|
--- |
|
license: mit |
|
base_model: FacebookAI/xlm-roberta-large |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- cnec |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: CNEC1_1_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: 0.8595505617977528 |
|
- name: Recall |
|
type: recall |
|
value: 0.8995189738107964 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8790806999216505 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9695206428373511 |
|
--- |
|
|
|
<!-- 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_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: 0.2397 |
|
- Precision: 0.8596 |
|
- Recall: 0.8995 |
|
- F1: 0.8791 |
|
- Accuracy: 0.9695 |
|
|
|
## 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: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 25 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.3533 | 1.72 | 500 | 0.1415 | 0.7483 | 0.8439 | 0.7933 | 0.9609 | |
|
| 0.1509 | 3.44 | 1000 | 0.1352 | 0.8073 | 0.8685 | 0.8368 | 0.9664 | |
|
| 0.1072 | 5.15 | 1500 | 0.1533 | 0.8151 | 0.8739 | 0.8434 | 0.9674 | |
|
| 0.0778 | 6.87 | 2000 | 0.1740 | 0.8400 | 0.8781 | 0.8586 | 0.9668 | |
|
| 0.059 | 8.59 | 2500 | 0.1676 | 0.8365 | 0.8942 | 0.8644 | 0.9699 | |
|
| 0.0475 | 10.31 | 3000 | 0.1699 | 0.8295 | 0.8813 | 0.8546 | 0.9678 | |
|
| 0.0381 | 12.03 | 3500 | 0.1876 | 0.8418 | 0.8985 | 0.8692 | 0.9686 | |
|
| 0.0287 | 13.75 | 4000 | 0.2100 | 0.8446 | 0.8979 | 0.8705 | 0.9681 | |
|
| 0.0238 | 15.46 | 4500 | 0.2007 | 0.8466 | 0.8995 | 0.8722 | 0.9702 | |
|
| 0.0186 | 17.18 | 5000 | 0.2201 | 0.8568 | 0.8926 | 0.8743 | 0.9689 | |
|
| 0.0161 | 18.9 | 5500 | 0.2200 | 0.8573 | 0.8990 | 0.8776 | 0.9700 | |
|
| 0.014 | 20.62 | 6000 | 0.2326 | 0.8601 | 0.8974 | 0.8784 | 0.9697 | |
|
| 0.0104 | 22.34 | 6500 | 0.2370 | 0.8639 | 0.8990 | 0.8811 | 0.9696 | |
|
| 0.0099 | 24.05 | 7000 | 0.2397 | 0.8596 | 0.8995 | 0.8791 | 0.9695 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.36.2 |
|
- Pytorch 2.1.2+cu121 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.15.0 |
|
|
