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scenario-KD-PO-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all_delta

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PyTorch
xlm-roberta
Generated from Trainer
Inference Endpoints
Model card Files Community
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  1. README.md +109 -0
  2. config.json +39 -0
  3. pytorch_model.bin +3 -0
  4. training_args.bin +3 -0
README.md ADDED
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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_delta
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+ results: []
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+ ---
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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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+
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+ # scenario-KD-PO-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all_delta
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+
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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: 3.3760
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+ - Accuracy: 0.5586
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+ - F1: 0.5590
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 7777
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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+ | 4.88 | 1.09 | 500 | 4.1661 | 0.4518 | 0.4462 |
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+ | 3.9513 | 2.17 | 1000 | 3.7172 | 0.5154 | 0.5102 |
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+ | 3.4556 | 3.26 | 1500 | 3.5612 | 0.5293 | 0.5221 |
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+ | 3.0959 | 4.35 | 2000 | 3.5271 | 0.5440 | 0.5349 |
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+ | 2.7593 | 5.43 | 2500 | 3.3105 | 0.5602 | 0.5595 |
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+ | 2.548 | 6.52 | 3000 | 3.4842 | 0.5355 | 0.5360 |
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+ | 2.3323 | 7.61 | 3500 | 3.2883 | 0.5575 | 0.5444 |
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+ | 2.1215 | 8.7 | 4000 | 3.2282 | 0.5583 | 0.5607 |
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+ | 1.9719 | 9.78 | 4500 | 3.5734 | 0.5594 | 0.5534 |
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+ | 1.8238 | 10.87 | 5000 | 3.2866 | 0.5552 | 0.5538 |
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+ | 1.703 | 11.96 | 5500 | 3.2345 | 0.5556 | 0.5557 |
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+ | 1.5931 | 13.04 | 6000 | 3.2219 | 0.5583 | 0.5539 |
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+ | 1.4959 | 14.13 | 6500 | 3.3073 | 0.5637 | 0.5644 |
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+ | 1.4198 | 15.22 | 7000 | 3.5221 | 0.5579 | 0.5541 |
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+ | 1.35 | 16.3 | 7500 | 3.5125 | 0.5660 | 0.5643 |
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+ | 1.2937 | 17.39 | 8000 | 3.5089 | 0.5640 | 0.5637 |
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+ | 1.2282 | 18.48 | 8500 | 3.4262 | 0.5664 | 0.5658 |
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+ | 1.1698 | 19.57 | 9000 | 3.3739 | 0.5598 | 0.5593 |
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+ | 1.1402 | 20.65 | 9500 | 3.4930 | 0.5521 | 0.5541 |
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+ | 1.0874 | 21.74 | 10000 | 3.4935 | 0.5625 | 0.5602 |
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+ | 1.0652 | 22.83 | 10500 | 3.3963 | 0.5482 | 0.5478 |
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+ | 1.0191 | 23.91 | 11000 | 3.4823 | 0.5571 | 0.5583 |
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+ | 0.9868 | 25.0 | 11500 | 3.6035 | 0.5579 | 0.5586 |
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+ | 0.9487 | 26.09 | 12000 | 3.6034 | 0.5525 | 0.5488 |
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+ | 0.936 | 27.17 | 12500 | 3.5428 | 0.5556 | 0.5542 |
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+ | 0.9116 | 28.26 | 13000 | 3.6023 | 0.5532 | 0.5509 |
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+ | 0.8868 | 29.35 | 13500 | 3.5292 | 0.5579 | 0.5581 |
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+ | 0.8733 | 30.43 | 14000 | 3.4206 | 0.5594 | 0.5589 |
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+ | 0.8538 | 31.52 | 14500 | 3.4417 | 0.5594 | 0.5592 |
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+ | 0.8289 | 32.61 | 15000 | 3.4970 | 0.5579 | 0.5584 |
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+ | 0.823 | 33.7 | 15500 | 3.4860 | 0.5625 | 0.5617 |
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+ | 0.7992 | 34.78 | 16000 | 3.5193 | 0.5671 | 0.5659 |
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+ | 0.7974 | 35.87 | 16500 | 3.3709 | 0.5490 | 0.5497 |
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+ | 0.7775 | 36.96 | 17000 | 3.3854 | 0.5706 | 0.5720 |
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+ | 0.7691 | 38.04 | 17500 | 3.3827 | 0.5698 | 0.5700 |
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+ | 0.758 | 39.13 | 18000 | 3.4608 | 0.5818 | 0.5818 |
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+ | 0.757 | 40.22 | 18500 | 3.3860 | 0.5683 | 0.5681 |
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+ | 0.7481 | 41.3 | 19000 | 3.3757 | 0.5687 | 0.5686 |
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+ | 0.7387 | 42.39 | 19500 | 3.4830 | 0.5714 | 0.5707 |
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+ | 0.7276 | 43.48 | 20000 | 3.3942 | 0.5617 | 0.5611 |
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+ | 0.7279 | 44.57 | 20500 | 3.3357 | 0.5725 | 0.5726 |
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+ | 0.7127 | 45.65 | 21000 | 3.3856 | 0.5521 | 0.5523 |
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+ | 0.7148 | 46.74 | 21500 | 3.4401 | 0.5660 | 0.5673 |
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+ | 0.7219 | 47.83 | 22000 | 3.4684 | 0.5629 | 0.5627 |
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+ | 0.7005 | 48.91 | 22500 | 3.3840 | 0.5625 | 0.5631 |
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+ | 0.7114 | 50.0 | 23000 | 3.3760 | 0.5586 | 0.5590 |
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+
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+
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+ ### Framework versions
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+
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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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+ "problem_type": "single_label_classification",
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+ }
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