metadata

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: 0.4158878504672897
          - name: Recall
            type: recall
            value: 0.5028248587570622
          - name: F1
            type: f1
            value: 0.45524296675191817
          - name: Accuracy
            type: accuracy
            value: 0.8060836501901141

luganda-ner-v1

This model is a fine-tuned version of xlm-roberta-base on the lg-ner dataset. It achieves the following results on the evaluation set:

Loss: 0.7681
Precision: 0.4159
Recall: 0.5028
F1: 0.4552
Accuracy: 0.8061

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	0.9702	0.2686	0.3672	0.3103	0.7240
No log	2.0	50	0.8977	0.2702	0.3785	0.3153	0.7468
No log	3.0	75	0.8785	0.2517	0.4124	0.3126	0.7551
No log	4.0	100	0.8608	0.2927	0.4746	0.3621	0.7567
No log	5.0	125	0.7859	0.4053	0.4350	0.4196	0.7909
No log	6.0	150	0.7728	0.4010	0.4350	0.4173	0.7901
No log	7.0	175	0.7647	0.4118	0.4746	0.4409	0.7932
No log	8.0	200	0.7800	0.3929	0.4972	0.4389	0.7985
No log	9.0	225	0.7706	0.4211	0.4972	0.4560	0.8053
No log	10.0	250	0.7681	0.4159	0.5028	0.4552	0.8061

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2