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update model card README.md
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README.md
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---
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license: apache-2.0
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base_model: bert-base-cased
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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
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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: 0.9323631552836117
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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: 0.940528818083243
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- name: Accuracy
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type: accuracy
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value: 0.9861217401542356
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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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# bert-finetuned-ner
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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: 0.0592
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- Precision: 0.9324
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- Recall: 0.9488
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- F1: 0.9405
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- Accuracy: 0.9861
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.0774 | 1.0 | 1756 | 0.0764 | 0.9146 | 0.9337 | 0.9241 | 0.9802 |
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| 0.0394 | 2.0 | 3512 | 0.0554 | 0.9265 | 0.9483 | 0.9373 | 0.9860 |
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| 0.0261 | 3.0 | 5268 | 0.0592 | 0.9324 | 0.9488 | 0.9405 | 0.9861 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.4
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- Tokenizers 0.13.3
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