belisards commited on
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kairos-posicao-bert2

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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: belisards/congretimbau
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - f1
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+ - recall
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+ - precision
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+ model-index:
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+ - name: MyDrive
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # MyDrive
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+
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+ This model is a fine-tuned version of [belisards/congretimbau](https://huggingface.co/belisards/congretimbau) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1271
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+ - Accuracy: 0.8367
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+ - F1: 0.7603
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+ - Recall: 0.7548
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+ - Precision: 0.7665
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 5151
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 150
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+ - num_epochs: 14
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
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+ | 0.1299 | 2.8333 | 51 | 0.1261 | 0.75 | 0.5739 | 0.5733 | 0.6589 |
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+ | 0.1037 | 5.6667 | 102 | 0.1088 | 0.8214 | 0.7172 | 0.6888 | 0.8124 |
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+ | 0.0511 | 8.5 | 153 | 0.1180 | 0.8304 | 0.7652 | 0.7509 | 0.7860 |
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+ | 0.0138 | 11.3333 | 204 | 0.1670 | 0.8304 | 0.7711 | 0.7622 | 0.7822 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.47.0
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+ - Pytorch 2.5.1+cu121
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+ - Datasets 3.2.0
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+ - Tokenizers 0.21.0
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+ "layer_norm_eps": 1e-12,
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+ "model_type": "bert",
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+ "num_attention_heads": 16,
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+ "num_hidden_layers": 24,
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+ "output_past": true,
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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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+ "problem_type": "single_label_classification",
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+ "torch_dtype": "float32",
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+ "type_vocab_size": 2,
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+ }
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