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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-v3 |
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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: 0.8141289437585734 |
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- name: Recall |
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type: recall |
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value: 0.7971793149764943 |
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- name: F1 |
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type: f1 |
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value: 0.8055649813369528 |
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- name: Accuracy |
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type: accuracy |
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value: 0.952700740525628 |
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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-v3 |
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the lg-ner dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2295 |
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- Precision: 0.8141 |
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- Recall: 0.7972 |
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- F1: 0.8056 |
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- Accuracy: 0.9527 |
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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: 6 |
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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 | 0.4226 | 0.6273 | 0.3606 | 0.4580 | 0.8928 | |
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| 0.5572 | 2.0 | 522 | 0.2835 | 0.7720 | 0.6185 | 0.6868 | 0.9219 | |
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| 0.5572 | 3.0 | 783 | 0.2740 | 0.7579 | 0.7401 | 0.7489 | 0.9311 | |
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| 0.1745 | 4.0 | 1044 | 0.2423 | 0.7895 | 0.7683 | 0.7788 | 0.9399 | |
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| 0.1745 | 5.0 | 1305 | 0.2273 | 0.8048 | 0.7945 | 0.7996 | 0.9498 | |
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| 0.086 | 6.0 | 1566 | 0.2295 | 0.8141 | 0.7972 | 0.8056 | 0.9527 | |
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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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