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--- |
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license: mit |
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base_model: haryoaw/scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_a |
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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-PO-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all_alpha |
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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-PO-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all_alpha |
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This model is a fine-tuned version of [haryoaw/scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_a](https://huggingface.co/haryoaw/scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_a) on the tweet_sentiment_multilingual dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4750 |
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- Accuracy: 0.5637 |
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- F1: 0.5640 |
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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: 2222 |
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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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| 4.905 | 1.09 | 500 | 4.5461 | 0.4363 | 0.4113 | |
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| 4.0059 | 2.17 | 1000 | 3.6093 | 0.5058 | 0.5040 | |
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| 3.4109 | 3.26 | 1500 | 3.4190 | 0.5208 | 0.5131 | |
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| 3.0676 | 4.35 | 2000 | 3.2675 | 0.5490 | 0.5477 | |
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| 2.7673 | 5.43 | 2500 | 3.2746 | 0.5467 | 0.5412 | |
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| 2.5421 | 6.52 | 3000 | 3.1951 | 0.5475 | 0.5367 | |
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| 2.3609 | 7.61 | 3500 | 3.3137 | 0.5432 | 0.5410 | |
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| 2.1176 | 8.7 | 4000 | 3.5963 | 0.5451 | 0.5303 | |
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| 1.9583 | 9.78 | 4500 | 3.5109 | 0.5571 | 0.5583 | |
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| 1.8268 | 10.87 | 5000 | 3.3664 | 0.5471 | 0.5477 | |
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| 1.7388 | 11.96 | 5500 | 3.3858 | 0.5517 | 0.5528 | |
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| 1.5976 | 13.04 | 6000 | 3.4404 | 0.5617 | 0.5577 | |
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| 1.4912 | 14.13 | 6500 | 3.3307 | 0.5586 | 0.5585 | |
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| 1.4157 | 15.22 | 7000 | 3.5579 | 0.5432 | 0.5355 | |
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| 1.3536 | 16.3 | 7500 | 3.3542 | 0.5617 | 0.5603 | |
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| 1.2883 | 17.39 | 8000 | 3.6026 | 0.5571 | 0.5543 | |
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| 1.2443 | 18.48 | 8500 | 3.6866 | 0.5478 | 0.5458 | |
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| 1.1637 | 19.57 | 9000 | 3.6125 | 0.5536 | 0.5547 | |
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| 1.1391 | 20.65 | 9500 | 3.5456 | 0.5613 | 0.5574 | |
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| 1.1029 | 21.74 | 10000 | 3.4366 | 0.5513 | 0.5526 | |
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| 1.0417 | 22.83 | 10500 | 3.6791 | 0.5586 | 0.5585 | |
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| 1.0169 | 23.91 | 11000 | 3.6637 | 0.5656 | 0.5607 | |
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| 1.0107 | 25.0 | 11500 | 3.5452 | 0.5575 | 0.5578 | |
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| 0.9502 | 26.09 | 12000 | 3.4362 | 0.5748 | 0.5742 | |
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| 0.9455 | 27.17 | 12500 | 3.4865 | 0.5694 | 0.5703 | |
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| 0.9194 | 28.26 | 13000 | 3.4523 | 0.5737 | 0.5716 | |
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| 0.9053 | 29.35 | 13500 | 3.5411 | 0.5586 | 0.5572 | |
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| 0.8737 | 30.43 | 14000 | 3.6550 | 0.5586 | 0.5586 | |
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| 0.865 | 31.52 | 14500 | 3.5079 | 0.5594 | 0.5611 | |
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| 0.8444 | 32.61 | 15000 | 3.4885 | 0.5509 | 0.5526 | |
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| 0.8343 | 33.7 | 15500 | 3.5705 | 0.5710 | 0.5698 | |
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| 0.8122 | 34.78 | 16000 | 3.4910 | 0.5521 | 0.5519 | |
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| 0.8161 | 35.87 | 16500 | 3.5302 | 0.5559 | 0.5563 | |
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| 0.7923 | 36.96 | 17000 | 3.5031 | 0.5656 | 0.5632 | |
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| 0.7824 | 38.04 | 17500 | 3.4182 | 0.5594 | 0.5592 | |
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| 0.7658 | 39.13 | 18000 | 3.5265 | 0.5594 | 0.5586 | |
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| 0.7588 | 40.22 | 18500 | 3.4465 | 0.5706 | 0.5711 | |
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| 0.7541 | 41.3 | 19000 | 3.4879 | 0.5540 | 0.5534 | |
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| 0.7488 | 42.39 | 19500 | 3.4246 | 0.5687 | 0.5693 | |
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| 0.7412 | 43.48 | 20000 | 3.4806 | 0.5745 | 0.5750 | |
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| 0.7314 | 44.57 | 20500 | 3.5638 | 0.5590 | 0.5586 | |
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| 0.7283 | 45.65 | 21000 | 3.4212 | 0.5664 | 0.5667 | |
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| 0.7179 | 46.74 | 21500 | 3.4444 | 0.5556 | 0.5560 | |
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| 0.7168 | 47.83 | 22000 | 3.4104 | 0.5602 | 0.5606 | |
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| 0.7161 | 48.91 | 22500 | 3.3766 | 0.5667 | 0.5676 | |
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| 0.7052 | 50.0 | 23000 | 3.4750 | 0.5637 | 0.5640 | |
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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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