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---
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license: apache-2.0
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base_model: google-bert/bert-large-uncased
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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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- f1
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model-index:
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- name: bert-large-uncased-for-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: F1
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type: f1
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value: 0.9507620164126612
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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-large-uncased-for-ner
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This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on the conll2003 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0371
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- F1: 0.9508
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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: 5e-05
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- train_batch_size: 24
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- eval_batch_size: 24
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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 | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| 0.1141 | 1.0 | 586 | 0.0443 | 0.9336 |
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| 0.0267 | 2.0 | 1172 | 0.0382 | 0.9458 |
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| 0.0108 | 3.0 | 1758 | 0.0371 | 0.9508 |
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### Framework versions
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- Transformers 4.43.3
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- Pytorch 2.4.0
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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