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---
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
datasets:
- cnec
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: CNEC1_1_extended_xlm-roberta-large
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cnec
type: cnec
config: default
split: validation
args: default
metrics:
- name: Precision
type: precision
value: 0.8424273329933707
- name: Recall
type: recall
value: 0.882950293960449
- name: F1
type: f1
value: 0.8622129436325678
- name: Accuracy
type: accuracy
value: 0.9652851996991648
---
<!-- 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. -->
# CNEC1_1_extended_xlm-roberta-large
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2119
- Precision: 0.8424
- Recall: 0.8830
- F1: 0.8622
- Accuracy: 0.9653
## 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: 5e-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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3746 | 0.86 | 500 | 0.1861 | 0.7228 | 0.8097 | 0.7638 | 0.9523 |
| 0.2127 | 1.72 | 1000 | 0.1635 | 0.7829 | 0.8461 | 0.8133 | 0.9611 |
| 0.1494 | 2.58 | 1500 | 0.1704 | 0.7579 | 0.8466 | 0.7998 | 0.9546 |
| 0.1274 | 3.44 | 2000 | 0.1800 | 0.8003 | 0.8675 | 0.8325 | 0.9615 |
| 0.0987 | 4.3 | 2500 | 0.1511 | 0.8025 | 0.8883 | 0.8432 | 0.9657 |
| 0.0827 | 5.16 | 3000 | 0.1910 | 0.8179 | 0.8739 | 0.8450 | 0.9630 |
| 0.0677 | 6.02 | 3500 | 0.1655 | 0.8374 | 0.8808 | 0.8586 | 0.9689 |
| 0.0475 | 6.88 | 4000 | 0.1793 | 0.8270 | 0.8658 | 0.8460 | 0.9633 |
| 0.0396 | 7.75 | 4500 | 0.1687 | 0.8363 | 0.8899 | 0.8622 | 0.9672 |
| 0.0256 | 8.61 | 5000 | 0.1904 | 0.8315 | 0.8808 | 0.8554 | 0.9665 |
| 0.0223 | 9.47 | 5500 | 0.2119 | 0.8424 | 0.8830 | 0.8622 | 0.9653 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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