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---
license: apache-2.0
base_model: google-bert/bert-large-cased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large-cased-finetuned-ner-lenerBr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: lener_br
type: lener_br
config: lener_br
split: validation
args: lener_br
metrics:
- name: Precision
type: precision
value: 0.8045846203869289
- name: Recall
type: recall
value: 0.82981220657277
- name: F1
type: f1
value: 0.8170037144036318
- name: Accuracy
type: accuracy
value: 0.9644917654463443
---
<!-- 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-large-cased-finetuned-ner-lenerBr
This model is a fine-tuned version of [google-bert/bert-large-cased](https://huggingface.co/google-bert/bert-large-cased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.8046
- Recall: 0.8298
- F1: 0.8170
- Accuracy: 0.9645
## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.9974 | 244 | nan | 0.6627 | 0.7490 | 0.7032 | 0.9402 |
| No log | 1.9990 | 489 | nan | 0.7002 | 0.8005 | 0.7470 | 0.9503 |
| 0.1592 | 2.9964 | 733 | nan | 0.7482 | 0.8080 | 0.7769 | 0.9545 |
| 0.1592 | 3.9980 | 978 | nan | 0.7749 | 0.8166 | 0.7952 | 0.9614 |
| 0.0279 | 4.9995 | 1223 | nan | 0.7845 | 0.7973 | 0.7909 | 0.9634 |
| 0.0279 | 5.9969 | 1467 | nan | 0.7840 | 0.8203 | 0.8017 | 0.9622 |
| 0.0122 | 6.9985 | 1712 | nan | 0.7989 | 0.8224 | 0.8105 | 0.9638 |
| 0.0122 | 8.0 | 1957 | nan | 0.7977 | 0.8286 | 0.8129 | 0.9634 |
| 0.007 | 8.9974 | 2201 | nan | 0.7947 | 0.8265 | 0.8103 | 0.9643 |
| 0.007 | 9.9745 | 2440 | nan | 0.8046 | 0.8298 | 0.8170 | 0.9645 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
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