End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: xlm-roberta-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-xlm-roberta-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: 0.9064220183486239
+    - name: Recall
+      type: recall
+      value: 0.9415501905972046
+    - name: F1
+      type: f1
+      value: 0.9236522281084449
+    - name: Accuracy
+      type: accuracy
+      value: 0.9821651859164199
+---
+
+<!-- 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-xlm-roberta-base-kanuri-ner-v1
+
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the Beijuka/Multilingual_PII_NER_dataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0719
+- Precision: 0.9064
+- Recall: 0.9416
+- F1: 0.9237
+- Accuracy: 0.9822
+
+## 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          | 0.9102    | 0.8262 | 0.8662 | 0.9666   |
+| 0.229         | 2.0   | 602  | 0.0918          | 0.8883    | 0.9287 | 0.9080 | 0.9736   |
+| 0.229         | 3.0   | 903  | 0.0924          | 0.8654    | 0.9401 | 0.9012 | 0.9751   |
+| 0.0702        | 4.0   | 1204 | 0.1025          | 0.8772    | 0.9461 | 0.9103 | 0.9750   |
+| 0.0514        | 5.0   | 1505 | 0.1446          | 0.8542    | 0.8670 | 0.8605 | 0.9671   |
+| 0.0514        | 6.0   | 1806 | 0.1227          | 0.8946    | 0.9203 | 0.9073 | 0.9732   |
+| 0.034         | 7.0   | 2107 | 0.1240          | 0.8949    | 0.9233 | 0.9089 | 0.9747   |
+
+
+### 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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