ciriatico/dodfminer_lite-ner_bertimbau-extrato_contrato

README.md

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+---
+library_name: transformers
+license: mit
+base_model: neuralmind/bert-base-portuguese-cased
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: e3_lr2e-05
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# e3_lr2e-05
+
+This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0753
+- Precision: 0.9611
+- Recall: 0.9778
+- F1: 0.9694
+- Accuracy: 0.9817
+
+## 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: 2e-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: 3
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.4195        | 0.2564 | 50   | 0.2315          | 0.8642    | 0.8460 | 0.8550 | 0.9499   |
+| 0.2396        | 0.5128 | 100  | 0.1778          | 0.8971    | 0.8970 | 0.8970 | 0.9517   |
+| 0.1717        | 0.7692 | 150  | 0.1330          | 0.9033    | 0.9323 | 0.9176 | 0.9639   |
+| 0.1249        | 1.0256 | 200  | 0.1090          | 0.9369    | 0.9554 | 0.9460 | 0.9728   |
+| 0.0929        | 1.2821 | 250  | 0.1066          | 0.9397    | 0.9630 | 0.9512 | 0.9739   |
+| 0.0954        | 1.5385 | 300  | 0.0831          | 0.9498    | 0.9670 | 0.9583 | 0.9788   |
+| 0.0858        | 1.7949 | 350  | 0.0844          | 0.9459    | 0.9727 | 0.9591 | 0.9776   |
+| 0.0715        | 2.0513 | 400  | 0.0868          | 0.9512    | 0.9766 | 0.9637 | 0.9796   |
+| 0.056         | 2.3077 | 450  | 0.0789          | 0.9616    | 0.9774 | 0.9695 | 0.9818   |
+| 0.0592        | 2.5641 | 500  | 0.0768          | 0.9614    | 0.9783 | 0.9698 | 0.9817   |
+| 0.0607        | 2.8205 | 550  | 0.0753          | 0.9611    | 0.9778 | 0.9694 | 0.9817   |
+
+
+### Framework versions
+
+- Transformers 4.45.0
+- Pytorch 2.4.1+cu121
+- Datasets 3.0.0
+- Tokenizers 0.20.0

config.json

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+{
+  "_name_or_path": "neuralmind/bert-base-portuguese-cased",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9",
+    "10": "LABEL_10",
+    "11": "LABEL_11",
+    "12": "LABEL_12",
+    "13": "LABEL_13",
+    "14": "LABEL_14",
+    "15": "LABEL_15",
+    "16": "LABEL_16",
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+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 29794
+}

model.safetensors

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special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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