End of training

README.md

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+---
+library_name: transformers
+license: afl-3.0
+base_model: masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0
+tags:
+- named-entity-recognition
+- lumasaba
+- african-language
+- pii-detection
+- token-classification
+- generated_from_trainer
+datasets:
+- Beijuka/Multilingual_PII_NER_dataset
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: multilingual-masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0-lumasaba-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: 0.9702892885066459
+    - name: Recall
+      type: recall
+      value: 0.9487767584097859
+    - name: F1
+      type: f1
+      value: 0.9594124468496328
+    - name: Accuracy
+      type: accuracy
+      value: 0.9525409491810164
+---
+
+<!-- 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-masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0-lumasaba-ner-v1
+
+This model is a fine-tuned version of [masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0](https://huggingface.co/masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0) on the Beijuka/Multilingual_PII_NER_dataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3834
+- Precision: 0.9703
+- Recall: 0.9488
+- F1: 0.9594
+- Accuracy: 0.9525
+
+## 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: 4
+- eval_batch_size: 4
+- 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.1185        | 1.0   | 796  | 0.5047          | 0.8681    | 0.8810 | 0.8745 | 0.8735   |
+| 0.3868        | 2.0   | 1592 | 0.4627          | 0.9012    | 0.9146 | 0.9079 | 0.9108   |
+| 0.2335        | 3.0   | 2388 | 0.4419          | 0.9115    | 0.9272 | 0.9193 | 0.9198   |
+| 0.1462        | 4.0   | 3184 | 0.3402          | 0.9499    | 0.9507 | 0.9503 | 0.9520   |
+| 0.1072        | 5.0   | 3980 | 0.2399          | 0.9560    | 0.9538 | 0.9549 | 0.9563   |
+| 0.0916        | 6.0   | 4776 | 0.3072          | 0.9548    | 0.9593 | 0.9570 | 0.9588   |
+| 0.0432        | 7.0   | 5572 | 0.3124          | 0.9573    | 0.9663 | 0.9618 | 0.9605   |
+| 0.0383        | 8.0   | 6368 | 0.3386          | 0.9669    | 0.9608 | 0.9639 | 0.9575   |
+| 0.0502        | 9.0   | 7164 | 0.4429          | 0.9644    | 0.9554 | 0.9599 | 0.9550   |
+| 0.0349        | 10.0  | 7960 | 0.4191          | 0.9605    | 0.9522 | 0.9564 | 0.9481   |
+| 0.039         | 11.0  | 8756 | 0.4815          | 0.9558    | 0.9648 | 0.9602 | 0.9537   |
+
+
+### 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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