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erickrribeiro
/
ner_model

Token Classification
Transformers
Safetensors
bert
Generated from Trainer
Eval Results
Inference Endpoints
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ner_model
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---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
datasets:
- __main__
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: __main__
      type: __main__
      config: local
      split: test
      args: local
    metrics:
    - name: Precision
      type: precision
      value: 0.5687651985949743
    - name: Recall
      type: recall
      value: 0.6082935991908683
    - name: F1
      type: f1
      value: 0.5878656705997346
    - name: Accuracy
      type: accuracy
      value: 0.7791311866764413
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# ner_model

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.
It achieves the following results on the evaluation set:
- Loss: 0.6378
- Precision: 0.5688
- Recall: 0.6083
- F1: 0.5879
- Accuracy: 0.7791

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6703        | 1.0   | 5737  | 0.6732          | 0.5189    | 0.5700 | 0.5432 | 0.7605   |
| 0.5251        | 2.0   | 11474 | 0.6378          | 0.5688    | 0.6083 | 0.5879 | 0.7791   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.15.0