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--- |
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license: afl-3.0 |
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base_model: masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- precision |
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- recall |
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- accuracy |
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model-index: |
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- name: ewc_stabilised |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ewc_stabilised |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1396 |
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- F1: 0.8317 |
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- Precision: 0.8305 |
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- Recall: 0.8328 |
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- Accuracy: 0.9605 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 3407 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| |
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| 0.3184 | 0.9993 | 701 | 0.1480 | 0.7895 | 0.7950 | 0.7841 | 0.9511 | |
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| 0.1333 | 2.0 | 1403 | 0.1271 | 0.8195 | 0.8148 | 0.8242 | 0.9578 | |
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| 0.0975 | 2.9993 | 2104 | 0.1241 | 0.8289 | 0.8254 | 0.8324 | 0.9598 | |
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| 0.0744 | 4.0 | 2806 | 0.1293 | 0.8307 | 0.8313 | 0.8300 | 0.9603 | |
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| 0.0596 | 4.9964 | 3505 | 0.1396 | 0.8317 | 0.8305 | 0.8328 | 0.9605 | |
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### Framework versions |
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- Transformers 4.43.4 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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