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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_Supertypes_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: 0.8317152103559871
- name: Recall
type: recall
value: 0.8682432432432432
- name: F1
type: f1
value: 0.8495867768595041
- name: Accuracy
type: accuracy
value: 0.9680139069969579
---
<!-- 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_Supertypes_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: 0.2072
- Precision: 0.8317
- Recall: 0.8682
- F1: 0.8496
- Accuracy: 0.9680
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2727 | 1.11 | 500 | 0.1414 | 0.7268 | 0.8012 | 0.7622 | 0.9594 |
| 0.1146 | 2.22 | 1000 | 0.1338 | 0.7697 | 0.8581 | 0.8115 | 0.9657 |
| 0.0725 | 3.33 | 1500 | 0.1444 | 0.7953 | 0.8625 | 0.8275 | 0.9668 |
| 0.0492 | 4.44 | 2000 | 0.1513 | 0.8085 | 0.8760 | 0.8409 | 0.9675 |
| 0.0388 | 5.56 | 2500 | 0.1604 | 0.8257 | 0.8731 | 0.8487 | 0.9674 |
| 0.0244 | 6.67 | 3000 | 0.1754 | 0.8278 | 0.8629 | 0.8450 | 0.9666 |
| 0.0169 | 7.78 | 3500 | 0.1877 | 0.8282 | 0.8653 | 0.8464 | 0.9677 |
| 0.0102 | 8.89 | 4000 | 0.1974 | 0.8252 | 0.8634 | 0.8439 | 0.9674 |
| 0.0068 | 10.0 | 4500 | 0.2072 | 0.8317 | 0.8682 | 0.8496 | 0.9680 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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