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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- sucx3_ner |
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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: histbert-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: sucx3_ner |
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type: sucx3_ner |
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config: simple_cased |
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split: validation |
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args: simple_cased |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8784308810627898 |
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- name: Recall |
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type: recall |
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value: 0.9261363636363636 |
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- name: F1 |
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type: f1 |
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value: 0.9016530520357625 |
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- name: Accuracy |
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type: accuracy |
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value: 0.992218705252845 |
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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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# histbert-finetuned-ner |
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This model is a fine-tuned version of [Riksarkivet/bert-base-cased-swe-historical](https://huggingface.co./Riksarkivet/bert-base-cased-swe-historical) on the sucx3_ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0495 |
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- Precision: 0.8784 |
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- Recall: 0.9261 |
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- F1: 0.9017 |
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- Accuracy: 0.9922 |
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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: 7 |
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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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| 0.0403 | 1.0 | 5391 | 0.0316 | 0.8496 | 0.8866 | 0.8677 | 0.9903 | |
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| 0.0199 | 2.0 | 10782 | 0.0308 | 0.8814 | 0.9034 | 0.8923 | 0.9915 | |
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| 0.0173 | 3.0 | 16173 | 0.0372 | 0.8698 | 0.9197 | 0.8940 | 0.9913 | |
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| 0.0066 | 4.0 | 21564 | 0.0397 | 0.8783 | 0.9239 | 0.9005 | 0.9921 | |
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| 0.0029 | 5.0 | 26955 | 0.0454 | 0.8855 | 0.9181 | 0.9015 | 0.9923 | |
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| 0.0035 | 6.0 | 32346 | 0.0454 | 0.8834 | 0.9211 | 0.9019 | 0.9922 | |
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| 0.0009 | 7.0 | 37737 | 0.0495 | 0.8784 | 0.9261 | 0.9017 | 0.9922 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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