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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: 0.8359161349134002
- name: Recall
type: recall
value: 0.8851351351351351
- name: F1
type: f1
value: 0.8598218471636193
- name: Accuracy
type: accuracy
value: 0.9700420107199769
---
<!-- 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: 0.1918
- Precision: 0.8359
- Recall: 0.8851
- F1: 0.8598
- Accuracy: 0.9700
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2903 | 2.22 | 500 | 0.1438 | 0.7586 | 0.8417 | 0.7980 | 0.9626 |
| 0.1147 | 4.44 | 1000 | 0.1401 | 0.7866 | 0.8629 | 0.8230 | 0.9660 |
| 0.0796 | 6.67 | 1500 | 0.1402 | 0.7956 | 0.8755 | 0.8336 | 0.9677 |
| 0.0561 | 8.89 | 2000 | 0.1419 | 0.8094 | 0.8793 | 0.8429 | 0.9700 |
| 0.0416 | 11.11 | 2500 | 0.1562 | 0.8271 | 0.8793 | 0.8524 | 0.9687 |
| 0.0306 | 13.33 | 3000 | 0.1761 | 0.8309 | 0.8890 | 0.8589 | 0.9702 |
| 0.0233 | 15.56 | 3500 | 0.1785 | 0.8332 | 0.8798 | 0.8559 | 0.9701 |
| 0.0188 | 17.78 | 4000 | 0.1875 | 0.8362 | 0.8847 | 0.8598 | 0.9694 |
| 0.015 | 20.0 | 4500 | 0.1918 | 0.8359 | 0.8851 | 0.8598 | 0.9700 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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