Tiago Barbosa de Lima
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update model card README.md
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README.md
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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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- tapaco
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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: punctuation-taboa-bert
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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: tapaco
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type: tapaco
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config: all_languages
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split: train
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args: all_languages
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metrics:
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- name: Precision
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type: precision
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value: 0.9849559686888454
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- name: Recall
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type: recall
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value: 0.9836325882496642
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- name: F1
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type: f1
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value: 0.9842938336490864
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- name: Accuracy
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type: accuracy
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value: 0.9945622875893589
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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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# punctuation-taboa-bert
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This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the tapaco dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0181
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- Precision: 0.9850
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- Recall: 0.9836
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- F1: 0.9843
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- Accuracy: 0.9946
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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: 4
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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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| 0.0272 | 1.0 | 17438 | 0.0181 | 0.9850 | 0.9836 | 0.9843 | 0.9946 |
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| 0.0234 | 2.0 | 34876 | 0.0196 | 0.9870 | 0.9853 | 0.9862 | 0.9948 |
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| 0.0092 | 3.0 | 52314 | 0.0233 | 0.9874 | 0.9853 | 0.9864 | 0.9950 |
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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 2.5.2
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- Tokenizers 0.13.1
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