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--- |
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license: mit |
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base_model: xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- tweet_sentiment_multilingual |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: scenario-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_ |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: tweet_sentiment_multilingual |
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type: tweet_sentiment_multilingual |
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config: all |
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split: validation |
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args: all |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.5767746913580247 |
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- name: F1 |
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type: f1 |
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value: 0.5751836259585372 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# scenario-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_ |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tweet_sentiment_multilingual dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.1676 |
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- Accuracy: 0.5768 |
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- F1: 0.5752 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 1123 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| |
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| 1.0487 | 1.09 | 500 | 0.9666 | 0.5305 | 0.5194 | |
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| 0.9092 | 2.17 | 1000 | 0.9220 | 0.5760 | 0.5733 | |
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| 0.76 | 3.26 | 1500 | 1.0464 | 0.5791 | 0.5681 | |
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| 0.6233 | 4.35 | 2000 | 1.1732 | 0.5864 | 0.5809 | |
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| 0.4852 | 5.43 | 2500 | 1.1695 | 0.5590 | 0.5578 | |
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| 0.374 | 6.52 | 3000 | 1.3903 | 0.5691 | 0.5669 | |
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| 0.2851 | 7.61 | 3500 | 1.5832 | 0.5760 | 0.5711 | |
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| 0.2203 | 8.7 | 4000 | 1.6098 | 0.5737 | 0.5739 | |
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| 0.1863 | 9.78 | 4500 | 1.9189 | 0.5656 | 0.5566 | |
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| 0.1437 | 10.87 | 5000 | 2.1445 | 0.5826 | 0.5783 | |
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| 0.1302 | 11.96 | 5500 | 1.9960 | 0.5791 | 0.5723 | |
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| 0.1075 | 13.04 | 6000 | 2.5978 | 0.5710 | 0.5663 | |
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| 0.0957 | 14.13 | 6500 | 2.9129 | 0.5675 | 0.5682 | |
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| 0.0898 | 15.22 | 7000 | 2.8487 | 0.5799 | 0.5780 | |
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| 0.082 | 16.3 | 7500 | 2.8461 | 0.5714 | 0.5621 | |
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| 0.0673 | 17.39 | 8000 | 2.8416 | 0.5849 | 0.5767 | |
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| 0.0647 | 18.48 | 8500 | 3.1083 | 0.5849 | 0.5810 | |
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| 0.0597 | 19.57 | 9000 | 2.9063 | 0.5772 | 0.5700 | |
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| 0.0508 | 20.65 | 9500 | 3.1069 | 0.5706 | 0.5663 | |
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| 0.0492 | 21.74 | 10000 | 3.1434 | 0.5841 | 0.5853 | |
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| 0.0485 | 22.83 | 10500 | 2.9341 | 0.5887 | 0.5816 | |
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| 0.0373 | 23.91 | 11000 | 3.2828 | 0.5810 | 0.5807 | |
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| 0.0352 | 25.0 | 11500 | 3.1742 | 0.5864 | 0.5802 | |
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| 0.0326 | 26.09 | 12000 | 3.2767 | 0.5733 | 0.5734 | |
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| 0.0269 | 27.17 | 12500 | 3.5101 | 0.5826 | 0.5797 | |
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| 0.0338 | 28.26 | 13000 | 3.2453 | 0.5725 | 0.5693 | |
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| 0.0289 | 29.35 | 13500 | 3.3957 | 0.5694 | 0.5703 | |
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| 0.0232 | 30.43 | 14000 | 3.4668 | 0.5710 | 0.5714 | |
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| 0.0215 | 31.52 | 14500 | 3.5250 | 0.5721 | 0.5660 | |
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| 0.0197 | 32.61 | 15000 | 3.5990 | 0.5787 | 0.5755 | |
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| 0.0138 | 33.7 | 15500 | 3.7731 | 0.5745 | 0.5682 | |
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| 0.0177 | 34.78 | 16000 | 3.6367 | 0.5698 | 0.5671 | |
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| 0.0145 | 35.87 | 16500 | 3.8987 | 0.5725 | 0.5705 | |
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| 0.013 | 36.96 | 17000 | 3.8459 | 0.5745 | 0.5737 | |
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| 0.0133 | 38.04 | 17500 | 3.7106 | 0.5733 | 0.5711 | |
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| 0.0095 | 39.13 | 18000 | 3.8834 | 0.5683 | 0.5688 | |
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| 0.0091 | 40.22 | 18500 | 3.9118 | 0.5733 | 0.5731 | |
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| 0.0107 | 41.3 | 19000 | 3.9038 | 0.5768 | 0.5733 | |
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| 0.0089 | 42.39 | 19500 | 3.8957 | 0.5826 | 0.5784 | |
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| 0.0042 | 43.48 | 20000 | 4.1050 | 0.5775 | 0.5761 | |
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| 0.0067 | 44.57 | 20500 | 4.0982 | 0.5756 | 0.5739 | |
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| 0.0042 | 45.65 | 21000 | 4.2051 | 0.5737 | 0.5733 | |
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| 0.0057 | 46.74 | 21500 | 4.1266 | 0.5764 | 0.5764 | |
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| 0.0056 | 47.83 | 22000 | 4.1318 | 0.5787 | 0.5765 | |
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| 0.0034 | 48.91 | 22500 | 4.1443 | 0.5791 | 0.5772 | |
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| 0.003 | 50.0 | 23000 | 4.1676 | 0.5768 | 0.5752 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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