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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: harem-ner
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: 0.6869415807560137
- name: Recall
type: recall
value: 0.7467314157639149
- name: F1
type: f1
value: 0.7155897619473779
- name: Accuracy
type: accuracy
value: 0.9527588964414234
---
<!-- 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. -->
# harem-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: 0.2411
- Precision: 0.6869
- Recall: 0.7467
- F1: 0.7156
- Accuracy: 0.9528
## 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 | 0.7683 | 0.0 | 0.0 | 0.0 | 0.8358 |
| No log | 2.0 | 32 | 0.4727 | 0.3375 | 0.2955 | 0.3151 | 0.8803 |
| No log | 3.0 | 48 | 0.3498 | 0.4859 | 0.4838 | 0.4848 | 0.9090 |
| No log | 4.0 | 64 | 0.2771 | 0.5651 | 0.6223 | 0.5924 | 0.9354 |
| No log | 5.0 | 80 | 0.2309 | 0.5901 | 0.6743 | 0.6294 | 0.9424 |
| No log | 6.0 | 96 | 0.2195 | 0.6229 | 0.6997 | 0.6590 | 0.9469 |
| No log | 7.0 | 112 | 0.2151 | 0.6239 | 0.6903 | 0.6554 | 0.9480 |
| No log | 8.0 | 128 | 0.2178 | 0.6682 | 0.7236 | 0.6948 | 0.9504 |
| No log | 9.0 | 144 | 0.2210 | 0.6808 | 0.7426 | 0.7104 | 0.9514 |
| No log | 10.0 | 160 | 0.2292 | 0.6863 | 0.7348 | 0.7097 | 0.9512 |
| No log | 11.0 | 176 | 0.2312 | 0.6932 | 0.7452 | 0.7183 | 0.9522 |
| No log | 12.0 | 192 | 0.2258 | 0.6966 | 0.7523 | 0.7234 | 0.9535 |
| No log | 13.0 | 208 | 0.2337 | 0.7076 | 0.7557 | 0.7309 | 0.9537 |
| No log | 14.0 | 224 | 0.2299 | 0.6907 | 0.7549 | 0.7214 | 0.9533 |
| No log | 15.0 | 240 | 0.2381 | 0.6980 | 0.7553 | 0.7255 | 0.9524 |
| No log | 16.0 | 256 | 0.2411 | 0.6869 | 0.7467 | 0.7156 | 0.9528 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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