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-v4
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: lg-ner
+      type: lg-ner
+      config: lug
+      split: test
+      args: lug
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7849185946872322
+    - name: Recall
+      type: recall
+      value: 0.7862660944206008
+    - name: F1
+      type: f1
+      value: 0.7855917667238421
+    - name: Accuracy
+      type: accuracy
+      value: 0.9542220362038296
+---
+
+<!-- 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-v4
+
+This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the lg-ner dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2222
+- Precision: 0.7849
+- Recall: 0.7863
+- F1: 0.7856
+- Accuracy: 0.9542
+
+## 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   | 261  | 0.3533          | 0.6141    | 0.4644 | 0.5288 | 0.9208   |
+| 0.5126        | 2.0   | 522  | 0.2765          | 0.6658    | 0.6567 | 0.6612 | 0.9326   |
+| 0.5126        | 3.0   | 783  | 0.2336          | 0.6834    | 0.7133 | 0.6980 | 0.9433   |
+| 0.2374        | 4.0   | 1044 | 0.2207          | 0.7358    | 0.7433 | 0.7395 | 0.9489   |
+| 0.2374        | 5.0   | 1305 | 0.2134          | 0.7796    | 0.7528 | 0.7659 | 0.9525   |
+| 0.1646        | 6.0   | 1566 | 0.2359          | 0.7423    | 0.7665 | 0.7542 | 0.9484   |
+| 0.1646        | 7.0   | 1827 | 0.2223          | 0.7807    | 0.7854 | 0.7831 | 0.9541   |
+| 0.1219        | 8.0   | 2088 | 0.2300          | 0.8140    | 0.7665 | 0.7896 | 0.9557   |
+| 0.1219        | 9.0   | 2349 | 0.2223          | 0.7733    | 0.7966 | 0.7848 | 0.9547   |
+| 0.1016        | 10.0  | 2610 | 0.2222          | 0.7849    | 0.7863 | 0.7856 | 0.9542   |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 1.13.1+cu116
+- Datasets 2.10.1
+- Tokenizers 0.13.2