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---
license: mit
base_model: neuralmind/bert-large-portuguese-cased
tags:
- generated_from_trainer
datasets:
- harem
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER_harem_bert-large-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: 0.7077353867693384
- name: Recall
type: recall
value: 0.7553231228987672
- name: F1
type: f1
value: 0.7307553306830503
- name: Accuracy
type: accuracy
value: 0.9551379448220711
---
<!-- 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-large-portuguese-cased
This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the harem dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2487
- Precision: 0.7077
- Recall: 0.7553
- F1: 0.7308
- Accuracy: 0.9551
## 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 | 0.6334 | 0.0163 | 0.0078 | 0.0106 | 0.8468 |
| No log | 2.0 | 32 | 0.4537 | 0.2614 | 0.3112 | 0.2841 | 0.8826 |
| No log | 3.0 | 48 | 0.3117 | 0.5262 | 0.5671 | 0.5458 | 0.9231 |
| No log | 4.0 | 64 | 0.2421 | 0.5852 | 0.6631 | 0.6217 | 0.9385 |
| No log | 5.0 | 80 | 0.2099 | 0.5950 | 0.6855 | 0.6370 | 0.9479 |
| No log | 6.0 | 96 | 0.2153 | 0.6810 | 0.7464 | 0.7122 | 0.9551 |
| No log | 7.0 | 112 | 0.2270 | 0.6894 | 0.7198 | 0.7043 | 0.9546 |
| No log | 8.0 | 128 | 0.2213 | 0.6918 | 0.7437 | 0.7168 | 0.9554 |
| No log | 9.0 | 144 | 0.2299 | 0.7021 | 0.7564 | 0.7283 | 0.9545 |
| No log | 10.0 | 160 | 0.2256 | 0.7002 | 0.7591 | 0.7284 | 0.9562 |
| No log | 11.0 | 176 | 0.2169 | 0.7100 | 0.7736 | 0.7404 | 0.9568 |
| No log | 12.0 | 192 | 0.2266 | 0.6981 | 0.7740 | 0.7341 | 0.9571 |
| No log | 13.0 | 208 | 0.2322 | 0.7093 | 0.7620 | 0.7347 | 0.9570 |
| No log | 14.0 | 224 | 0.2487 | 0.7077 | 0.7553 | 0.7308 | 0.9551 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|