update model card README.md

README.md

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+---
+license: afl-3.0
+tags:
+- generated_from_trainer
+datasets:
+- lg-ner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: luganda-ner-v5
+  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: 0.8502710027100271
+    - name: Recall
+      type: recall
+      value: 0.8428475486903962
+    - name: F1
+      type: f1
+      value: 0.8465430016863407
+    - name: Accuracy
+      type: accuracy
+      value: 0.959089589080877
+---
+
+<!-- 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-v5
+
+This model is a fine-tuned version of [masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0](https://huggingface.co/masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0) on the lg-ner dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2328
+- Precision: 0.8503
+- Recall: 0.8428
+- F1: 0.8465
+- Accuracy: 0.9591
+
+## 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: 6
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 261  | 0.2276          | 0.7703    | 0.6441 | 0.7015 | 0.9353   |
+| 0.3176        | 2.0   | 522  | 0.1848          | 0.8431    | 0.7542 | 0.7962 | 0.9545   |
+| 0.3176        | 3.0   | 783  | 0.1871          | 0.8564    | 0.8173 | 0.8364 | 0.9576   |
+| 0.0753        | 4.0   | 1044 | 0.2015          | 0.8691    | 0.8294 | 0.8488 | 0.9614   |
+| 0.0753        | 5.0   | 1305 | 0.2325          | 0.8616    | 0.8361 | 0.8487 | 0.9584   |
+| 0.0261        | 6.0   | 1566 | 0.2328          | 0.8503    | 0.8428 | 0.8465 | 0.9591   |
+
+
+### Framework versions
+
+- Transformers 4.27.4
+- Pytorch 1.13.1+cu116
+- Datasets 2.11.0
+- Tokenizers 0.13.2