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--- |
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license: mit |
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base_model: xlm-roberta-large |
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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-large-ner-silvanus |
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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: id |
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split: validation |
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args: id |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.969802244788883 |
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- name: Recall |
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type: recall |
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value: 0.9789587267332075 |
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- name: F1 |
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type: f1 |
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value: 0.9743589743589745 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9894519740718916 |
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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-large-ner-silvanus |
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co./xlm-roberta-large) on the wikiann dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0686 |
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- Precision: 0.9698 |
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- Recall: 0.9790 |
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- F1: 0.9744 |
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- Accuracy: 0.9895 |
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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: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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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| No log | 1.0 | 427 | 0.0717 | 0.9367 | 0.9701 | 0.9531 | 0.9862 | |
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| 0.0221 | 2.0 | 855 | 0.0715 | 0.9560 | 0.9733 | 0.9646 | 0.9880 | |
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| 0.0113 | 3.0 | 1281 | 0.0686 | 0.9698 | 0.9790 | 0.9744 | 0.9895 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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