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update model card README.md

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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - conll2003
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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: bert-finetuned-ner_tmp
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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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+ dataset:
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+ name: conll2003
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+ type: conll2003
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+ config: conll2003
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+ split: validation
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+ args: conll2003
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.9303630363036304
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+ - name: Recall
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+ type: recall
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+ value: 0.9488387748232918
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+ - name: F1
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+ type: f1
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+ value: 0.9395100816530578
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9860628716077
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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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+ # bert-finetuned-ner_tmp
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+
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+ This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0602
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+ - Precision: 0.9304
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+ - Recall: 0.9488
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+ - F1: 0.9395
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+ - Accuracy: 0.9861
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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: 8
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+ - eval_batch_size: 8
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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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+ - num_epochs: 2
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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 | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0858 | 1.0 | 1756 | 0.0679 | 0.9210 | 0.9359 | 0.9284 | 0.9829 |
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+ | 0.0343 | 2.0 | 3512 | 0.0602 | 0.9304 | 0.9488 | 0.9395 | 0.9861 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.30.2
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.13.1
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+ - Tokenizers 0.13.3