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---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- tweet_sentiment_multilingual
metrics:
- accuracy
- f1
model-index:
- name: scenario-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_
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: 0.5767746913580247
- name: F1
type: f1
value: 0.5751836259585372
---
<!-- 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-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_
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: 4.1676
- Accuracy: 0.5768
- F1: 0.5752
## 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: 1123
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 1.0487 | 1.09 | 500 | 0.9666 | 0.5305 | 0.5194 |
| 0.9092 | 2.17 | 1000 | 0.9220 | 0.5760 | 0.5733 |
| 0.76 | 3.26 | 1500 | 1.0464 | 0.5791 | 0.5681 |
| 0.6233 | 4.35 | 2000 | 1.1732 | 0.5864 | 0.5809 |
| 0.4852 | 5.43 | 2500 | 1.1695 | 0.5590 | 0.5578 |
| 0.374 | 6.52 | 3000 | 1.3903 | 0.5691 | 0.5669 |
| 0.2851 | 7.61 | 3500 | 1.5832 | 0.5760 | 0.5711 |
| 0.2203 | 8.7 | 4000 | 1.6098 | 0.5737 | 0.5739 |
| 0.1863 | 9.78 | 4500 | 1.9189 | 0.5656 | 0.5566 |
| 0.1437 | 10.87 | 5000 | 2.1445 | 0.5826 | 0.5783 |
| 0.1302 | 11.96 | 5500 | 1.9960 | 0.5791 | 0.5723 |
| 0.1075 | 13.04 | 6000 | 2.5978 | 0.5710 | 0.5663 |
| 0.0957 | 14.13 | 6500 | 2.9129 | 0.5675 | 0.5682 |
| 0.0898 | 15.22 | 7000 | 2.8487 | 0.5799 | 0.5780 |
| 0.082 | 16.3 | 7500 | 2.8461 | 0.5714 | 0.5621 |
| 0.0673 | 17.39 | 8000 | 2.8416 | 0.5849 | 0.5767 |
| 0.0647 | 18.48 | 8500 | 3.1083 | 0.5849 | 0.5810 |
| 0.0597 | 19.57 | 9000 | 2.9063 | 0.5772 | 0.5700 |
| 0.0508 | 20.65 | 9500 | 3.1069 | 0.5706 | 0.5663 |
| 0.0492 | 21.74 | 10000 | 3.1434 | 0.5841 | 0.5853 |
| 0.0485 | 22.83 | 10500 | 2.9341 | 0.5887 | 0.5816 |
| 0.0373 | 23.91 | 11000 | 3.2828 | 0.5810 | 0.5807 |
| 0.0352 | 25.0 | 11500 | 3.1742 | 0.5864 | 0.5802 |
| 0.0326 | 26.09 | 12000 | 3.2767 | 0.5733 | 0.5734 |
| 0.0269 | 27.17 | 12500 | 3.5101 | 0.5826 | 0.5797 |
| 0.0338 | 28.26 | 13000 | 3.2453 | 0.5725 | 0.5693 |
| 0.0289 | 29.35 | 13500 | 3.3957 | 0.5694 | 0.5703 |
| 0.0232 | 30.43 | 14000 | 3.4668 | 0.5710 | 0.5714 |
| 0.0215 | 31.52 | 14500 | 3.5250 | 0.5721 | 0.5660 |
| 0.0197 | 32.61 | 15000 | 3.5990 | 0.5787 | 0.5755 |
| 0.0138 | 33.7 | 15500 | 3.7731 | 0.5745 | 0.5682 |
| 0.0177 | 34.78 | 16000 | 3.6367 | 0.5698 | 0.5671 |
| 0.0145 | 35.87 | 16500 | 3.8987 | 0.5725 | 0.5705 |
| 0.013 | 36.96 | 17000 | 3.8459 | 0.5745 | 0.5737 |
| 0.0133 | 38.04 | 17500 | 3.7106 | 0.5733 | 0.5711 |
| 0.0095 | 39.13 | 18000 | 3.8834 | 0.5683 | 0.5688 |
| 0.0091 | 40.22 | 18500 | 3.9118 | 0.5733 | 0.5731 |
| 0.0107 | 41.3 | 19000 | 3.9038 | 0.5768 | 0.5733 |
| 0.0089 | 42.39 | 19500 | 3.8957 | 0.5826 | 0.5784 |
| 0.0042 | 43.48 | 20000 | 4.1050 | 0.5775 | 0.5761 |
| 0.0067 | 44.57 | 20500 | 4.0982 | 0.5756 | 0.5739 |
| 0.0042 | 45.65 | 21000 | 4.2051 | 0.5737 | 0.5733 |
| 0.0057 | 46.74 | 21500 | 4.1266 | 0.5764 | 0.5764 |
| 0.0056 | 47.83 | 22000 | 4.1318 | 0.5787 | 0.5765 |
| 0.0034 | 48.91 | 22500 | 4.1443 | 0.5791 | 0.5772 |
| 0.003 | 50.0 | 23000 | 4.1676 | 0.5768 | 0.5752 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
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