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--- |
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license: mit |
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base_model: FacebookAI/xlm-roberta-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cnec |
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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: CNEC1_1_extended_xlm-roberta-large |
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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: cnec |
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type: cnec |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8424273329933707 |
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- name: Recall |
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type: recall |
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value: 0.882950293960449 |
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- name: F1 |
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type: f1 |
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value: 0.8622129436325678 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9652851996991648 |
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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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# CNEC1_1_extended_xlm-roberta-large |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2119 |
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- Precision: 0.8424 |
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- Recall: 0.8830 |
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- F1: 0.8622 |
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- Accuracy: 0.9653 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 10 |
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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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| 0.3746 | 0.86 | 500 | 0.1861 | 0.7228 | 0.8097 | 0.7638 | 0.9523 | |
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| 0.2127 | 1.72 | 1000 | 0.1635 | 0.7829 | 0.8461 | 0.8133 | 0.9611 | |
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| 0.1494 | 2.58 | 1500 | 0.1704 | 0.7579 | 0.8466 | 0.7998 | 0.9546 | |
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| 0.1274 | 3.44 | 2000 | 0.1800 | 0.8003 | 0.8675 | 0.8325 | 0.9615 | |
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| 0.0987 | 4.3 | 2500 | 0.1511 | 0.8025 | 0.8883 | 0.8432 | 0.9657 | |
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| 0.0827 | 5.16 | 3000 | 0.1910 | 0.8179 | 0.8739 | 0.8450 | 0.9630 | |
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| 0.0677 | 6.02 | 3500 | 0.1655 | 0.8374 | 0.8808 | 0.8586 | 0.9689 | |
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| 0.0475 | 6.88 | 4000 | 0.1793 | 0.8270 | 0.8658 | 0.8460 | 0.9633 | |
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| 0.0396 | 7.75 | 4500 | 0.1687 | 0.8363 | 0.8899 | 0.8622 | 0.9672 | |
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| 0.0256 | 8.61 | 5000 | 0.1904 | 0.8315 | 0.8808 | 0.8554 | 0.9665 | |
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| 0.0223 | 9.47 | 5500 | 0.2119 | 0.8424 | 0.8830 | 0.8622 | 0.9653 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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