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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: 0.8533541341653667
- name: Recall
type: recall
value: 0.8770710849812934
- name: F1
type: f1
value: 0.8650500790722193
- name: Accuracy
type: accuracy
value: 0.9670664608320468
---
<!-- 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: 0.1498
- Precision: 0.8534
- Recall: 0.8771
- F1: 0.8651
- Accuracy: 0.9671
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3961 | 1.0 | 581 | 0.1800 | 0.8004 | 0.8231 | 0.8116 | 0.9560 |
| 0.1772 | 2.0 | 1162 | 0.1518 | 0.8357 | 0.8648 | 0.8500 | 0.9642 |
| 0.1266 | 3.0 | 1743 | 0.1545 | 0.8377 | 0.8717 | 0.8544 | 0.9680 |
| 0.1043 | 4.0 | 2324 | 0.1472 | 0.8473 | 0.8691 | 0.8580 | 0.9656 |
| 0.0804 | 5.0 | 2905 | 0.1498 | 0.8534 | 0.8771 | 0.8651 | 0.9671 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|