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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- lener_br |
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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-finetuned-lener_br-finetuned-lener-br |
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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: lener_br |
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type: lener_br |
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config: lener_br |
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split: train |
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args: lener_br |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9122490993309316 |
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- name: Recall |
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type: recall |
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value: 0.9162574308606876 |
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- name: F1 |
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type: f1 |
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value: 0.9142488716956804 |
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- name: Accuracy |
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type: accuracy |
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value: 0.982592974434832 |
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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-finetuned-lener_br-finetuned-lener-br |
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This model is a fine-tuned version of [Luciano/xlm-roberta-large-finetuned-lener_br](https://huggingface.co/Luciano/xlm-roberta-large-finetuned-lener_br) on the lener_br dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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- Precision: 0.9122 |
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- Recall: 0.9163 |
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- F1: 0.9142 |
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- Accuracy: 0.9826 |
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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: 2 |
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- eval_batch_size: 2 |
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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: 15 |
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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 | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.068 | 1.0 | 3914 | nan | 0.6196 | 0.8604 | 0.7204 | 0.9568 | |
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| 0.0767 | 2.0 | 7828 | nan | 0.8270 | 0.8710 | 0.8484 | 0.9693 | |
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| 0.0257 | 3.0 | 11742 | nan | 0.7243 | 0.9005 | 0.8029 | 0.9639 | |
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| 0.0193 | 4.0 | 15656 | nan | 0.9010 | 0.8984 | 0.8997 | 0.9821 | |
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| 0.0156 | 5.0 | 19570 | nan | 0.7150 | 0.9121 | 0.8016 | 0.9641 | |
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| 0.0165 | 6.0 | 23484 | nan | 0.7640 | 0.8796 | 0.8177 | 0.9691 | |
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| 0.0225 | 7.0 | 27398 | nan | 0.8851 | 0.9098 | 0.8973 | 0.9803 | |
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| 0.016 | 8.0 | 31312 | nan | 0.9081 | 0.9015 | 0.9048 | 0.9792 | |
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| 0.0078 | 9.0 | 35226 | nan | 0.8941 | 0.8863 | 0.8902 | 0.9788 | |
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| 0.0061 | 10.0 | 39140 | nan | 0.9026 | 0.9002 | 0.9014 | 0.9804 | |
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| 0.0057 | 11.0 | 43054 | nan | 0.8793 | 0.9018 | 0.8904 | 0.9769 | |
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| 0.0044 | 12.0 | 46968 | nan | 0.8790 | 0.9033 | 0.8910 | 0.9785 | |
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| 0.0043 | 13.0 | 50882 | nan | 0.9122 | 0.9163 | 0.9142 | 0.9826 | |
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| 0.0003 | 14.0 | 54796 | nan | 0.9032 | 0.9070 | 0.9051 | 0.9807 | |
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| 0.0025 | 15.0 | 58710 | nan | 0.8903 | 0.9085 | 0.8993 | 0.9798 | |
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### Framework versions |
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- Transformers 4.23.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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