|
--- |
|
license: mit |
|
base_model: xlm-roberta-base |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- tweet_sentiment_multilingual |
|
metrics: |
|
- accuracy |
|
- f1 |
|
model-index: |
|
- name: scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_a |
|
results: |
|
- task: |
|
name: Text Classification |
|
type: text-classification |
|
dataset: |
|
name: tweet_sentiment_multilingual |
|
type: tweet_sentiment_multilingual |
|
config: all |
|
split: validation |
|
args: all |
|
metrics: |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.6439043209876543 |
|
- name: F1 |
|
type: f1 |
|
value: 0.6443757148090576 |
|
--- |
|
|
|
<!-- 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-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_a |
|
|
|
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tweet_sentiment_multilingual dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.6822 |
|
- Accuracy: 0.6439 |
|
- F1: 0.6444 |
|
|
|
## 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: 1e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 32 |
|
- seed: 1234 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 30 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| |
|
| 0.9412 | 1.09 | 500 | 0.8062 | 0.6389 | 0.6335 | |
|
| 0.7943 | 2.17 | 1000 | 0.8448 | 0.6451 | 0.6394 | |
|
| 0.7026 | 3.26 | 1500 | 0.8509 | 0.6497 | 0.6438 | |
|
| 0.6019 | 4.35 | 2000 | 0.8999 | 0.6478 | 0.6468 | |
|
| 0.5379 | 5.43 | 2500 | 0.9424 | 0.6312 | 0.6222 | |
|
| 0.4635 | 6.52 | 3000 | 1.0401 | 0.6431 | 0.6439 | |
|
| 0.3985 | 7.61 | 3500 | 1.0584 | 0.6397 | 0.6390 | |
|
| 0.3506 | 8.7 | 4000 | 1.1607 | 0.6443 | 0.6432 | |
|
| 0.3105 | 9.78 | 4500 | 1.1806 | 0.6408 | 0.6423 | |
|
| 0.2712 | 10.87 | 5000 | 1.3112 | 0.6316 | 0.6304 | |
|
| 0.2361 | 11.96 | 5500 | 1.3772 | 0.6466 | 0.6454 | |
|
| 0.2111 | 13.04 | 6000 | 1.4492 | 0.6385 | 0.6396 | |
|
| 0.1885 | 14.13 | 6500 | 1.6604 | 0.6335 | 0.6347 | |
|
| 0.1658 | 15.22 | 7000 | 1.7153 | 0.6358 | 0.6353 | |
|
| 0.1501 | 16.3 | 7500 | 1.7849 | 0.6412 | 0.6427 | |
|
| 0.135 | 17.39 | 8000 | 1.9749 | 0.6416 | 0.6394 | |
|
| 0.1217 | 18.48 | 8500 | 2.0530 | 0.6439 | 0.6431 | |
|
| 0.1112 | 19.57 | 9000 | 2.1378 | 0.6439 | 0.6448 | |
|
| 0.1018 | 20.65 | 9500 | 2.2656 | 0.6393 | 0.6390 | |
|
| 0.0885 | 21.74 | 10000 | 2.3568 | 0.6431 | 0.6438 | |
|
| 0.0897 | 22.83 | 10500 | 2.3852 | 0.6435 | 0.6446 | |
|
| 0.0854 | 23.91 | 11000 | 2.4019 | 0.6327 | 0.6329 | |
|
| 0.0734 | 25.0 | 11500 | 2.5260 | 0.6331 | 0.6333 | |
|
| 0.067 | 26.09 | 12000 | 2.5368 | 0.6470 | 0.6465 | |
|
| 0.0546 | 27.17 | 12500 | 2.6255 | 0.6431 | 0.6441 | |
|
| 0.0581 | 28.26 | 13000 | 2.6467 | 0.6458 | 0.6456 | |
|
| 0.0564 | 29.35 | 13500 | 2.6822 | 0.6439 | 0.6444 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.3 |
|
- Pytorch 2.1.1+cu121 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.13.3 |
|
|
