|
--- |
|
license: mit |
|
base_model: FacebookAI/xlm-roberta-large |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- cnec |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: CNEC2_0_extended_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: 0.8860882210373243 |
|
- name: Recall |
|
type: recall |
|
value: 0.9071960297766749 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8965179009318294 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9772540983606557 |
|
--- |
|
|
|
<!-- 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_extended_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: 0.1919 |
|
- Precision: 0.8861 |
|
- Recall: 0.9072 |
|
- F1: 0.8965 |
|
- Accuracy: 0.9773 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.1664 | 1.12 | 1000 | 0.1312 | 0.8299 | 0.8521 | 0.8408 | 0.9695 | |
|
| 0.1153 | 2.24 | 2000 | 0.1121 | 0.8283 | 0.8640 | 0.8458 | 0.9722 | |
|
| 0.0815 | 3.36 | 3000 | 0.1159 | 0.8523 | 0.8531 | 0.8527 | 0.9735 | |
|
| 0.0633 | 4.48 | 4000 | 0.1166 | 0.8515 | 0.8819 | 0.8664 | 0.9750 | |
|
| 0.0472 | 5.6 | 5000 | 0.1624 | 0.8635 | 0.8918 | 0.8774 | 0.9735 | |
|
| 0.0369 | 6.72 | 6000 | 0.1476 | 0.8710 | 0.8983 | 0.8844 | 0.9770 | |
|
| 0.0325 | 7.84 | 7000 | 0.1590 | 0.8710 | 0.8943 | 0.8825 | 0.9752 | |
|
| 0.0268 | 8.96 | 8000 | 0.1698 | 0.8709 | 0.9037 | 0.8870 | 0.9761 | |
|
| 0.0236 | 10.08 | 9000 | 0.1721 | 0.8807 | 0.9087 | 0.8945 | 0.9763 | |
|
| 0.0125 | 11.2 | 10000 | 0.1843 | 0.8781 | 0.9047 | 0.8912 | 0.9768 | |
|
| 0.009 | 12.32 | 11000 | 0.1971 | 0.8789 | 0.9077 | 0.8931 | 0.9766 | |
|
| 0.0097 | 13.44 | 12000 | 0.1823 | 0.8857 | 0.9077 | 0.8966 | 0.9775 | |
|
| 0.0077 | 14.56 | 13000 | 0.1919 | 0.8861 | 0.9072 | 0.8965 | 0.9773 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.36.2 |
|
- Pytorch 2.1.2+cu121 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.15.0 |
|
|
