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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-NON-KD-PO-COPY-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-NON-KD-PO-COPY-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: 2.1172 |
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- Accuracy: 0.8547 |
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- F1: 0.8307 |
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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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| 0.5096 | 0.2672 | 5000 | 0.6672 | 0.8301 | 0.7901 | |
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| 0.4124 | 0.5344 | 10000 | 0.6564 | 0.8398 | 0.8058 | |
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| 0.3501 | 0.8017 | 15000 | 0.6697 | 0.8466 | 0.8172 | |
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| 0.232 | 1.0689 | 20000 | 0.7193 | 0.8480 | 0.8171 | |
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| 0.2262 | 1.3361 | 25000 | 0.7387 | 0.8489 | 0.8243 | |
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| 0.2146 | 1.6033 | 30000 | 0.7377 | 0.8480 | 0.8243 | |
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| 0.2007 | 1.8706 | 35000 | 0.7711 | 0.8463 | 0.8186 | |
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| 0.1472 | 2.1378 | 40000 | 0.8809 | 0.8492 | 0.8218 | |
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| 0.1509 | 2.4050 | 45000 | 0.8559 | 0.8495 | 0.8242 | |
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| 0.1513 | 2.6722 | 50000 | 0.8919 | 0.8481 | 0.8226 | |
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| 0.1428 | 2.9394 | 55000 | 0.8636 | 0.8503 | 0.8272 | |
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| 0.1154 | 3.2067 | 60000 | 0.9868 | 0.8512 | 0.8312 | |
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| 0.1148 | 3.4739 | 65000 | 0.9541 | 0.8502 | 0.8253 | |
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| 0.1152 | 3.7411 | 70000 | 0.9729 | 0.8503 | 0.8305 | |
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| 0.1031 | 4.0083 | 75000 | 1.0581 | 0.8459 | 0.8260 | |
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| 0.0942 | 4.2756 | 80000 | 1.0732 | 0.8487 | 0.8248 | |
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| 0.0961 | 4.5428 | 85000 | 1.0528 | 0.8509 | 0.8284 | |
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| 0.099 | 4.8100 | 90000 | 1.0421 | 0.8486 | 0.8250 | |
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| 0.0761 | 5.0772 | 95000 | 1.1302 | 0.8480 | 0.8221 | |
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| 0.0822 | 5.3444 | 100000 | 1.1138 | 0.8492 | 0.8261 | |
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| 0.0833 | 5.6117 | 105000 | 1.1368 | 0.8478 | 0.8243 | |
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| 0.0768 | 5.8789 | 110000 | 1.1240 | 0.8503 | 0.8256 | |
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| 0.0593 | 6.1461 | 115000 | 1.2497 | 0.8491 | 0.8277 | |
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| 0.0687 | 6.4133 | 120000 | 1.2261 | 0.8484 | 0.8252 | |
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| 0.0738 | 6.6806 | 125000 | 1.1406 | 0.8482 | 0.8257 | |
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| 0.0651 | 6.9478 | 130000 | 1.2154 | 0.8488 | 0.8232 | |
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| 0.0604 | 7.2150 | 135000 | 1.3001 | 0.8459 | 0.8245 | |
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| 0.0646 | 7.4822 | 140000 | 1.2426 | 0.8488 | 0.8223 | |
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| 0.0653 | 7.7495 | 145000 | 1.2843 | 0.8499 | 0.8283 | |
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| 0.0433 | 8.0167 | 150000 | 1.3361 | 0.8500 | 0.8261 | |
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| 0.0497 | 8.2839 | 155000 | 1.2612 | 0.8492 | 0.8241 | |
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| 0.0517 | 8.5511 | 160000 | 1.3544 | 0.8474 | 0.8270 | |
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| 0.0624 | 8.8183 | 165000 | 1.2964 | 0.8489 | 0.8275 | |
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| 0.0381 | 9.0856 | 170000 | 1.3753 | 0.8486 | 0.8242 | |
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| 0.0405 | 9.3528 | 175000 | 1.4366 | 0.8484 | 0.8219 | |
