End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: microsoft/deberta-v3-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-microsoft/deberta-v3-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: 0.9415322580645161
+    - name: Recall
+      type: recall
+      value: 0.9415322580645161
+    - name: F1
+      type: f1
+      value: 0.9415322580645161
+    - name: Accuracy
+      type: accuracy
+      value: 0.9854707843260583
+---
+
+<!-- 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-microsoft/deberta-v3-base-kanuri-ner-v1
+
+This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the Beijuka/Multilingual_PII_NER_dataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0800
+- Precision: 0.9415
+- Recall: 0.9415
+- F1: 0.9415
+- Accuracy: 0.9855
+
+## 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  | 0.0974          | 0.8852    | 0.8857 | 0.8854 | 0.9718   |
+| 0.1637        | 2.0   | 602  | 0.0877          | 0.8894    | 0.9194 | 0.9042 | 0.9756   |
+| 0.1637        | 3.0   | 903  | 0.0788          | 0.8860    | 0.9276 | 0.9063 | 0.9758   |
+| 0.0643        | 4.0   | 1204 | 0.1024          | 0.8899    | 0.9238 | 0.9065 | 0.9772   |
+| 0.0463        | 5.0   | 1505 | 0.0785          | 0.9248    | 0.9130 | 0.9188 | 0.9774   |
+| 0.0463        | 6.0   | 1806 | 0.0940          | 0.9132    | 0.9289 | 0.9210 | 0.9795   |
+| 0.0316        | 7.0   | 2107 | 0.1033          | 0.8974    | 0.9276 | 0.9123 | 0.9770   |
+| 0.0316        | 8.0   | 2408 | 0.1152          | 0.8884    | 0.9302 | 0.9088 | 0.9781   |
+| 0.0179        | 9.0   | 2709 | 0.1308          | 0.8975    | 0.9289 | 0.9129 | 0.9782   |
+
+
+### 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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