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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: 0.8750653423941454
- name: Recall
type: recall
value: 0.89470871191876
- name: F1
type: f1
value: 0.8847780126849896
- name: Accuracy
type: accuracy
value: 0.9699164786446582
---
<!-- 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: 0.2020
- Precision: 0.8751
- Recall: 0.8947
- F1: 0.8848
- Accuracy: 0.9699
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3776 | 1.0 | 581 | 0.1732 | 0.7868 | 0.8423 | 0.8136 | 0.9580 |
| 0.1773 | 2.0 | 1162 | 0.1476 | 0.8243 | 0.8675 | 0.8453 | 0.9625 |
| 0.127 | 3.0 | 1743 | 0.1522 | 0.8373 | 0.8691 | 0.8529 | 0.9654 |
| 0.1057 | 4.0 | 2324 | 0.1516 | 0.8604 | 0.8728 | 0.8665 | 0.9665 |
| 0.0852 | 5.0 | 2905 | 0.1555 | 0.8501 | 0.8883 | 0.8688 | 0.9700 |
| 0.069 | 6.0 | 3486 | 0.1847 | 0.8637 | 0.8910 | 0.8771 | 0.9681 |
| 0.0452 | 7.0 | 4067 | 0.1751 | 0.8666 | 0.8851 | 0.8757 | 0.9682 |
| 0.0385 | 8.0 | 4648 | 0.1968 | 0.8626 | 0.8888 | 0.8755 | 0.9690 |
| 0.0326 | 9.0 | 5229 | 0.1932 | 0.8717 | 0.8936 | 0.8826 | 0.9704 |
| 0.026 | 10.0 | 5810 | 0.2020 | 0.8751 | 0.8947 | 0.8848 | 0.9699 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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