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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: 0.8526912181303116
- name: Recall
type: recall
value: 0.8962779156327544
- name: F1
type: f1
value: 0.8739414468908783
- name: Accuracy
type: accuracy
value: 0.9765807962529274
---
<!-- 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: 0.1428
- Precision: 0.8527
- Recall: 0.8963
- F1: 0.8739
- Accuracy: 0.9766
## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2508 | 1.12 | 500 | 0.1431 | 0.7569 | 0.8481 | 0.7999 | 0.9672 |
| 0.1103 | 2.24 | 1000 | 0.1169 | 0.7717 | 0.8541 | 0.8108 | 0.9704 |
| 0.0731 | 3.36 | 1500 | 0.1134 | 0.8066 | 0.8715 | 0.8378 | 0.9749 |
| 0.0527 | 4.47 | 2000 | 0.1137 | 0.8360 | 0.8928 | 0.8635 | 0.9767 |
| 0.039 | 5.59 | 2500 | 0.1248 | 0.8364 | 0.8854 | 0.8602 | 0.9755 |
| 0.0265 | 6.71 | 3000 | 0.1252 | 0.8427 | 0.8878 | 0.8647 | 0.9769 |
| 0.0206 | 7.83 | 3500 | 0.1424 | 0.8473 | 0.8953 | 0.8707 | 0.9757 |
| 0.0148 | 8.95 | 4000 | 0.1428 | 0.8527 | 0.8963 | 0.8739 | 0.9766 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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