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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: lora-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: 0.8535113174695299 |
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- name: Recall |
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type: recall |
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value: 0.8726560645620698 |
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- name: F1 |
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type: f1 |
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value: 0.8629775247931459 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9730680240629338 |
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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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# lora-ner |
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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: 0.0920 |
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- Precision: 0.8535 |
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- Recall: 0.8727 |
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- F1: 0.8630 |
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- Accuracy: 0.9731 |
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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: 0.001 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 220 | 0.1085 | 0.8026 | 0.8314 | 0.8167 | 0.9675 | |
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| No log | 2.0 | 440 | 0.0804 | 0.8693 | 0.8818 | 0.8755 | 0.9759 | |
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| 0.2014 | 3.0 | 660 | 0.0720 | 0.8764 | 0.8970 | 0.8866 | 0.9783 | |
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| 0.2014 | 4.0 | 880 | 0.0688 | 0.8773 | 0.9056 | 0.8912 | 0.9792 | |
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| 0.0882 | 5.0 | 1100 | 0.0674 | 0.8823 | 0.9067 | 0.8943 | 0.9796 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.4.0 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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