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| 0.0488 | 9.6200 | 180000 | 1.4429 | 0.8458 | 0.8235 | |
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| 0.0495 | 9.8872 | 185000 | 1.3962 | 0.8496 | 0.8273 | |
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| 0.0363 | 10.1545 | 190000 | 1.5269 | 0.8483 | 0.8247 | |
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| 0.0423 | 10.4217 | 195000 | 1.4415 | 0.8489 | 0.8236 | |
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| 0.0448 | 10.6889 | 200000 | 1.3974 | 0.8482 | 0.8218 | |
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| 0.0373 | 10.9561 | 205000 | 1.4432 | 0.8465 | 0.8245 | |
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| 0.0294 | 11.2233 | 210000 | 1.5381 | 0.8493 | 0.8234 | |
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| 0.0336 | 11.4906 | 215000 | 1.5496 | 0.8490 | 0.8218 | |
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| 0.041 | 11.7578 | 220000 | 1.4867 | 0.8468 | 0.8240 | |
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| 0.028 | 12.0250 | 225000 | 1.5215 | 0.8498 | 0.8270 | |
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| 0.0287 | 12.2922 | 230000 | 1.5916 | 0.8495 | 0.8256 | |
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| 0.0314 | 12.5595 | 235000 | 1.6239 | 0.8500 | 0.8291 | |
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| 0.0347 | 12.8267 | 240000 | 1.5390 | 0.8464 | 0.8243 | |
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| 0.0247 | 13.0939 | 245000 | 1.5492 | 0.8502 | 0.8271 | |
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| 0.0281 | 13.3611 | 250000 | 1.5729 | 0.8465 | 0.8262 | |
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| 0.025 | 13.6283 | 255000 | 1.6933 | 0.8470 | 0.8249 | |
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| 0.0325 | 13.8956 | 260000 | 1.5823 | 0.8511 | 0.8289 | |
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| 0.0268 | 14.1628 | 265000 | 1.6099 | 0.8476 | 0.8213 | |
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| 0.0339 | 14.4300 | 270000 | 1.6234 | 0.8514 | 0.8306 | |
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| 0.0285 | 14.6972 | 275000 | 1.5725 | 0.8524 | 0.8288 | |
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| 0.0309 | 14.9645 | 280000 | 1.6213 | 0.8498 | 0.8251 | |
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| 0.0274 | 15.2317 | 285000 | 1.6650 | 0.8481 | 0.8235 | |
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| 0.02 | 15.4989 | 290000 | 1.7295 | 0.8488 | 0.8261 | |
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| 0.0299 | 15.7661 | 295000 | 1.6868 | 0.8513 | 0.8277 | |
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| 0.0196 | 16.0333 | 300000 | 1.7188 | 0.8492 | 0.8234 | |
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| 0.0221 | 16.3006 | 305000 | 1.7492 | 0.8511 | 0.8265 | |
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| 0.0273 | 16.5678 | 310000 | 1.6775 | 0.8505 | 0.8267 | |
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| 0.0212 | 16.8350 | 315000 | 1.7104 | 0.8492 | 0.8253 | |
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| 0.0188 | 17.1022 | 320000 | 1.6627 | 0.8502 | 0.8271 | |
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| 0.0225 | 17.3695 | 325000 | 1.7774 | 0.8504 | 0.8256 | |
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| 0.0204 | 17.6367 | 330000 | 1.7537 | 0.8489 | 0.8240 | |
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| 0.0261 | 17.9039 | 335000 | 1.6829 | 0.8516 | 0.8273 | |
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| 0.0153 | 18.1711 | 340000 | 1.8292 | 0.8532 | 0.8295 | |
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| 0.0226 | 18.4384 | 345000 | 1.7632 | 0.8511 | 0.8273 | |
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| 0.0213 | 18.7056 | 350000 | 1.7662 | 0.8515 | 0.8252 | |
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| 0.0162 | 18.9728 | 355000 | 1.7639 | 0.8519 | 0.8280 | |
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| 0.0155 | 19.2400 | 360000 | 1.8033 | 0.8521 | 0.8262 | |
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| 0.0151 | 19.5072 | 365000 | 1.8838 | 0.8505 | 0.8241 | |
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| 0.0151 | 19.7745 | 370000 | 1.8777 | 0.8532 | 0.8268 | |
