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--- |
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license: mit |
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base_model: xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- wikiann |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: xlm-roberta-base-ka-ner |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: wikiann |
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type: wikiann |
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config: ka |
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split: validation |
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args: ka |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8505682876839947 |
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- name: Recall |
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type: recall |
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value: 0.8702816057519472 |
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- name: F1 |
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type: f1 |
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value: 0.8603120330609663 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9424682155180856 |
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language: |
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- ka |
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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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# xlm-roberta-base-ka-ner |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the wikiann dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2031 |
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- Precision: 0.8506 |
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- Recall: 0.8703 |
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- F1: 0.8603 |
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- Accuracy: 0.9425 |
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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: 16 |
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- seed: 42 |
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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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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.5349 | 1.0 | 625 | 0.2377 | 0.8302 | 0.8218 | 0.8260 | 0.9287 | |
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| 0.2353 | 2.0 | 1250 | 0.2037 | 0.8556 | 0.8536 | 0.8546 | 0.9394 | |
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| 0.1782 | 3.0 | 1875 | 0.2031 | 0.8506 | 0.8703 | 0.8603 | 0.9425 | |
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## Metrics per category |
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{'LOC': {'precision': 0.8558191459670667, |
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'recall': 0.9074874223142941, |
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'f1': 0.8808962941683425, |
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'number': 16895}, |
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'ORG': {'precision': 0.7917612346799818, |
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'recall': 0.7510226049515608, |
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'f1': 0.7708540492763231, |
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'number': 9290}, |
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'PER': {'precision': 0.8896882494004796, |
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'recall': 0.9157884743188076, |
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'f1': 0.9025497076023392, |
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'number': 10533}, |
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'overall_precision': 0.8505682876839947, |
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'overall_recall': 0.8702816057519472, |
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'overall_f1': 0.8603120330609663, |
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'overall_accuracy': 0.9424682155180856} |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
