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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-v2 |
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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: 0.9352766798418972 |
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- name: Recall |
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type: recall |
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value: 0.9288518155053974 |
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- name: F1 |
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type: f1 |
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value: 0.93205317577548 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9817219554779573 |
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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-v2 |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/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: 0.0955 |
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- Precision: 0.9353 |
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- Recall: 0.9289 |
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- F1: 0.9321 |
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- Accuracy: 0.9817 |
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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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| 0.5913 | 1.0 | 609 | 0.2667 | 0.6740 | 0.7620 | 0.7153 | 0.9336 | |
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| 0.2461 | 2.0 | 1218 | 0.1704 | 0.7981 | 0.8437 | 0.8203 | 0.9562 | |
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| 0.1784 | 3.0 | 1827 | 0.1273 | 0.8578 | 0.8943 | 0.8757 | 0.9669 | |
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| 0.1337 | 4.0 | 2436 | 0.1048 | 0.8731 | 0.9132 | 0.8927 | 0.9726 | |
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| 0.0868 | 5.0 | 3045 | 0.0988 | 0.9129 | 0.9178 | 0.9153 | 0.9760 | |
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| 0.0736 | 6.0 | 3654 | 0.0961 | 0.9146 | 0.9225 | 0.9185 | 0.9781 | |
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| 0.0602 | 7.0 | 4263 | 0.0877 | 0.9270 | 0.9222 | 0.9246 | 0.9798 | |
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| 0.0566 | 8.0 | 4872 | 0.0948 | 0.9281 | 0.9222 | 0.9252 | 0.9807 | |
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| 0.0514 | 9.0 | 5481 | 0.0930 | 0.9349 | 0.9271 | 0.9310 | 0.9817 | |
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| 0.0395 | 10.0 | 6090 | 0.0955 | 0.9353 | 0.9289 | 0.9321 | 0.9817 | |
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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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