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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: bert-base-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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- 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-ner-conll2003 |
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results: |
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- task: |
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type: token-classification |
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name: 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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- type: precision |
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value: 0.9414244508542268 |
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name: Precision |
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- type: recall |
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value: 0.9493231905134802 |
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name: Recall |
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- type: f1 |
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value: 0.9453573218960619 |
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name: F1 |
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- type: accuracy |
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value: 0.9865601220074031 |
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name: Accuracy |
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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-ner-conll2003 |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0631 |
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- Precision: 0.9414 |
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- Recall: 0.9493 |
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- F1: 0.9454 |
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- Accuracy: 0.9866 |
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## Model description |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") |
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model = AutoModelForTokenClassification.from_pretrained("PassbyGrocer/bert-ner-conll2003") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer) |
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example = "My name is Wolfgang and I live in Berlin." |
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ner_results = nlp(example) |
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print(ner_results) |
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``` |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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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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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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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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| 0.0844 | 1.0 | 878 | 0.0693 | 0.9029 | 0.9201 | 0.9114 | 0.9806 | |
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| 0.0216 | 2.0 | 1756 | 0.0559 | 0.9340 | 0.9444 | 0.9391 | 0.9854 | |
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| 0.0206 | 3.0 | 2634 | 0.0569 | 0.9436 | 0.9447 | 0.9442 | 0.9863 | |
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| 0.0141 | 4.0 | 3512 | 0.0634 | 0.9369 | 0.9488 | 0.9428 | 0.9860 | |
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| 0.0176 | 5.0 | 4390 | 0.0631 | 0.9414 | 0.9493 | 0.9454 | 0.9866 | |
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### Framework versions |
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- Transformers 4.46.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.1 |
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