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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: 0.8311333636777424
- name: Recall
type: recall
value: 0.8812741312741312
- name: F1
type: f1
value: 0.8554696650269384
- name: Accuracy
type: accuracy
value: 0.9682167173692597
---
<!-- 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: 0.1827
- Precision: 0.8311
- Recall: 0.8813
- F1: 0.8555
- Accuracy: 0.9682
## 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
- lr_scheduler_warmup_ratio: 0.01
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.0633 | 0.56 | 500 | 0.2514 | 0.4967 | 0.6544 | 0.5648 | 0.9322 |
| 0.2454 | 1.11 | 1000 | 0.2044 | 0.6504 | 0.7901 | 0.7134 | 0.9532 |
| 0.2064 | 1.67 | 1500 | 0.1721 | 0.7254 | 0.7828 | 0.7530 | 0.9562 |
| 0.1698 | 2.22 | 2000 | 0.1755 | 0.7472 | 0.8388 | 0.7904 | 0.9604 |
| 0.1472 | 2.78 | 2500 | 0.1478 | 0.7547 | 0.8417 | 0.7958 | 0.9624 |
| 0.1244 | 3.33 | 3000 | 0.1516 | 0.7934 | 0.8412 | 0.8166 | 0.9638 |
| 0.12 | 3.89 | 3500 | 0.1366 | 0.7851 | 0.8692 | 0.8250 | 0.9665 |
| 0.0946 | 4.44 | 4000 | 0.1678 | 0.7815 | 0.8494 | 0.8141 | 0.9652 |
| 0.1024 | 5.0 | 4500 | 0.1389 | 0.7756 | 0.8509 | 0.8115 | 0.9649 |
| 0.0765 | 5.56 | 5000 | 0.1563 | 0.7824 | 0.8571 | 0.8181 | 0.9663 |
| 0.0802 | 6.11 | 5500 | 0.1677 | 0.8024 | 0.8586 | 0.8296 | 0.9646 |
| 0.0612 | 6.67 | 6000 | 0.1723 | 0.8068 | 0.8769 | 0.8404 | 0.9662 |
| 0.0529 | 7.22 | 6500 | 0.1698 | 0.8230 | 0.8774 | 0.8493 | 0.9686 |
| 0.0476 | 7.78 | 7000 | 0.1648 | 0.8271 | 0.8702 | 0.8481 | 0.9689 |
| 0.0487 | 8.33 | 7500 | 0.1721 | 0.8287 | 0.8707 | 0.8491 | 0.9683 |
| 0.0392 | 8.89 | 8000 | 0.1787 | 0.8222 | 0.8769 | 0.8487 | 0.9681 |
| 0.0361 | 9.44 | 8500 | 0.1803 | 0.8392 | 0.8818 | 0.8600 | 0.9682 |
| 0.034 | 10.0 | 9000 | 0.1827 | 0.8311 | 0.8813 | 0.8555 | 0.9682 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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