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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-large-uncased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large-uncased-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: 0.8195459032576505
- name: Recall
type: recall
value: 0.8534128289473685
- name: F1
type: f1
value: 0.8361365696444758
- name: Accuracy
type: accuracy
value: 0.9658050781203017
---
<!-- 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-uncased-finetuned-ner-lenerBr
This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.8195
- Recall: 0.8534
- F1: 0.8361
- Accuracy: 0.9658
## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.9995 | 489 | nan | 0.6811 | 0.7451 | 0.7116 | 0.9503 |
| 0.1982 | 1.9990 | 978 | nan | 0.7258 | 0.8314 | 0.7750 | 0.9536 |
| 0.0517 | 2.9985 | 1467 | nan | 0.7487 | 0.8238 | 0.7845 | 0.9587 |
| 0.0289 | 4.0 | 1957 | nan | 0.7801 | 0.8684 | 0.8219 | 0.9641 |
| 0.0191 | 4.9995 | 2446 | nan | 0.7986 | 0.8567 | 0.8266 | 0.9665 |
| 0.0138 | 5.9990 | 2935 | nan | 0.8120 | 0.8491 | 0.8302 | 0.9642 |
| 0.0097 | 6.9985 | 3424 | nan | 0.8201 | 0.8643 | 0.8416 | 0.9663 |
| 0.0076 | 8.0 | 3914 | nan | 0.8079 | 0.8672 | 0.8365 | 0.9660 |
| 0.0053 | 8.9995 | 4403 | nan | 0.8211 | 0.8409 | 0.8309 | 0.9662 |
| 0.0041 | 9.9949 | 4890 | nan | 0.8195 | 0.8534 | 0.8361 | 0.9658 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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