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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: CNEC2_0_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: 0.8675623800383877
- name: Recall
type: recall
value: 0.8972704714640198
- name: F1
type: f1
value: 0.8821663820444011
- name: Accuracy
type: accuracy
value: 0.9754391100702576
---
<!-- 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. -->
# CNEC2_0_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: 0.1456
- Precision: 0.8676
- Recall: 0.8973
- F1: 0.8822
- 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3439 | 0.56 | 500 | 0.1575 | 0.7882 | 0.8015 | 0.7948 | 0.9605 |
| 0.1636 | 1.12 | 1000 | 0.1242 | 0.8071 | 0.8432 | 0.8248 | 0.9699 |
| 0.1347 | 1.68 | 1500 | 0.1246 | 0.8273 | 0.8486 | 0.8378 | 0.9688 |
| 0.105 | 2.24 | 2000 | 0.1276 | 0.8428 | 0.8645 | 0.8535 | 0.9727 |
| 0.0942 | 2.8 | 2500 | 0.1263 | 0.8412 | 0.8809 | 0.8606 | 0.9734 |
| 0.0778 | 3.36 | 3000 | 0.1178 | 0.8550 | 0.8779 | 0.8663 | 0.9746 |
| 0.0696 | 3.92 | 3500 | 0.1168 | 0.8491 | 0.8878 | 0.8680 | 0.9738 |
| 0.0565 | 4.48 | 4000 | 0.1135 | 0.8377 | 0.8734 | 0.8552 | 0.9734 |
| 0.0532 | 5.04 | 4500 | 0.1218 | 0.8673 | 0.8888 | 0.8779 | 0.9752 |
| 0.0451 | 5.6 | 5000 | 0.1339 | 0.8613 | 0.8878 | 0.8744 | 0.9751 |
| 0.0396 | 6.16 | 5500 | 0.1339 | 0.8595 | 0.8864 | 0.8727 | 0.9751 |
| 0.0331 | 6.72 | 6000 | 0.1361 | 0.8617 | 0.8933 | 0.8772 | 0.9755 |
| 0.0263 | 7.28 | 6500 | 0.1450 | 0.8720 | 0.8958 | 0.8837 | 0.9758 |
| 0.0278 | 7.84 | 7000 | 0.1456 | 0.8676 | 0.8973 | 0.8822 | 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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