End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: xlm-roberta-large
+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-xlm-roberta-large-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: 0.25703485930281395
+    - name: Recall
+      type: recall
+      value: 0.46788990825688076
+    - name: F1
+      type: f1
+      value: 0.3317972350230415
+    - name: Accuracy
+      type: accuracy
+      value: 0.25703485930281395
+---
+
+<!-- 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-large-lumasaba-ner-v1
+
+This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the Beijuka/Multilingual_PII_NER_dataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.4160
+- Precision: 0.2570
+- Recall: 0.4679
+- F1: 0.3318
+- Accuracy: 0.2570
+
+## 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: 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 2.4785        | 1.0   | 796  | 2.2775          | 0.2916    | 0.5325 | 0.3768 | 0.2916   |
+| 2.3894        | 2.0   | 1592 | 2.4192          | 0.2916    | 0.5325 | 0.3768 | 0.2916   |
+| 2.3051        | 3.0   | 2388 | 2.3613          | 0.2916    | 0.5325 | 0.3768 | 0.2916   |
+| 2.3278        | 4.0   | 3184 | 2.8233          | 0.2916    | 0.5325 | 0.3768 | 0.2916   |
+
+
+### 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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