readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - lg-ner
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: luganda-ner-v1
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: lg-ner
        type: lg-ner
        config: lug
        split: train
        args: lug
      metrics:
      - name: Precision
        type: precision
        value: 0.29015544041450775
      - name: Recall
        type: recall
        value: 0.27722772277227725
      - name: F1
        type: f1
        value: 0.2835443037974684
      - name: Accuracy
        type: accuracy
        value: 0.7297843665768194
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # luganda-ner-v1
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the lg-ner dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0530
  - Precision: 0.2902
  - Recall: 0.2772
  - F1: 0.2835
  - Accuracy: 0.7298
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 25   | 1.2878          | 0.0       | 0.0    | 0.0    | 0.7271   |
  | No log        | 2.0   | 50   | 1.2373          | 0.0       | 0.0    | 0.0    | 0.7271   |
  | No log        | 3.0   | 75   | 1.2309          | 0.3542    | 0.1683 | 0.2282 | 0.7244   |
  | No log        | 4.0   | 100  | 1.1505          | 0.2712    | 0.2376 | 0.2533 | 0.7183   |
  | No log        | 5.0   | 125  | 1.1360          | 0.2579    | 0.2426 | 0.25   | 0.7170   |
  | No log        | 6.0   | 150  | 1.0932          | 0.3108    | 0.2277 | 0.2629 | 0.7338   |
  | No log        | 7.0   | 175  | 1.0761          | 0.2989    | 0.2574 | 0.2766 | 0.7298   |
  | No log        | 8.0   | 200  | 1.0645          | 0.2805    | 0.3069 | 0.2931 | 0.7244   |
  | No log        | 9.0   | 225  | 1.0577          | 0.3022    | 0.2723 | 0.2865 | 0.7325   |
  | No log        | 10.0  | 250  | 1.0530          | 0.2902    | 0.2772 | 0.2835 | 0.7298   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  