update model card README.md
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README.md
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metrics:
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- name: Precision
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type: precision
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value: 0.
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- name: Recall
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type: recall
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value: 0.
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- name: F1
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type: f1
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value: 0.
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- name: Accuracy
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type: accuracy
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value: 0.
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- task:
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type: token-classification
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name: 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: test
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9842606502473917
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verified: true
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- name: Precision
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type: precision
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value: 0.9880888491353608
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verified: true
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- name: Recall
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type: recall
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value: 0.9863977974551678
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verified: true
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- name: F1
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type: f1
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value: 0.9872425991435487
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verified: true
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- name: loss
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type: loss
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value: 0.12697908282279968
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verified: true
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- task:
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type: token-classification
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name: 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: validation
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.979126510974644
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verified: true
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- name: Precision
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type: precision
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value: 0.9846948786709399
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verified: true
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- name: Recall
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type: recall
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value: 0.9839386958155646
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verified: true
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- name: F1
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type: f1
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value: 0.9843166420124387
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verified: true
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- name: loss
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type: loss
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value: 0.17586557567119598
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verified: true
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- task:
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type: token-classification
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name: 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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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9986508230532317
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verified: true
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- name: Precision
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type: precision
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value: 0.9980332928982356
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verified: true
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- name: Recall
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type: recall
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value: 0.998726011303645
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verified: true
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- name: F1
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type: f1
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value: 0.998379531941543
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verified: true
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- name: loss
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type: loss
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value: 0.002737082075327635
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verified: true
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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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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) 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.
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- Recall: 0.
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- F1: 0.
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- Accuracy: 0.
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## Model description
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.0058 | 12.0 | 46968 | nan | 0.
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### Framework versions
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metrics:
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- name: Precision
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type: precision
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value: 0.8762313715584744
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- name: Recall
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type: recall
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value: 0.8966141121736882
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- name: F1
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type: f1
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value: 0.8863055697496168
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- name: Accuracy
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type: accuracy
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value: 0.979500052295785
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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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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) 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: 0.8762
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- Recall: 0.8966
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- F1: 0.8863
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- Accuracy: 0.9795
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## Model description
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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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| 0.0785 | 1.0 | 3914 | nan | 0.7119 | 0.8410 | 0.7711 | 0.9658 |
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| 0.076 | 2.0 | 7828 | nan | 0.8397 | 0.8679 | 0.8536 | 0.9740 |
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| 0.0434 | 3.0 | 11742 | nan | 0.8545 | 0.8666 | 0.8605 | 0.9693 |
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| 0.022 | 4.0 | 15656 | nan | 0.8293 | 0.8573 | 0.8431 | 0.9652 |
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| 0.0284 | 5.0 | 19570 | nan | 0.8789 | 0.8571 | 0.8678 | 0.9776 |
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| 0.029 | 6.0 | 23484 | nan | 0.8521 | 0.8788 | 0.8653 | 0.9771 |
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| 0.0227 | 7.0 | 27398 | nan | 0.7648 | 0.8873 | 0.8215 | 0.9686 |
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| 0.0219 | 8.0 | 31312 | nan | 0.8609 | 0.9026 | 0.8813 | 0.9780 |
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| 0.0121 | 9.0 | 35226 | nan | 0.8746 | 0.8979 | 0.8861 | 0.9812 |
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| 0.0087 | 10.0 | 39140 | nan | 0.8829 | 0.8827 | 0.8828 | 0.9808 |
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| 0.0081 | 11.0 | 43054 | nan | 0.8740 | 0.8749 | 0.8745 | 0.9765 |
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| 0.0058 | 12.0 | 46968 | nan | 0.8838 | 0.8842 | 0.8840 | 0.9788 |
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| 0.0044 | 13.0 | 50882 | nan | 0.869 | 0.8984 | 0.8835 | 0.9788 |
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| 0.002 | 14.0 | 54796 | nan | 0.8762 | 0.8966 | 0.8863 | 0.9795 |
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| 0.0017 | 15.0 | 58710 | nan | 0.8729 | 0.8982 | 0.8854 | 0.9791 |
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### Framework versions
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