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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: 0.5783305117853887
- name: Recall
type: recall
value: 0.6134825252106645
- name: F1
type: f1
value: 0.5953881217321357
- name: Accuracy
type: accuracy
value: 0.7670984455958549
---
<!-- 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: 1.5136
- Precision: 0.5783
- Recall: 0.6135
- F1: 0.5954
- Accuracy: 0.7671
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7447 | 1.0 | 5905 | 0.7678 | 0.4966 | 0.5209 | 0.5085 | 0.7409 |
| 0.6153 | 2.0 | 11810 | 0.7378 | 0.5628 | 0.5600 | 0.5614 | 0.7624 |
| 0.4623 | 3.0 | 17715 | 0.7959 | 0.5449 | 0.5836 | 0.5636 | 0.7573 |
| 0.3629 | 4.0 | 23620 | 0.8921 | 0.5679 | 0.6017 | 0.5843 | 0.7631 |
| 0.246 | 5.0 | 29525 | 1.0286 | 0.5878 | 0.5955 | 0.5916 | 0.7685 |
| 0.1923 | 6.0 | 35430 | 1.2142 | 0.5926 | 0.5957 | 0.5941 | 0.7689 |
| 0.1477 | 7.0 | 41335 | 1.3019 | 0.5681 | 0.6091 | 0.5879 | 0.7591 |
| 0.1214 | 8.0 | 47240 | 1.4101 | 0.5834 | 0.6110 | 0.5969 | 0.7659 |
| 0.0793 | 9.0 | 53145 | 1.4745 | 0.5848 | 0.6136 | 0.5989 | 0.7688 |
| 0.0733 | 10.0 | 59050 | 1.5136 | 0.5783 | 0.6135 | 0.5954 | 0.7671 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.15.0
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