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---
tags:
- generated_from_trainer
datasets:
- contratos_tceal
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner-bert-large-cased-pt-contratos_tceal
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: contratos_tceal
type: contratos_tceal
config: contratos_tceal
split: validation
args: contratos_tceal
metrics:
- name: Precision
type: precision
value: 0.918525703200776
- name: Recall
type: recall
value: 0.9458964541368403
- name: F1
type: f1
value: 0.9320101697695399
- name: Accuracy
type: accuracy
value: 0.9639352869753552
---
<!-- 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-bert-large-cased-pt-contratos_tceal
This model was trained from scratch on the contratos_tceal dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2229
- Precision: 0.9185
- Recall: 0.9459
- F1: 0.9320
- Accuracy: 0.9639
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 363 | 0.1242 | 0.9164 | 0.9469 | 0.9314 | 0.9696 |
| 0.1307 | 2.0 | 726 | 0.1413 | 0.9197 | 0.9424 | 0.9309 | 0.9664 |
| 0.0831 | 3.0 | 1089 | 0.1366 | 0.9237 | 0.9477 | 0.9356 | 0.9682 |
| 0.0831 | 4.0 | 1452 | 0.1360 | 0.9283 | 0.9489 | 0.9385 | 0.9696 |
| 0.0646 | 5.0 | 1815 | 0.1572 | 0.9171 | 0.9427 | 0.9297 | 0.9646 |
| 0.0473 | 6.0 | 2178 | 0.1674 | 0.9069 | 0.9454 | 0.9257 | 0.9646 |
| 0.0367 | 7.0 | 2541 | 0.1783 | 0.9155 | 0.9414 | 0.9283 | 0.9644 |
| 0.0367 | 8.0 | 2904 | 0.1823 | 0.9244 | 0.9442 | 0.9342 | 0.9656 |
| 0.029 | 9.0 | 3267 | 0.1815 | 0.9190 | 0.9444 | 0.9315 | 0.9655 |
| 0.0227 | 10.0 | 3630 | 0.1945 | 0.9084 | 0.9457 | 0.9267 | 0.9617 |
| 0.0227 | 11.0 | 3993 | 0.1962 | 0.9134 | 0.9442 | 0.9285 | 0.9635 |
| 0.0188 | 12.0 | 4356 | 0.1893 | 0.9203 | 0.9442 | 0.9321 | 0.9651 |
| 0.0134 | 13.0 | 4719 | 0.1982 | 0.9181 | 0.9441 | 0.9309 | 0.9650 |
| 0.0126 | 14.0 | 5082 | 0.1962 | 0.9162 | 0.9447 | 0.9303 | 0.9667 |
| 0.0126 | 15.0 | 5445 | 0.2112 | 0.9196 | 0.9446 | 0.9319 | 0.9642 |
| 0.0099 | 16.0 | 5808 | 0.2138 | 0.9165 | 0.9449 | 0.9305 | 0.9630 |
| 0.007 | 17.0 | 6171 | 0.2110 | 0.9208 | 0.9447 | 0.9326 | 0.9652 |
| 0.0075 | 18.0 | 6534 | 0.2216 | 0.9210 | 0.9452 | 0.9330 | 0.9641 |
| 0.0075 | 19.0 | 6897 | 0.2232 | 0.9191 | 0.9461 | 0.9324 | 0.9640 |
| 0.0062 | 20.0 | 7260 | 0.2229 | 0.9185 | 0.9459 | 0.9320 | 0.9639 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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