update model card README.md

README.md

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+---
+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
+---
+
+<!-- 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: 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