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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: CNEC1_1_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: 0.831814415907208
- name: Recall
type: recall
value: 0.887709991158267
- name: F1
type: f1
value: 0.8588537211291701
- name: Accuracy
type: accuracy
value: 0.9631523478668176
---
<!-- 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. -->
# CNEC1_1_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: 0.1988
- Precision: 0.8318
- Recall: 0.8877
- F1: 0.8589
- Accuracy: 0.9632
## 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: 0.1
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.0776 | 0.85 | 500 | 0.3123 | 0.5698 | 0.6799 | 0.6200 | 0.9204 |
| 0.3031 | 1.7 | 1000 | 0.2037 | 0.7176 | 0.8143 | 0.7629 | 0.9474 |
| 0.2204 | 2.56 | 1500 | 0.1951 | 0.7407 | 0.8400 | 0.7872 | 0.9496 |
| 0.18 | 3.41 | 2000 | 0.1868 | 0.7400 | 0.8546 | 0.7932 | 0.9544 |
| 0.1501 | 4.26 | 2500 | 0.1725 | 0.7852 | 0.8660 | 0.8236 | 0.9590 |
| 0.1209 | 5.11 | 3000 | 0.1842 | 0.8026 | 0.8859 | 0.8422 | 0.9609 |
| 0.1061 | 5.96 | 3500 | 0.1814 | 0.7875 | 0.8749 | 0.8289 | 0.9616 |
| 0.0833 | 6.81 | 4000 | 0.1893 | 0.8163 | 0.8899 | 0.8515 | 0.9626 |
| 0.0771 | 7.67 | 4500 | 0.1847 | 0.8244 | 0.8859 | 0.8540 | 0.9623 |
| 0.0603 | 8.52 | 5000 | 0.1875 | 0.8297 | 0.8917 | 0.8596 | 0.9637 |
| 0.0569 | 9.37 | 5500 | 0.1988 | 0.8318 | 0.8877 | 0.8589 | 0.9632 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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