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--- |
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base_model: microsoft/mdeberta-v3-base |
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datasets: |
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- tweet_sentiment_multilingual |
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library_name: transformers |
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license: mit |
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metrics: |
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- accuracy |
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- f1 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: scenario-NON-KD-SCR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual |
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results: |
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- task: |
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type: text-classification |
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name: 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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- type: accuracy |
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value: 0.4903549382716049 |
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name: Accuracy |
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- type: f1 |
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value: 0.490123758683559 |
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name: F1 |
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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-SCR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-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: 6.8615 |
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- Accuracy: 0.4904 |
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- F1: 0.4901 |
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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: 66 |
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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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| 1.0475 | 1.0870 | 500 | 1.0371 | 0.4985 | 0.4949 | |
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| 0.7462 | 2.1739 | 1000 | 1.2759 | 0.5123 | 0.5122 | |
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| 0.421 | 3.2609 | 1500 | 1.6791 | 0.5139 | 0.5126 | |
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| 0.2321 | 4.3478 | 2000 | 2.1227 | 0.4946 | 0.4940 | |
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| 0.1534 | 5.4348 | 2500 | 2.4070 | 0.4958 | 0.4966 | |
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| 0.0987 | 6.5217 | 3000 | 2.8761 | 0.4904 | 0.4900 | |
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| 0.0734 | 7.6087 | 3500 | 2.8613 | 0.4911 | 0.4881 | |
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| 0.0697 | 8.6957 | 4000 | 3.5593 | 0.4969 | 0.4932 | |
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| 0.0586 | 9.7826 | 4500 | 3.4005 | 0.4900 | 0.4883 | |
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| 0.0462 | 10.8696 | 5000 | 3.6698 | 0.4861 | 0.4866 | |
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| 0.0321 | 11.9565 | 5500 | 4.1118 | 0.4877 | 0.4883 | |
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| 0.0267 | 13.0435 | 6000 | 4.1028 | 0.4965 | 0.4959 | |
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| 0.0257 | 14.1304 | 6500 | 4.3167 | 0.4842 | 0.4815 | |
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| 0.0185 | 15.2174 | 7000 | 4.3273 | 0.4923 | 0.4876 | |
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| 0.0178 | 16.3043 | 7500 | 4.7543 | 0.4958 | 0.4959 | |
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| 0.0149 | 17.3913 | 8000 | 4.3035 | 0.4927 | 0.4929 | |
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| 0.0125 | 18.4783 | 8500 | 4.5842 | 0.4904 | 0.4884 | |
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| 0.0116 | 19.5652 | 9000 | 5.3172 | 0.4853 | 0.4833 | |
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| 0.0114 | 20.6522 | 9500 | 4.8280 | 0.4857 | 0.4825 | |
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| 0.0036 | 21.7391 | 10000 | 5.6275 | 0.4850 | 0.4820 | |
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| 0.0094 | 22.8261 | 10500 | 5.1559 | 0.4842 | 0.4815 | |
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| 0.0054 | 23.9130 | 11000 | 5.3889 | 0.4846 | 0.4826 | |
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| 0.0085 | 25.0 | 11500 | 4.8587 | 0.4888 | 0.4861 | |
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| 0.0068 | 26.0870 | 12000 | 5.3553 | 0.4896 | 0.4881 | |
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| 0.0054 | 27.1739 | 12500 | 5.3446 | 0.4853 | 0.4845 | |
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| 0.0042 | 28.2609 | 13000 | 5.3437 | 0.4838 | 0.4832 | |
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| 0.003 | 29.3478 | 13500 | 5.9054 | 0.4796 | 0.4784 | |
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| 0.0032 | 30.4348 | 14000 | 5.7871 | 0.4884 | 0.4881 | |
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| 0.0038 | 31.5217 | 14500 | 5.9122 | 0.4803 | 0.4787 | |
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| 0.0041 | 32.6087 | 15000 | 5.4601 | 0.4834 | 0.4786 | |
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| 0.0025 | 33.6957 | 15500 | 5.1979 | 0.4884 | 0.4853 | |
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| 0.0018 | 34.7826 | 16000 | 5.5286 | 0.4896 | 0.4869 | |
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| 0.0006 | 35.8696 | 16500 | 5.7718 | 0.4877 | 0.4859 | |
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| 0.0015 | 36.9565 | 17000 | 6.0193 | 0.4834 | 0.4832 | |
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| 0.0003 | 38.0435 | 17500 | 6.2210 | 0.4838 | 0.4828 | |
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| 0.0004 | 39.1304 | 18000 | 6.3234 | 0.4880 | 0.4879 | |
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| 0.0002 | 40.2174 | 18500 | 6.3829 | 0.4888 | 0.4885 | |
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| 0.0001 | 41.3043 | 19000 | 6.5514 | 0.4892 | 0.4889 | |
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| 0.0001 | 42.3913 | 19500 | 6.6261 | 0.4892 | 0.4891 | |
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| 0.0003 | 43.4783 | 20000 | 6.6971 | 0.4861 | 0.4849 | |
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| 0.0013 | 44.5652 | 20500 | 6.7077 | 0.4865 | 0.4849 | |
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| 0.0001 | 45.6522 | 21000 | 6.7350 | 0.4911 | 0.4903 | |
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| 0.0001 | 46.7391 | 21500 | 6.7889 | 0.4896 | 0.4888 | |
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| 0.0002 | 47.8261 | 22000 | 6.8318 | 0.4900 | 0.4902 | |
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| 0.0006 | 48.9130 | 22500 | 6.8526 | 0.4904 | 0.4901 | |
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| 0.0001 | 50.0 | 23000 | 6.8615 | 0.4904 | 0.4901 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.19.1 |
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