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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: 0.8564668769716088
- name: Recall
type: recall
value: 0.8971499380421314
- name: F1
type: f1
value: 0.876336493847085
- name: Accuracy
type: accuracy
value: 0.9708532522091844
---
<!-- 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: 0.2155
- Precision: 0.8565
- Recall: 0.8971
- F1: 0.8763
- 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: 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4393 | 1.0 | 900 | 0.1671 | 0.7756 | 0.8195 | 0.7969 | 0.9590 |
| 0.1716 | 2.0 | 1800 | 0.1409 | 0.8155 | 0.8583 | 0.8364 | 0.9662 |
| 0.1326 | 3.0 | 2700 | 0.1288 | 0.8203 | 0.8748 | 0.8467 | 0.9687 |
| 0.1027 | 4.0 | 3600 | 0.1408 | 0.8290 | 0.8732 | 0.8505 | 0.9683 |
| 0.0891 | 5.0 | 4500 | 0.1447 | 0.8485 | 0.9000 | 0.8735 | 0.9725 |
| 0.0715 | 6.0 | 5400 | 0.1393 | 0.8561 | 0.8868 | 0.8712 | 0.9713 |
| 0.0644 | 7.0 | 6300 | 0.1586 | 0.8517 | 0.8918 | 0.8713 | 0.9702 |
| 0.0535 | 8.0 | 7200 | 0.1526 | 0.8481 | 0.8810 | 0.8643 | 0.9696 |
| 0.0492 | 9.0 | 8100 | 0.1795 | 0.8529 | 0.8984 | 0.8751 | 0.9702 |
| 0.0391 | 10.0 | 9000 | 0.1903 | 0.8536 | 0.8938 | 0.8733 | 0.9693 |
| 0.0323 | 11.0 | 9900 | 0.1885 | 0.8615 | 0.9046 | 0.8825 | 0.9724 |
| 0.0274 | 12.0 | 10800 | 0.2099 | 0.8585 | 0.9025 | 0.8800 | 0.9696 |
| 0.0237 | 13.0 | 11700 | 0.1944 | 0.8624 | 0.9009 | 0.8812 | 0.9720 |
| 0.0245 | 14.0 | 12600 | 0.2129 | 0.8618 | 0.8967 | 0.8789 | 0.9711 |
| 0.0206 | 15.0 | 13500 | 0.2155 | 0.8565 | 0.8971 | 0.8763 | 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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