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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-v1 |
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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: 0.7987967914438503 |
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- name: Recall |
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type: recall |
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value: 0.8025520483546004 |
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- name: F1 |
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type: f1 |
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value: 0.8006700167504188 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9451502831421519 |
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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-v1 |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the lg-ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3188 |
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- Precision: 0.7988 |
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- Recall: 0.8026 |
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- F1: 0.8007 |
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- Accuracy: 0.9452 |
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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 | 0.2915 | 0.7456 | 0.6891 | 0.7162 | 0.9240 | |
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| 0.2284 | 2.0 | 522 | 0.2965 | 0.7393 | 0.7314 | 0.7353 | 0.9294 | |
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| 0.2284 | 3.0 | 783 | 0.2830 | 0.7426 | 0.7576 | 0.7500 | 0.9271 | |
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| 0.1426 | 4.0 | 1044 | 0.2710 | 0.7935 | 0.7690 | 0.7810 | 0.9387 | |
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| 0.1426 | 5.0 | 1305 | 0.2805 | 0.8087 | 0.7636 | 0.7855 | 0.9389 | |
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| 0.0881 | 6.0 | 1566 | 0.2992 | 0.7734 | 0.7884 | 0.7808 | 0.9404 | |
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| 0.0881 | 7.0 | 1827 | 0.2746 | 0.8109 | 0.7864 | 0.7985 | 0.9457 | |
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| 0.0582 | 8.0 | 2088 | 0.3149 | 0.7753 | 0.7925 | 0.7838 | 0.9400 | |
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| 0.0582 | 9.0 | 2349 | 0.3179 | 0.7940 | 0.7945 | 0.7942 | 0.9440 | |
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| 0.0403 | 10.0 | 2610 | 0.3188 | 0.7988 | 0.8026 | 0.8007 | 0.9452 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.2 |
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