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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: 0.8299725022914757
- name: Recall
type: recall
value: 0.874034749034749
- name: F1
type: f1
value: 0.8514339445228021
- name: Accuracy
type: accuracy
value: 0.9687092568448501
---
<!-- 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: 0.1727
- Precision: 0.8300
- Recall: 0.8740
- F1: 0.8514
- Accuracy: 0.9687
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3664 | 0.56 | 500 | 0.1708 | 0.6886 | 0.8132 | 0.7457 | 0.9536 |
| 0.1841 | 1.11 | 1000 | 0.1512 | 0.7474 | 0.8470 | 0.7941 | 0.9631 |
| 0.1528 | 1.67 | 1500 | 0.1650 | 0.7530 | 0.8181 | 0.7842 | 0.9612 |
| 0.1313 | 2.22 | 2000 | 0.1598 | 0.7809 | 0.8687 | 0.8225 | 0.9656 |
| 0.1094 | 2.78 | 2500 | 0.1421 | 0.7791 | 0.8475 | 0.8118 | 0.9636 |
| 0.0897 | 3.33 | 3000 | 0.1395 | 0.7958 | 0.8634 | 0.8282 | 0.9669 |
| 0.0864 | 3.89 | 3500 | 0.1454 | 0.7897 | 0.8789 | 0.8319 | 0.9664 |
| 0.0674 | 4.44 | 4000 | 0.1524 | 0.8174 | 0.8663 | 0.8411 | 0.9675 |
| 0.0689 | 5.0 | 4500 | 0.1475 | 0.8178 | 0.8687 | 0.8425 | 0.9674 |
| 0.05 | 5.56 | 5000 | 0.1628 | 0.8257 | 0.8731 | 0.8487 | 0.9676 |
| 0.0521 | 6.11 | 5500 | 0.1614 | 0.8257 | 0.8644 | 0.8446 | 0.9668 |
| 0.0409 | 6.67 | 6000 | 0.1648 | 0.8258 | 0.8740 | 0.8492 | 0.9681 |
| 0.0345 | 7.22 | 6500 | 0.1684 | 0.8295 | 0.8711 | 0.8498 | 0.9682 |
| 0.0302 | 7.78 | 7000 | 0.1727 | 0.8300 | 0.8740 | 0.8514 | 0.9687 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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