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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: 0.8515562649640862
- name: Recall
type: recall
value: 0.8814539446509707
- name: F1
type: f1
value: 0.8662472092551248
- name: Accuracy
type: accuracy
value: 0.9700709836303056
---
<!-- 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: 0.2179
- Precision: 0.8516
- Recall: 0.8815
- F1: 0.8662
- Accuracy: 0.9701
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.267 | 1.0 | 7193 | 0.2806 | 0.7707 | 0.8009 | 0.7855 | 0.9525 |
| 0.1977 | 2.0 | 14386 | 0.1792 | 0.8151 | 0.8451 | 0.8299 | 0.9616 |
| 0.1767 | 3.0 | 21579 | 0.1935 | 0.8293 | 0.8711 | 0.8497 | 0.9662 |
| 0.0929 | 4.0 | 28772 | 0.2219 | 0.8382 | 0.8860 | 0.8614 | 0.9677 |
| 0.0788 | 5.0 | 35965 | 0.2179 | 0.8516 | 0.8815 | 0.8662 | 0.9701 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|