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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: 0.848714069591528
- name: Recall
type: recall
value: 0.8995189738107964
- name: F1
type: f1
value: 0.8733783082511676
- name: Accuracy
type: accuracy
value: 0.9711435696473103
---
<!-- 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: 0.1689
- Precision: 0.8487
- Recall: 0.8995
- F1: 0.8734
- Accuracy: 0.9711
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3372 | 1.72 | 500 | 0.1525 | 0.7806 | 0.8632 | 0.8198 | 0.9639 |
| 0.117 | 3.44 | 1000 | 0.1341 | 0.8162 | 0.8899 | 0.8514 | 0.9702 |
| 0.077 | 5.15 | 1500 | 0.1457 | 0.8204 | 0.8765 | 0.8475 | 0.9672 |
| 0.0548 | 6.87 | 2000 | 0.1759 | 0.8449 | 0.8910 | 0.8673 | 0.9690 |
| 0.037 | 8.59 | 2500 | 0.1689 | 0.8487 | 0.8995 | 0.8734 | 0.9711 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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