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oracle-corejur/bert_oracle_class_bin_cur2_anno_neg_v1

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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: neuralmind/bert-base-portuguese-cased
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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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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: oracle_class_bin
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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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+ # oracle_class_bin
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+
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+ This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1283
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+ - Precision: 0.7996
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+ - Recall: 0.8499
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+ - Accuracy: 0.9627
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+ - F1: 0.8240
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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: 2e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 2000
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+ - num_epochs: 3
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
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+ |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|
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+ | 0.1359 | 0.5606 | 1800 | 0.1183 | 0.7706 | 0.7945 | 0.9546 | 0.7824 |
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+ | 0.1129 | 1.1211 | 3600 | 0.1203 | 0.7550 | 0.8529 | 0.9565 | 0.8010 |
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+ | 0.0919 | 1.6817 | 5400 | 0.1111 | 0.8016 | 0.8180 | 0.9605 | 0.8098 |
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+ | 0.0658 | 2.2423 | 7200 | 0.1142 | 0.8059 | 0.8249 | 0.9616 | 0.8153 |
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+ | 0.0731 | 2.8029 | 9000 | 0.1283 | 0.7996 | 0.8499 | 0.9627 | 0.8240 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.2
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+ - Pytorch 2.1.0
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+ - Datasets 2.19.2
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+ - Tokenizers 0.19.1
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