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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: 0.8205234732031574
- name: Recall
type: recall
value: 0.88604755495738
- name: F1
type: f1
value: 0.8520276100086281
- name: Accuracy
type: accuracy
value: 0.9624497443303798
---
<!-- 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: 0.1914
- Precision: 0.8205
- Recall: 0.8860
- F1: 0.8520
- Accuracy: 0.9624
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4532 | 0.85 | 500 | 0.2036 | 0.7303 | 0.8295 | 0.7767 | 0.9476 |
| 0.2172 | 1.7 | 1000 | 0.1727 | 0.7560 | 0.8591 | 0.8043 | 0.9566 |
| 0.1572 | 2.56 | 1500 | 0.1901 | 0.7733 | 0.8690 | 0.8183 | 0.9566 |
| 0.1341 | 3.41 | 2000 | 0.1661 | 0.7905 | 0.8753 | 0.8307 | 0.9599 |
| 0.1093 | 4.26 | 2500 | 0.1747 | 0.8087 | 0.8856 | 0.8454 | 0.9610 |
| 0.0876 | 5.11 | 3000 | 0.1987 | 0.7949 | 0.8798 | 0.8352 | 0.9588 |
| 0.0752 | 5.96 | 3500 | 0.1827 | 0.8146 | 0.8834 | 0.8476 | 0.9622 |
| 0.0574 | 6.81 | 4000 | 0.1834 | 0.8221 | 0.8937 | 0.8564 | 0.9638 |
| 0.0542 | 7.67 | 4500 | 0.1914 | 0.8205 | 0.8860 | 0.8520 | 0.9624 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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