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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-v6 |
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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: 0.8241451500348919 |
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- name: Recall |
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type: recall |
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value: 0.7931497649429147 |
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- name: F1 |
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type: f1 |
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value: 0.8083504449007528 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9525918396979817 |
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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-v6 |
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This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the lg-ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2417 |
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- Precision: 0.8241 |
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- Recall: 0.7931 |
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- F1: 0.8084 |
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- Accuracy: 0.9526 |
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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 | 0.4290 | 0.5281 | 0.3096 | 0.3903 | 0.8864 | |
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| 0.5483 | 2.0 | 522 | 0.2873 | 0.7307 | 0.5776 | 0.6452 | 0.9216 | |
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| 0.5483 | 3.0 | 783 | 0.2482 | 0.7745 | 0.6783 | 0.7232 | 0.9334 | |
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| 0.1931 | 4.0 | 1044 | 0.2472 | 0.7671 | 0.6991 | 0.7316 | 0.9360 | |
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| 0.1931 | 5.0 | 1305 | 0.2425 | 0.8053 | 0.7388 | 0.7706 | 0.9433 | |
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| 0.1016 | 6.0 | 1566 | 0.2157 | 0.8253 | 0.7710 | 0.7972 | 0.9490 | |
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| 0.1016 | 7.0 | 1827 | 0.2332 | 0.8161 | 0.7717 | 0.7932 | 0.9501 | |
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| 0.0654 | 8.0 | 2088 | 0.2375 | 0.8312 | 0.7804 | 0.8050 | 0.9514 | |
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| 0.0654 | 9.0 | 2349 | 0.2367 | 0.8309 | 0.7884 | 0.8091 | 0.9528 | |
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| 0.047 | 10.0 | 2610 | 0.2417 | 0.8241 | 0.7931 | 0.8084 | 0.9526 | |
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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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