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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: 0.8074141048824593
- name: Recall
type: recall
value: 0.861969111969112
- name: F1
type: f1
value: 0.8338001867413634
- name: Accuracy
type: accuracy
value: 0.9655222367086774
---
<!-- 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: 0.2044
- Precision: 0.8074
- Recall: 0.8620
- F1: 0.8338
- Accuracy: 0.9655
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5564 | 1.11 | 500 | 0.1852 | 0.6302 | 0.7558 | 0.6873 | 0.9502 |
| 0.1786 | 2.22 | 1000 | 0.1552 | 0.6952 | 0.8069 | 0.7469 | 0.9568 |
| 0.1219 | 3.33 | 1500 | 0.1665 | 0.6860 | 0.8214 | 0.7476 | 0.9577 |
| 0.087 | 4.44 | 2000 | 0.1616 | 0.7572 | 0.8263 | 0.7902 | 0.9595 |
| 0.0689 | 5.56 | 2500 | 0.1679 | 0.7670 | 0.8243 | 0.7946 | 0.9616 |
| 0.0442 | 6.67 | 3000 | 0.1612 | 0.7346 | 0.8364 | 0.7822 | 0.9631 |
| 0.0353 | 7.78 | 3500 | 0.1864 | 0.8099 | 0.8576 | 0.8331 | 0.9653 |
| 0.0205 | 8.89 | 4000 | 0.1950 | 0.8026 | 0.8653 | 0.8328 | 0.9654 |
| 0.0133 | 10.0 | 4500 | 0.2044 | 0.8074 | 0.8620 | 0.8338 | 0.9655 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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