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---
license: mit
tags:
- generated_from_trainer
datasets:
- harem
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-portuguese-cased_harem-selective-lowC-sm-first-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: harem
type: harem
args: selective
metrics:
- name: Precision
type: precision
value: 0.8
- name: Recall
type: recall
value: 0.8764044943820225
- name: F1
type: f1
value: 0.8364611260053619
- name: Accuracy
type: accuracy
value: 0.9764089121887287
---
<!-- 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. -->
# bert-base-portuguese-cased_harem-selective-lowC-sm-first-ner
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: 0.1160
- Precision: 0.8
- Recall: 0.8764
- F1: 0.8365
- Accuracy: 0.9764
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.055 | 1.0 | 2517 | 0.0934 | 0.81 | 0.9101 | 0.8571 | 0.9699 |
| 0.0236 | 2.0 | 5034 | 0.0883 | 0.8307 | 0.8820 | 0.8556 | 0.9751 |
| 0.0129 | 3.0 | 7551 | 0.1160 | 0.8 | 0.8764 | 0.8365 | 0.9764 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
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