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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: 0.8532910388580491
- name: Recall
type: recall
value: 0.8888888888888888
- name: F1
type: f1
value: 0.8707262795872951
- name: Accuracy
type: accuracy
value: 0.9697812545270172
---
<!-- 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: 0.2032
- Precision: 0.8533
- Recall: 0.8889
- F1: 0.8707
- Accuracy: 0.9698
## 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: 1
- eval_batch_size: 1
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1913 | 1.0 | 7193 | 0.1739 | 0.7382 | 0.8422 | 0.7868 | 0.9593 |
| 0.1663 | 2.0 | 14386 | 0.1877 | 0.7835 | 0.8579 | 0.8190 | 0.9618 |
| 0.1395 | 3.0 | 21579 | 0.1784 | 0.8391 | 0.8786 | 0.8584 | 0.9679 |
| 0.0647 | 4.0 | 28772 | 0.1968 | 0.8314 | 0.8802 | 0.8551 | 0.9666 |
| 0.0322 | 5.0 | 35965 | 0.2032 | 0.8533 | 0.8889 | 0.8707 | 0.9698 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|