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--- |
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license: mit |
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base_model: haryoaw/scenario-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all |
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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-KD-PR-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all55 |
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results: [] |
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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-KD-PR-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all55 |
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This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all](https://huggingface.co/haryoaw/scenario-MDBT-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all) on the tweet_sentiment_multilingual dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2180 |
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- Accuracy: 0.5999 |
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- F1: 0.6008 |
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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: 55 |
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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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| 1.2423 | 1.09 | 500 | 1.2214 | 0.4842 | 0.4591 | |
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| 1.1465 | 2.17 | 1000 | 1.2081 | 0.5498 | 0.5406 | |
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| 1.089 | 3.26 | 1500 | 1.2345 | 0.5540 | 0.5476 | |
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| 1.043 | 4.35 | 2000 | 1.2340 | 0.5756 | 0.5777 | |
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| 1.01 | 5.43 | 2500 | 1.2397 | 0.5706 | 0.5717 | |
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| 0.9787 | 6.52 | 3000 | 1.2536 | 0.5718 | 0.5723 | |
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| 0.9656 | 7.61 | 3500 | 1.2564 | 0.5579 | 0.5603 | |
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| 0.9505 | 8.7 | 4000 | 1.2641 | 0.5644 | 0.5660 | |
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| 0.9432 | 9.78 | 4500 | 1.2385 | 0.5880 | 0.5876 | |
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| 0.9304 | 10.87 | 5000 | 1.2612 | 0.5864 | 0.5862 | |
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| 0.9245 | 11.96 | 5500 | 1.2567 | 0.5748 | 0.5728 | |
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| 0.9189 | 13.04 | 6000 | 1.2463 | 0.5745 | 0.5745 | |
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| 0.9131 | 14.13 | 6500 | 1.2599 | 0.5729 | 0.5738 | |
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| 0.9098 | 15.22 | 7000 | 1.2614 | 0.5706 | 0.5704 | |
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| 0.9052 | 16.3 | 7500 | 1.2468 | 0.5741 | 0.5748 | |
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| 0.9013 | 17.39 | 8000 | 1.2550 | 0.5756 | 0.5775 | |
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| 0.8972 | 18.48 | 8500 | 1.2661 | 0.5733 | 0.5743 | |
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| 0.8972 | 19.57 | 9000 | 1.2506 | 0.5783 | 0.5780 | |
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| 0.8912 | 20.65 | 9500 | 1.2519 | 0.5737 | 0.5752 | |
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| 0.8903 | 21.74 | 10000 | 1.2313 | 0.5795 | 0.5782 | |
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| 0.8868 | 22.83 | 10500 | 1.2384 | 0.5895 | 0.5896 | |
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| 0.8847 | 23.91 | 11000 | 1.2474 | 0.5752 | 0.5736 | |
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| 0.8834 | 25.0 | 11500 | 1.2458 | 0.5791 | 0.5795 | |
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| 0.8815 | 26.09 | 12000 | 1.2548 | 0.5748 | 0.5739 | |
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| 0.8794 | 27.17 | 12500 | 1.2378 | 0.5864 | 0.5857 | |
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| 0.8791 | 28.26 | 13000 | 1.2327 | 0.5968 | 0.5953 | |
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| 0.8749 | 29.35 | 13500 | 1.2249 | 0.5949 | 0.5935 | |
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| 0.8748 | 30.43 | 14000 | 1.2309 | 0.5938 | 0.5905 | |
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| 0.8734 | 31.52 | 14500 | 1.2242 | 0.5880 | 0.5885 | |
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| 0.872 | 32.61 | 15000 | 1.2372 | 0.5841 | 0.5856 | |
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| 0.8712 | 33.7 | 15500 | 1.2394 | 0.5783 | 0.5800 | |
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| 0.87 | 34.78 | 16000 | 1.2363 | 0.5922 | 0.5921 | |
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| 0.8692 | 35.87 | 16500 | 1.2375 | 0.5903 | 0.5916 | |
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| 0.8677 | 36.96 | 17000 | 1.2341 | 0.5968 | 0.5951 | |
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| 0.8672 | 38.04 | 17500 | 1.2227 | 0.6038 | 0.6013 | |
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| 0.8657 | 39.13 | 18000 | 1.2250 | 0.5899 | 0.5904 | |
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| 0.865 | 40.22 | 18500 | 1.2275 | 0.5949 | 0.5952 | |
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| 0.865 | 41.3 | 19000 | 1.2196 | 0.5953 | 0.5958 | |
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| 0.864 | 42.39 | 19500 | 1.2375 | 0.5818 | 0.5815 | |
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| 0.8636 | 43.48 | 20000 | 1.2373 | 0.5849 | 0.5856 | |
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| 0.8635 | 44.57 | 20500 | 1.2292 | 0.5930 | 0.5940 | |
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| 0.8622 | 45.65 | 21000 | 1.2243 | 0.5903 | 0.5914 | |
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| 0.8619 | 46.74 | 21500 | 1.2198 | 0.5984 | 0.5992 | |
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| 0.8608 | 47.83 | 22000 | 1.2175 | 0.6046 | 0.6054 | |
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| 0.8621 | 48.91 | 22500 | 1.2179 | 0.5995 | 0.6004 | |
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| 0.8606 | 50.0 | 23000 | 1.2180 | 0.5999 | 0.6008 | |
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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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