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--- |
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license: apache-2.0 |
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base_model: distilbert-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: distilbert-base-uncased-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: 0.921011931064958 |
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- name: Recall |
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type: recall |
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value: 0.93265465935787 |
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- name: F1 |
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type: f1 |
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value: 0.9267967316991829 |
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- name: Accuracy |
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type: accuracy |
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value: 0.982826822565015 |
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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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# distilbert-base-uncased-finetuned-ner |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0610 |
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- Precision: 0.9210 |
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- Recall: 0.9327 |
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- F1: 0.9268 |
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- Accuracy: 0.9828 |
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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: 16 |
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- eval_batch_size: 16 |
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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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| 0.248 | 1.0 | 878 | 0.0676 | 0.9021 | 0.9205 | 0.9112 | 0.9805 | |
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| 0.0508 | 2.0 | 1756 | 0.0614 | 0.9208 | 0.9289 | 0.9248 | 0.9825 | |
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| 0.0308 | 3.0 | 2634 | 0.0610 | 0.9210 | 0.9327 | 0.9268 | 0.9828 | |
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### Framework versions |
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- Transformers 4.37.0 |
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- Pytorch 2.1.2+cpu |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.1 |
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