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erickrribeiro
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ner_model

Token Classification
Transformers
Safetensors
bert
Generated from Trainer
Eval Results
Inference Endpoints
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  1. README.md +20 -12
  2. model.safetensors +1 -1
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.5687651985949743
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  - name: Recall
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  type: recall
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- value: 0.6082935991908683
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  - name: F1
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  type: f1
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- value: 0.5878656705997346
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  - name: Accuracy
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  type: accuracy
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- value: 0.7791311866764413
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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
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the __main__ dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6378
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- - Precision: 0.5688
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- - Recall: 0.6083
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- - F1: 0.5879
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- - Accuracy: 0.7791
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  ## Model description
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@@ -73,14 +73,22 @@ The following hyperparameters were used during training:
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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: 2
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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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- | 0.6703 | 1.0 | 5737 | 0.6732 | 0.5189 | 0.5700 | 0.5432 | 0.7605 |
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- | 0.5251 | 2.0 | 11474 | 0.6378 | 0.5688 | 0.6083 | 0.5879 | 0.7791 |
 
 
 
 
 
 
 
 
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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: 0.5783305117853887
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  - name: Recall
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  type: recall
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+ value: 0.6134825252106645
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  - name: F1
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  type: f1
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+ value: 0.5953881217321357
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  - name: Accuracy
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  type: accuracy
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+ value: 0.7670984455958549
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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 [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the __main__ dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.5136
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+ - Precision: 0.5783
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+ - Recall: 0.6135
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+ - F1: 0.5954
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+ - Accuracy: 0.7671
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  ## Model description
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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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+ | 0.7447 | 1.0 | 5905 | 0.7678 | 0.4966 | 0.5209 | 0.5085 | 0.7409 |
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+ | 0.6153 | 2.0 | 11810 | 0.7378 | 0.5628 | 0.5600 | 0.5614 | 0.7624 |
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+ | 0.4623 | 3.0 | 17715 | 0.7959 | 0.5449 | 0.5836 | 0.5636 | 0.7573 |
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+ | 0.3629 | 4.0 | 23620 | 0.8921 | 0.5679 | 0.6017 | 0.5843 | 0.7631 |
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+ | 0.246 | 5.0 | 29525 | 1.0286 | 0.5878 | 0.5955 | 0.5916 | 0.7685 |
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+ | 0.1923 | 6.0 | 35430 | 1.2142 | 0.5926 | 0.5957 | 0.5941 | 0.7689 |
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+ | 0.1477 | 7.0 | 41335 | 1.3019 | 0.5681 | 0.6091 | 0.5879 | 0.7591 |
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+ | 0.1214 | 8.0 | 47240 | 1.4101 | 0.5834 | 0.6110 | 0.5969 | 0.7659 |
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+ | 0.0793 | 9.0 | 53145 | 1.4745 | 0.5848 | 0.6136 | 0.5989 | 0.7688 |
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+ | 0.0733 | 10.0 | 59050 | 1.5136 | 0.5783 | 0.6135 | 0.5954 | 0.7671 |
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  ### Framework versions
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