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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: 0.8574273197929112
- name: Recall
type: recall
value: 0.889301941346551
- name: F1
type: f1
value: 0.8730738037307381
- name: Accuracy
type: accuracy
value: 0.9718673040706939
---
<!-- 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: 0.1905
- Precision: 0.8574
- Recall: 0.8893
- F1: 0.8731
- Accuracy: 0.9719
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2136 | 1.0 | 7193 | 0.1833 | 0.7605 | 0.8513 | 0.8034 | 0.9620 |
| 0.1556 | 2.0 | 14386 | 0.1683 | 0.8282 | 0.8881 | 0.8571 | 0.9689 |
| 0.1154 | 3.0 | 21579 | 0.1599 | 0.8409 | 0.8819 | 0.8609 | 0.9703 |
| 0.0522 | 4.0 | 28772 | 0.1905 | 0.8574 | 0.8893 | 0.8731 | 0.9719 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|