update model card README.md

README.md

@@ -0,0 +1,98 @@
+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- lg-ner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: luganda-ner-v6
+  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: 0.8241451500348919
+    - name: Recall
+      type: recall
+      value: 0.7931497649429147
+    - name: F1
+      type: f1
+      value: 0.8083504449007528
+    - name: Accuracy
+      type: accuracy
+      value: 0.9525918396979817
+---
+
+<!-- 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-v6
+
+This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the lg-ner dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2417
+- Precision: 0.8241
+- Recall: 0.7931
+- F1: 0.8084
+- Accuracy: 0.9526
+
+## 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  | 0.4290          | 0.5281    | 0.3096 | 0.3903 | 0.8864   |
+| 0.5483        | 2.0   | 522  | 0.2873          | 0.7307    | 0.5776 | 0.6452 | 0.9216   |
+| 0.5483        | 3.0   | 783  | 0.2482          | 0.7745    | 0.6783 | 0.7232 | 0.9334   |
+| 0.1931        | 4.0   | 1044 | 0.2472          | 0.7671    | 0.6991 | 0.7316 | 0.9360   |
+| 0.1931        | 5.0   | 1305 | 0.2425          | 0.8053    | 0.7388 | 0.7706 | 0.9433   |
+| 0.1016        | 6.0   | 1566 | 0.2157          | 0.8253    | 0.7710 | 0.7972 | 0.9490   |
+| 0.1016        | 7.0   | 1827 | 0.2332          | 0.8161    | 0.7717 | 0.7932 | 0.9501   |
+| 0.0654        | 8.0   | 2088 | 0.2375          | 0.8312    | 0.7804 | 0.8050 | 0.9514   |
+| 0.0654        | 9.0   | 2349 | 0.2367          | 0.8309    | 0.7884 | 0.8091 | 0.9528   |
+| 0.047         | 10.0  | 2610 | 0.2417          | 0.8241    | 0.7931 | 0.8084 | 0.9526   |
+
+
+### Framework versions
+
+- Transformers 4.27.4
+- Pytorch 1.13.1+cu116
+- Datasets 2.11.0
+- Tokenizers 0.13.2