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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-SCR-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: 0.47762345679012347 |
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- name: F1 |
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type: f1 |
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value: 0.47819062529207484 |
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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 [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: 6.0055 |
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- Accuracy: 0.4776 |
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- F1: 0.4782 |
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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: 11423 |
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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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| 1.1157 | 1.09 | 500 | 1.0964 | 0.3835 | 0.2965 | |
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| 0.9636 | 2.17 | 1000 | 1.1184 | 0.4954 | 0.4470 | |
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| 0.5977 | 3.26 | 1500 | 1.4984 | 0.5116 | 0.5070 | |
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| 0.342 | 4.35 | 2000 | 1.8178 | 0.5077 | 0.5054 | |
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| 0.1946 | 5.43 | 2500 | 2.5918 | 0.5077 | 0.5062 | |
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| 0.1442 | 6.52 | 3000 | 2.5451 | 0.4904 | 0.4833 | |
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| 0.101 | 7.61 | 3500 | 3.3273 | 0.4942 | 0.4879 | |
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| 0.0788 | 8.7 | 4000 | 3.3097 | 0.4811 | 0.4729 | |
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| 0.0596 | 9.78 | 4500 | 3.4639 | 0.4954 | 0.4959 | |
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| 0.0505 | 10.87 | 5000 | 3.5381 | 0.4884 | 0.4884 | |
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| 0.0413 | 11.96 | 5500 | 3.3937 | 0.4958 | 0.4961 | |
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| 0.0364 | 13.04 | 6000 | 3.9058 | 0.4850 | 0.4848 | |
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| 0.0273 | 14.13 | 6500 | 4.3025 | 0.4892 | 0.4887 | |
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| 0.0282 | 15.22 | 7000 | 3.9833 | 0.4877 | 0.4885 | |
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| 0.0253 | 16.3 | 7500 | 4.4515 | 0.4811 | 0.4802 | |
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| 0.0188 | 17.39 | 8000 | 4.7345 | 0.4873 | 0.4843 | |
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| 0.0191 | 18.48 | 8500 | 4.5842 | 0.4880 | 0.4880 | |
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| 0.0187 | 19.57 | 9000 | 4.6871 | 0.4838 | 0.4821 | |
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| 0.0189 | 20.65 | 9500 | 4.7307 | 0.4931 | 0.4857 | |
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| 0.0157 | 21.74 | 10000 | 4.8938 | 0.4796 | 0.4722 | |
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| 0.0133 | 22.83 | 10500 | 4.6099 | 0.4765 | 0.4681 | |
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| 0.0107 | 23.91 | 11000 | 5.0670 | 0.4815 | 0.4787 | |
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| 0.0076 | 25.0 | 11500 | 4.9710 | 0.4799 | 0.4780 | |
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| 0.0078 | 26.09 | 12000 | 5.0339 | 0.4830 | 0.4841 | |
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| 0.0101 | 27.17 | 12500 | 5.0560 | 0.4904 | 0.4907 | |
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| 0.0086 | 28.26 | 13000 | 5.0095 | 0.4850 | 0.4843 | |
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| 0.0074 | 29.35 | 13500 | 5.1031 | 0.4846 | 0.4831 | |
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| 0.0032 | 30.43 | 14000 | 5.4537 | 0.4830 | 0.4840 | |
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| 0.0054 | 31.52 | 14500 | 5.4554 | 0.4838 | 0.4847 | |
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| 0.0046 | 32.61 | 15000 | 5.5972 | 0.4780 | 0.4774 | |
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| 0.0059 | 33.7 | 15500 | 5.3884 | 0.4853 | 0.4863 | |
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| 0.0029 | 34.78 | 16000 | 5.3174 | 0.4738 | 0.4736 | |
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| 0.0033 | 35.87 | 16500 | 5.5911 | 0.4753 | 0.4742 | |
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| 0.0041 | 36.96 | 17000 | 5.2149 | 0.4769 | 0.4747 | |
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| 0.0034 | 38.04 | 17500 | 5.5052 | 0.4857 | 0.4853 | |
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| 0.0014 | 39.13 | 18000 | 5.5164 | 0.4807 | 0.4812 | |
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| 0.0015 | 40.22 | 18500 | 5.6182 | 0.4803 | 0.4791 | |
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| 0.0002 | 41.3 | 19000 | 5.7053 | 0.4799 | 0.4780 | |
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| 0.0001 | 42.39 | 19500 | 5.7820 | 0.4826 | 0.4808 | |
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| 0.0001 | 43.48 | 20000 | 5.8324 | 0.4850 | 0.4844 | |
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| 0.0005 | 44.57 | 20500 | 5.9002 | 0.4823 | 0.4798 | |
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| 0.0004 | 45.65 | 21000 | 5.9340 | 0.4811 | 0.4810 | |
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| 0.0011 | 46.74 | 21500 | 5.9656 | 0.4780 | 0.4785 | |
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| 0.0002 | 47.83 | 22000 | 5.9859 | 0.4792 | 0.4798 | |
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| 0.0001 | 48.91 | 22500 | 5.9994 | 0.4788 | 0.4793 | |
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| 0.0001 | 50.0 | 23000 | 6.0055 | 0.4776 | 0.4782 | |
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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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