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---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
datasets:
- harem
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER_harem_bert-base-portuguese-cased
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: harem
type: harem
config: default
split: test
args: default
metrics:
- name: Precision
type: precision
value: 0.6852879944482998
- name: Recall
type: recall
value: 0.7377661561449383
- name: F1
type: f1
value: 0.7105594531390537
- name: Accuracy
type: accuracy
value: 0.952219112355058
---
<!-- 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_harem_bert-base-portuguese-cased
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the harem dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2351
- Precision: 0.6853
- Recall: 0.7378
- F1: 0.7106
- Accuracy: 0.9522
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 16 | 0.7692 | 0.0 | 0.0 | 0.0 | 0.8358 |
| No log | 2.0 | 32 | 0.4831 | 0.3140 | 0.2731 | 0.2921 | 0.8790 |
| No log | 3.0 | 48 | 0.3405 | 0.4692 | 0.4897 | 0.4793 | 0.9119 |
| No log | 4.0 | 64 | 0.2747 | 0.5481 | 0.6156 | 0.5799 | 0.9340 |
| No log | 5.0 | 80 | 0.2282 | 0.6077 | 0.6758 | 0.6399 | 0.9443 |
| No log | 6.0 | 96 | 0.2145 | 0.6267 | 0.6892 | 0.6565 | 0.9479 |
| No log | 7.0 | 112 | 0.2223 | 0.6395 | 0.6926 | 0.6650 | 0.9493 |
| No log | 8.0 | 128 | 0.2100 | 0.6822 | 0.7378 | 0.7089 | 0.9530 |
| No log | 9.0 | 144 | 0.2077 | 0.6810 | 0.7497 | 0.7137 | 0.9537 |
| No log | 10.0 | 160 | 0.2173 | 0.6846 | 0.7460 | 0.7140 | 0.9523 |
| No log | 11.0 | 176 | 0.2226 | 0.7001 | 0.7594 | 0.7285 | 0.9542 |
| No log | 12.0 | 192 | 0.2204 | 0.7015 | 0.7568 | 0.7281 | 0.9538 |
| No log | 13.0 | 208 | 0.2278 | 0.6746 | 0.7411 | 0.7063 | 0.9533 |
| No log | 14.0 | 224 | 0.2351 | 0.6853 | 0.7378 | 0.7106 | 0.9522 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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