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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: 0.8314350797266514
- name: Recall
type: recall
value: 0.8807915057915058
- name: F1
type: f1
value: 0.8554019217248652
- name: Accuracy
type: accuracy
value: 0.970911198029842
---
<!-- 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: 0.1616
- Precision: 0.8314
- Recall: 0.8808
- F1: 0.8554
- Accuracy: 0.9709
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3363 | 1.11 | 500 | 0.1563 | 0.7344 | 0.8234 | 0.7763 | 0.9607 |
| 0.1372 | 2.22 | 1000 | 0.1308 | 0.7641 | 0.8692 | 0.8133 | 0.9652 |
| 0.0998 | 3.33 | 1500 | 0.1368 | 0.7912 | 0.8668 | 0.8273 | 0.9671 |
| 0.077 | 4.44 | 2000 | 0.1360 | 0.8079 | 0.8707 | 0.8381 | 0.9690 |
| 0.0623 | 5.56 | 2500 | 0.1421 | 0.8181 | 0.8707 | 0.8436 | 0.9686 |
| 0.0458 | 6.67 | 3000 | 0.1488 | 0.8129 | 0.8764 | 0.8435 | 0.9706 |
| 0.0382 | 7.78 | 3500 | 0.1585 | 0.8320 | 0.8745 | 0.8527 | 0.9693 |
| 0.0299 | 8.89 | 4000 | 0.1585 | 0.8291 | 0.8755 | 0.8516 | 0.9705 |
| 0.0257 | 10.0 | 4500 | 0.1616 | 0.8314 | 0.8808 | 0.8554 | 0.9709 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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