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--- |
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base_model: haryoaw/scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1 |
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datasets: |
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- massive |
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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-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_155 |
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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-SCR-MSV-D2_data-AmazonScience_massive_all_1_155 |
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This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1](https://huggingface.co/haryoaw/scenario-MDBT-TCR_data-AmazonScience_massive_all_1_1) on the massive dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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- Accuracy: 0.0315 |
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- F1: 0.0010 |
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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: 30 |
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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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| 0.0 | 0.2672 | 5000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 0.5344 | 10000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 0.8017 | 15000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 1.0689 | 20000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 1.3361 | 25000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 1.6033 | 30000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 1.8706 | 35000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 2.1378 | 40000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 2.4050 | 45000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 2.6722 | 50000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 2.9394 | 55000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 3.2067 | 60000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 3.4739 | 65000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 3.7411 | 70000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 4.0083 | 75000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 4.2756 | 80000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 4.5428 | 85000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 4.8100 | 90000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 5.0772 | 95000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 5.3444 | 100000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 5.6117 | 105000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 5.8789 | 110000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 6.1461 | 115000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 6.4133 | 120000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 6.6806 | 125000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 6.9478 | 130000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 7.2150 | 135000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 7.4822 | 140000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 7.7495 | 145000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 8.0167 | 150000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 8.2839 | 155000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 8.5511 | 160000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 8.8183 | 165000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 9.0856 | 170000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 9.3528 | 175000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 9.6200 | 180000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 9.8872 | 185000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 10.1545 | 190000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 10.4217 | 195000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 10.6889 | 200000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 10.9561 | 205000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 11.2233 | 210000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 11.4906 | 215000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 11.7578 | 220000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 12.0250 | 225000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 12.2922 | 230000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 12.5595 | 235000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 12.8267 | 240000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 13.0939 | 245000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 13.3611 | 250000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 13.6283 | 255000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 13.8956 | 260000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 14.1628 | 265000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 14.4300 | 270000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 14.6972 | 275000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 14.9645 | 280000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 15.2317 | 285000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 15.4989 | 290000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 15.7661 | 295000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 16.0333 | 300000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 16.3006 | 305000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 16.5678 | 310000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 16.8350 | 315000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 17.1022 | 320000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 17.3695 | 325000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 17.6367 | 330000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 17.9039 | 335000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 18.1711 | 340000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 18.4384 | 345000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 18.7056 | 350000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 18.9728 | 355000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 19.2400 | 360000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 19.5072 | 365000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 19.7745 | 370000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 20.0417 | 375000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 20.3089 | 380000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 20.5761 | 385000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 20.8434 | 390000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 21.1106 | 395000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 21.3778 | 400000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 21.6450 | 405000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 21.9122 | 410000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 22.1795 | 415000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 22.4467 | 420000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 22.7139 | 425000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 22.9811 | 430000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 23.2484 | 435000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 23.5156 | 440000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 23.7828 | 445000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 24.0500 | 450000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 24.3172 | 455000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 24.5845 | 460000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 24.8517 | 465000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 25.1189 | 470000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 25.3861 | 475000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 25.6534 | 480000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 25.9206 | 485000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 26.1878 | 490000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 26.4550 | 495000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 26.7222 | 500000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 26.9895 | 505000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 27.2567 | 510000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 27.5239 | 515000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 27.7911 | 520000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 28.0584 | 525000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 28.3256 | 530000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 28.5928 | 535000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 28.8600 | 540000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 29.1273 | 545000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 29.3945 | 550000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 29.6617 | 555000 | nan | 0.0315 | 0.0010 | |
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| 0.0 | 29.9289 | 560000 | nan | 0.0315 | 0.0010 | |
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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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