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---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-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: 0.9166029074215761
- name: Recall
type: recall
value: 0.9289222021194107
- name: F1
type: f1
value: 0.9227214377406933
- name: Accuracy
type: accuracy
value: 0.9853721218641206
---
<!-- 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. -->
# xlm-roberta-large-finetuned-ner-lenerBr
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.9166
- Recall: 0.9289
- F1: 0.9227
- Accuracy: 0.9854
## 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 | 0.8191 | 0.8167 | 0.8179 | 0.9751 |
| 0.163 | 1.9990 | 978 | nan | 0.8600 | 0.9080 | 0.8833 | 0.9790 |
| 0.0427 | 2.9985 | 1467 | nan | 0.8736 | 0.9163 | 0.8944 | 0.9814 |
| 0.0279 | 4.0 | 1957 | nan | 0.8688 | 0.9191 | 0.8932 | 0.9801 |
| 0.019 | 4.9995 | 2446 | nan | 0.9123 | 0.9196 | 0.9159 | 0.9840 |
| 0.0143 | 5.9990 | 2935 | nan | 0.9008 | 0.9346 | 0.9174 | 0.9842 |
| 0.0112 | 6.9985 | 3424 | nan | 0.9063 | 0.9250 | 0.9156 | 0.9843 |
| 0.0072 | 8.0 | 3914 | nan | 0.8954 | 0.9315 | 0.9131 | 0.9841 |
| 0.0065 | 8.9995 | 4403 | nan | 0.9226 | 0.9245 | 0.9236 | 0.9857 |
| 0.0048 | 9.9949 | 4890 | nan | 0.9166 | 0.9289 | 0.9227 | 0.9854 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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