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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: CNEC_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: 0.8548310328415041
- name: Recall
type: recall
value: 0.8913151364764268
- name: F1
type: f1
value: 0.8726919339164239
- name: Accuracy
type: accuracy
value: 0.9753512880562061
---
<!-- 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. -->
# CNEC_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: 0.1540
- Precision: 0.8548
- Recall: 0.8913
- F1: 0.8727
- Accuracy: 0.9754
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2864 | 0.56 | 500 | 0.1328 | 0.7015 | 0.8119 | 0.7527 | 0.9629 |
| 0.13 | 1.12 | 1000 | 0.1221 | 0.7836 | 0.8734 | 0.8261 | 0.9701 |
| 0.0972 | 1.68 | 1500 | 0.1140 | 0.7836 | 0.8610 | 0.8205 | 0.9710 |
| 0.0807 | 2.24 | 2000 | 0.1244 | 0.8032 | 0.8730 | 0.8366 | 0.9730 |
| 0.0626 | 2.8 | 2500 | 0.1135 | 0.8104 | 0.8844 | 0.8458 | 0.9755 |
| 0.0451 | 3.36 | 3000 | 0.1371 | 0.8305 | 0.8824 | 0.8556 | 0.9733 |
| 0.0397 | 3.92 | 3500 | 0.1251 | 0.8307 | 0.8814 | 0.8553 | 0.9736 |
| 0.0244 | 4.48 | 4000 | 0.1441 | 0.8370 | 0.8794 | 0.8577 | 0.9740 |
| 0.0257 | 5.04 | 4500 | 0.1319 | 0.8541 | 0.8888 | 0.8711 | 0.9759 |
| 0.0164 | 5.6 | 5000 | 0.1465 | 0.8421 | 0.8868 | 0.8639 | 0.9754 |
| 0.013 | 6.16 | 5500 | 0.1494 | 0.8473 | 0.8868 | 0.8666 | 0.9751 |
| 0.0108 | 6.72 | 6000 | 0.1540 | 0.8548 | 0.8913 | 0.8727 | 0.9754 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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