End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: Davlan/afro-xlmr-base
+tags:
+- named-entity-recognition
+- kanuri
+- african-language
+- pii-detection
+- token-classification
+- generated_from_trainer
+datasets:
+- Beijuka/Multilingual_PII_NER_dataset
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: multilingual-Davlan/afro-xlmr-base-kanuri-ner-v1
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: Beijuka/Multilingual_PII_NER_dataset
+      type: Beijuka/Multilingual_PII_NER_dataset
+      args: 'split: train+validation+test'
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9328358208955224
+    - name: Recall
+      type: recall
+      value: 0.9529860228716646
+    - name: F1
+      type: f1
+      value: 0.9428032683846638
+    - name: Accuracy
+      type: accuracy
+      value: 0.9857189865087199
+---
+
+<!-- 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. -->
+
+# multilingual-Davlan/afro-xlmr-base-kanuri-ner-v1
+
+This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the Beijuka/Multilingual_PII_NER_dataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0769
+- Precision: 0.9328
+- Recall: 0.9530
+- F1: 0.9428
+- Accuracy: 0.9857
+
+## 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: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 301  | 0.1158          | 0.8646    | 0.8610 | 0.8628 | 0.9683   |
+| 0.2058        | 2.0   | 602  | 0.0876          | 0.8848    | 0.9431 | 0.9130 | 0.9751   |
+| 0.2058        | 3.0   | 903  | 0.0854          | 0.9078    | 0.9143 | 0.9110 | 0.9783   |
+| 0.0658        | 4.0   | 1204 | 0.1092          | 0.8847    | 0.9383 | 0.9107 | 0.9755   |
+| 0.0491        | 5.0   | 1505 | 0.0881          | 0.9046    | 0.9431 | 0.9234 | 0.9782   |
+| 0.0491        | 6.0   | 1806 | 0.1227          | 0.9015    | 0.9323 | 0.9166 | 0.9770   |
+| 0.0298        | 7.0   | 2107 | 0.1005          | 0.9218    | 0.9461 | 0.9338 | 0.9805   |
+| 0.0298        | 8.0   | 2408 | 0.1454          | 0.8970    | 0.9395 | 0.9178 | 0.9774   |
+| 0.0164        | 9.0   | 2709 | 0.1301          | 0.9146    | 0.9305 | 0.9225 | 0.9789   |
+| 0.0089        | 10.0  | 3010 | 0.1297          | 0.9215    | 0.9425 | 0.9319 | 0.9806   |
+
+
+### Framework versions
+
+- Transformers 4.55.4
+- Pytorch 2.8.0+cu126
+- Datasets 4.0.0
+- Tokenizers 0.21.4

model.safetensors

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