|
--- |
|
license: mit |
|
base_model: xlm-roberta-base |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- smsa |
|
metrics: |
|
- accuracy |
|
- f1 |
|
model-index: |
|
- name: scenario-normal-finetune-clf-data-smsa-model-xlm-roberta-base |
|
results: |
|
- task: |
|
name: Text Classification |
|
type: text-classification |
|
dataset: |
|
name: smsa |
|
type: smsa |
|
config: smsa_nusantara_text |
|
split: validation |
|
args: smsa_nusantara_text |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9222222222222223 |
|
- name: F1 |
|
type: f1 |
|
value: 0.9010725836501758 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# scenario-normal-finetune-clf-data-smsa-model-xlm-roberta-base |
|
|
|
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the smsa dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3511 |
|
- Accuracy: 0.9222 |
|
- F1: 0.9011 |
|
|
|
## 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: 32 |
|
- eval_batch_size: 32 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 6969 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
|
| No log | 0.29 | 100 | 0.4204 | 0.8397 | 0.6487 | |
|
| No log | 0.58 | 200 | 0.3298 | 0.9095 | 0.8696 | |
|
| No log | 0.87 | 300 | 0.2664 | 0.9214 | 0.8843 | |
|
| No log | 1.16 | 400 | 0.2882 | 0.9151 | 0.8849 | |
|
| 0.3642 | 1.45 | 500 | 0.2531 | 0.9175 | 0.8808 | |
|
| 0.3642 | 1.74 | 600 | 0.2847 | 0.9175 | 0.8820 | |
|
| 0.3642 | 2.03 | 700 | 0.2889 | 0.9294 | 0.9060 | |
|
| 0.3642 | 2.33 | 800 | 0.3066 | 0.9270 | 0.8996 | |
|
| 0.3642 | 2.62 | 900 | 0.3736 | 0.9190 | 0.8914 | |
|
| 0.2064 | 2.91 | 1000 | 0.2706 | 0.9214 | 0.8853 | |
|
| 0.2064 | 3.2 | 1100 | 0.3201 | 0.9190 | 0.8878 | |
|
| 0.2064 | 3.49 | 1200 | 0.2372 | 0.9254 | 0.9007 | |
|
| 0.2064 | 3.78 | 1300 | 0.2534 | 0.9190 | 0.8904 | |
|
| 0.2064 | 4.07 | 1400 | 0.3266 | 0.9214 | 0.8939 | |
|
| 0.1543 | 4.36 | 1500 | 0.3405 | 0.9135 | 0.8815 | |
|
| 0.1543 | 4.65 | 1600 | 0.3485 | 0.9238 | 0.8988 | |
|
| 0.1543 | 4.94 | 1700 | 0.3287 | 0.9270 | 0.9011 | |
|
| 0.1543 | 5.23 | 1800 | 0.3631 | 0.9167 | 0.8866 | |
|
| 0.1543 | 5.52 | 1900 | 0.3714 | 0.9167 | 0.8922 | |
|
| 0.1227 | 5.81 | 2000 | 0.3030 | 0.9119 | 0.8794 | |
|
| 0.1227 | 6.1 | 2100 | 0.3363 | 0.9286 | 0.9046 | |
|
| 0.1227 | 6.4 | 2200 | 0.3511 | 0.9222 | 0.9011 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.3 |
|
- Pytorch 2.0.1 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|
