.944483 | 0.960497 | 0.952422 | |0.989445|
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|22000 |0.026800 | 0.030162 | 0.943868 | 0.961418 | 0.952562 | |0.989425|
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config.json
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{
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"_name_or_path": "./model/",
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"architectures": [
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"BertForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"directionality": "bidi",
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "O",
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"1": "B-ORGANIZACAO",
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"10": "I-LEGISLACAO",
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"11": "B-JURISPRUDENCIA",
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"12": "I-JURISPRUDENCIA",
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"2": "I-ORGANIZACAO",
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"3": "B-PESSOA",
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"4": "I-PESSOA",
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"5": "B-TEMPO",
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"6": "I-TEMPO",
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"7": "B-LOCAL",
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"8": "I-LOCAL",
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"9": "B-LEGISLACAO"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"B-JURISPRUDENCIA": "11",
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"B-LEGISLACAO": "9",
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"B-LOCAL": "7",
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"B-ORGANIZACAO": "1",
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"B-PESSOA": "3",
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"B-TEMPO": "5",
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"I-JURISPRUDENCIA": "12",
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"I-LEGISLACAO": "10",
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"I-LOCAL": "8",
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"I-ORGANIZACAO": "2",
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"I-PESSOA": "4",
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"I-TEMPO": "6",
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"O": "0"
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"output_past": true,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.23.1",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 29794
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}
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pytorch_model.bin
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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+
"do_lower_case": false,
|
5 |
+
"mask_token": "[MASK]",
|
6 |
+
"name_or_path": "./model/",
|
7 |
+
"never_split": null,
|
8 |
+
"pad_token": "[PAD]",
|
9 |
+
"sep_token": "[SEP]",
|
10 |
+
"special_tokens_map_file": "/root/.cache/huggingface/hub/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/special_tokens_map.json",
|
11 |
+
"strip_accents": null,
|
12 |
+
"tokenize_chinese_chars": true,
|
13 |
+
"tokenizer_class": "BertTokenizer",
|
14 |
+
"unk_token": "[UNK]"
|
15 |
+
}
|
training_args.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:389017999f38b1c9ff0a840821632744cb86b8b27543e7d2a1eae02f5feee6a3
|
3 |
+
size 3439
|
vocab.txt
ADDED
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|