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| 0.0138 | 20.0417 | 375000 | 1.8770 | 0.8522 | 0.8284 | |
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| 0.0152 | 20.3089 | 380000 | 1.8785 | 0.8525 | 0.8275 | |
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| 0.0136 | 20.5761 | 385000 | 1.8761 | 0.8530 | 0.8310 | |
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| 0.0181 | 20.8434 | 390000 | 1.8843 | 0.8526 | 0.8288 | |
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| 0.0113 | 21.1106 | 395000 | 1.8946 | 0.8540 | 0.8306 | |
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| 0.0144 | 21.3778 | 400000 | 1.8532 | 0.8530 | 0.8318 | |
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| 0.0149 | 21.6450 | 405000 | 1.9300 | 0.8519 | 0.8296 | |
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| 0.0165 | 21.9122 | 410000 | 1.9072 | 0.8532 | 0.8316 | |
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| 0.0128 | 22.1795 | 415000 | 1.9919 | 0.8516 | 0.8270 | |
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| 0.0071 | 22.4467 | 420000 | 1.9807 | 0.8543 | 0.8313 | |
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| 0.0099 | 22.7139 | 425000 | 1.9709 | 0.8527 | 0.8277 | |
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| 0.011 | 22.9811 | 430000 | 1.9782 | 0.8521 | 0.8279 | |
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| 0.0102 | 23.2484 | 435000 | 2.0483 | 0.8515 | 0.8268 | |
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| 0.013 | 23.5156 | 440000 | 2.0266 | 0.8527 | 0.8290 | |
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| 0.0145 | 23.7828 | 445000 | 1.9633 | 0.8533 | 0.8303 | |
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| 0.0091 | 24.0500 | 450000 | 2.0645 | 0.8514 | 0.8274 | |
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| 0.0128 | 24.3172 | 455000 | 2.0243 | 0.8541 | 0.8314 | |
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| 0.0101 | 24.5845 | 460000 | 2.0680 | 0.8518 | 0.8281 | |
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| 0.0087 | 24.8517 | 465000 | 2.0453 | 0.8526 | 0.8290 | |
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| 0.0087 | 25.1189 | 470000 | 2.0808 | 0.8529 | 0.8294 | |
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| 0.0079 | 25.3861 | 475000 | 2.1050 | 0.8522 | 0.8285 | |
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| 0.0101 | 25.6534 | 480000 | 2.0773 | 0.8518 | 0.8275 | |
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| 0.0094 | 25.9206 | 485000 | 2.0602 | 0.8533 | 0.8305 | |
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| 0.0097 | 26.1878 | 490000 | 2.0994 | 0.8519 | 0.8267 | |
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| 0.01 | 26.4550 | 495000 | 2.0608 | 0.8549 | 0.8309 | |
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| 0.0107 | 26.7222 | 500000 | 2.0748 | 0.8539 | 0.8300 | |
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| 0.0088 | 26.9895 | 505000 | 2.1072 | 0.8539 | 0.8305 | |
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| 0.0057 | 27.2567 | 510000 | 2.1086 | 0.8548 | 0.8318 | |
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| 0.0096 | 27.5239 | 515000 | 2.1032 | 0.8539 | 0.8297 | |
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| 0.0082 | 27.7911 | 520000 | 2.1119 | 0.8538 | 0.8294 | |
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| 0.0081 | 28.0584 | 525000 | 2.1045 | 0.8543 | 0.8296 | |
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| 0.0079 | 28.3256 | 530000 | 2.1268 | 0.8539 | 0.8296 | |
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| 0.0089 | 28.5928 | 535000 | 2.1128 | 0.8547 | 0.8303 | |
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| 0.0079 | 28.8600 | 540000 | 2.1003 | 0.8540 | 0.8294 | |
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| 0.0056 | 29.1273 | 545000 | 2.1130 | 0.8541 | 0.8294 | |
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| 0.0082 | 29.3945 | 550000 | 2.1167 | 0.8544 | 0.8302 | |
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| 0.0056 | 29.6617 | 555000 | 2.1182 | 0.8546 | 0.8306 | |
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| 0.0076 | 29.9289 | 560000 | 2.1172 | 0.8547 | 0.8307 | |
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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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