End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: Davlan/afro-xlmr-base
+tags:
+- named-entity-recognition
+- hausa
+- 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-hausa-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: 0.9298021697511167
+    - name: Recall
+      type: recall
+      value: 0.9256670902160101
+    - name: F1
+      type: f1
+      value: 0.9277300222858963
+    - name: Accuracy
+      type: accuracy
+      value: 0.9811780190852254
+---
+
+<!-- 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-hausa-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: 0.1152
+- Precision: 0.9298
+- Recall: 0.9257
+- F1: 0.9277
+- Accuracy: 0.9812
+
+## 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  | 0.1139          | 0.8862    | 0.8862 | 0.8862 | 0.9694   |
+| 0.2008        | 2.0   | 602  | 0.0925          | 0.8741    | 0.9155 | 0.8944 | 0.9729   |
+| 0.2008        | 3.0   | 903  | 0.0910          | 0.8901    | 0.9125 | 0.9012 | 0.9747   |
+| 0.0686        | 4.0   | 1204 | 0.1056          | 0.8947    | 0.9263 | 0.9102 | 0.9753   |
+| 0.0501        | 5.0   | 1505 | 0.0921          | 0.9071    | 0.9305 | 0.9187 | 0.9775   |
+| 0.0501        | 6.0   | 1806 | 0.0939          | 0.9062    | 0.9377 | 0.9217 | 0.9789   |
+| 0.036         | 7.0   | 2107 | 0.1034          | 0.8926    | 0.9359 | 0.9137 | 0.9769   |
+| 0.036         | 8.0   | 2408 | 0.1305          | 0.9019    | 0.9425 | 0.9218 | 0.9779   |
+| 0.0219        | 9.0   | 2709 | 0.1320          | 0.9037    | 0.9335 | 0.9184 | 0.9778   |
+| 0.0089        | 10.0  | 3010 | 0.1241          | 0.9271    | 0.9065 | 0.9167 | 0.9781   |
+| 0.0089        | 11.0  | 3311 | 0.1386          | 0.9184    | 0.9311 | 0.9247 | 0.9791   |
+| 0.0056        | 12.0  | 3612 | 0.1482          | 0.9094    | 0.9377 | 0.9233 | 0.9788   |
+| 0.0056        | 13.0  | 3913 | 0.1550          | 0.9109    | 0.9311 | 0.9209 | 0.9783   |
+| 0.0032        | 14.0  | 4214 | 0.1631          | 0.9078    | 0.9377 | 0.9225 | 0.9792   |
+
+
+### Framework versions
+
+- Transformers 4.55.4
+- Pytorch 2.8.0+cu126
+- Datasets 4.0.0
+- Tokenizers 0.21.4

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