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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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- lg-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: luganda-ner-v4 |
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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: lg-ner |
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type: lg-ner |
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config: lug |
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split: test |
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args: lug |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7849185946872322 |
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- name: Recall |
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type: recall |
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value: 0.7862660944206008 |
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- name: F1 |
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type: f1 |
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value: 0.7855917667238421 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9542220362038296 |
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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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# luganda-ner-v4 |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the lg-ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2222 |
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- Precision: 0.7849 |
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- Recall: 0.7863 |
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- F1: 0.7856 |
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- Accuracy: 0.9542 |
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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: 10 |
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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 | 261 | 0.3533 | 0.6141 | 0.4644 | 0.5288 | 0.9208 | |
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| 0.5126 | 2.0 | 522 | 0.2765 | 0.6658 | 0.6567 | 0.6612 | 0.9326 | |
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| 0.5126 | 3.0 | 783 | 0.2336 | 0.6834 | 0.7133 | 0.6980 | 0.9433 | |
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| 0.2374 | 4.0 | 1044 | 0.2207 | 0.7358 | 0.7433 | 0.7395 | 0.9489 | |
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| 0.2374 | 5.0 | 1305 | 0.2134 | 0.7796 | 0.7528 | 0.7659 | 0.9525 | |
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| 0.1646 | 6.0 | 1566 | 0.2359 | 0.7423 | 0.7665 | 0.7542 | 0.9484 | |
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| 0.1646 | 7.0 | 1827 | 0.2223 | 0.7807 | 0.7854 | 0.7831 | 0.9541 | |
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| 0.1219 | 8.0 | 2088 | 0.2300 | 0.8140 | 0.7665 | 0.7896 | 0.9557 | |
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| 0.1219 | 9.0 | 2349 | 0.2223 | 0.7733 | 0.7966 | 0.7848 | 0.9547 | |
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| 0.1016 | 10.0 | 2610 | 0.2222 | 0.7849 | 0.7863 | 0.7856 | 0.9542 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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