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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: mistral-7b-drug-prots_pair |
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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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# mistral-7b-drug-prots_pair |
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This model was trained from scratch on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0454 |
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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: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 30 |
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- training_steps: 5000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.1497 | 0.01 | 50 | 0.0787 | |
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| 0.0734 | 0.02 | 100 | 0.0740 | |
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| 0.0694 | 0.03 | 150 | 0.0688 | |
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| 0.0709 | 0.04 | 200 | 0.0679 | |
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| 0.0668 | 0.05 | 250 | 0.0662 | |
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| 0.0651 | 0.06 | 300 | 0.0643 | |
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| 0.0619 | 0.07 | 350 | 0.0627 | |
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| 0.0614 | 0.08 | 400 | 0.0627 | |
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| 0.0607 | 0.09 | 450 | 0.0612 | |
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| 0.0609 | 0.1 | 500 | 0.0601 | |
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| 0.0586 | 0.11 | 550 | 0.0596 | |
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| 0.0578 | 0.12 | 600 | 0.0590 | |
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| 0.0577 | 0.13 | 650 | 0.0584 | |
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| 0.0569 | 0.14 | 700 | 0.0580 | |
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| 0.0564 | 0.15 | 750 | 0.0575 | |
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| 0.0566 | 0.16 | 800 | 0.0569 | |
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| 0.0558 | 0.17 | 850 | 0.0562 | |
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| 0.0556 | 0.18 | 900 | 0.0556 | |
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| 0.0552 | 0.19 | 950 | 0.0551 | |
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| 0.0546 | 0.2 | 1000 | 0.0547 | |
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| 0.0544 | 0.21 | 1050 | 0.0543 | |
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| 0.0541 | 0.22 | 1100 | 0.0545 | |
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| 0.0532 | 0.23 | 1150 | 0.0536 | |
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| 0.0527 | 0.24 | 1200 | 0.0534 | |
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| 0.0526 | 0.25 | 1250 | 0.0525 | |
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| 0.0524 | 0.26 | 1300 | 0.0523 | |
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| 0.0518 | 0.27 | 1350 | 0.0517 | |
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| 0.0514 | 0.28 | 1400 | 0.0518 | |
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| 0.0507 | 0.29 | 1450 | 0.0516 | |
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| 0.0501 | 0.3 | 1500 | 0.0512 | |
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| 0.0493 | 0.31 | 1550 | 0.0508 | |
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| 0.0495 | 0.32 | 1600 | 0.0507 | |
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| 0.0484 | 0.33 | 1650 | 0.0506 | |
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| 0.0482 | 0.34 | 1700 | 0.0504 | |
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| 0.0477 | 0.35 | 1750 | 0.0501 | |
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| 0.0474 | 0.36 | 1800 | 0.0503 | |
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| 0.0467 | 0.37 | 1850 | 0.0501 | |
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| 0.0469 | 0.38 | 1900 | 0.0494 | |
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| 0.0462 | 0.39 | 1950 | 0.0492 | |
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| 0.0455 | 0.4 | 2000 | 0.0490 | |
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| 0.0456 | 0.41 | 2050 | 0.0486 | |
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| 0.0454 | 0.42 | 2100 | 0.0488 | |
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| 0.0452 | 0.43 | 2150 | 0.0487 | |
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| 0.0456 | 0.44 | 2200 | 0.0485 | |
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| 0.0444 | 0.45 | 2250 | 0.0484 | |
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| 0.0441 | 0.46 | 2300 | 0.0477 | |
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| 0.0439 | 0.47 | 2350 | 0.0476 | |
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| 0.0441 | 0.48 | 2400 | 0.0471 | |
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| 0.0437 | 0.49 | 2450 | 0.0475 | |
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| 0.0432 | 0.5 | 2500 | 0.0472 | |
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| 0.0428 | 0.51 | 2550 | 0.0471 | |
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| 0.0431 | 0.52 | 2600 | 0.0472 | |
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| 0.0425 | 0.53 | 2650 | 0.0467 | |
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| 0.0429 | 0.54 | 2700 | 0.0464 | |
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| 0.0421 | 0.55 | 2750 | 0.0464 | |
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| 0.0422 | 0.56 | 2800 | 0.0460 | |
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| 0.0417 | 0.57 | 2850 | 0.0464 | |
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| 0.0419 | 0.58 | 2900 | 0.0462 | |
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| 0.0407 | 0.59 | 2950 | 0.0461 | |
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| 0.0409 | 0.6 | 3000 | 0.0462 | |
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| 0.0408 | 0.61 | 3050 | 0.0459 | |
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| 0.04 | 0.62 | 3100 | 0.0458 | |
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| 0.0401 | 0.63 | 3150 | 0.0453 | |
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| 0.0398 | 0.64 | 3200 | 0.0454 | |
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| 0.0395 | 0.65 | 3250 | 0.0451 | |
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| 0.0395 | 0.66 | 3300 | 0.0452 | |
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| 0.0387 | 0.67 | 3350 | 0.0453 | |
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| 0.0384 | 0.68 | 3400 | 0.0454 | |
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| 0.0386 | 0.69 | 3450 | 0.0451 | |
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| 0.0385 | 0.7 | 3500 | 0.0451 | |
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| 0.0383 | 0.71 | 3550 | 0.0450 | |
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| 0.0385 | 0.72 | 3600 | 0.0448 | |
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| 0.0378 | 0.73 | 3650 | 0.0449 | |
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| 0.0378 | 0.74 | 3700 | 0.0448 | |
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| 0.0376 | 0.75 | 3750 | 0.0453 | |
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| 0.0376 | 0.76 | 3800 | 0.0453 | |
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| 0.0374 | 0.77 | 3850 | 0.0454 | |
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| 0.0376 | 0.78 | 3900 | 0.0454 | |
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| 0.0369 | 0.79 | 3950 | 0.0454 | |
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| 0.037 | 0.8 | 4000 | 0.0448 | |
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| 0.0367 | 0.81 | 4050 | 0.0451 | |
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| 0.0369 | 0.82 | 4100 | 0.0450 | |
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| 0.0366 | 0.83 | 4150 | 0.0449 | |
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| 0.0368 | 0.84 | 4200 | 0.0449 | |
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| 0.0361 | 0.85 | 4250 | 0.0453 | |
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| 0.037 | 0.86 | 4300 | 0.0454 | |
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| 0.0364 | 0.87 | 4350 | 0.0453 | |
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| 0.0356 | 0.88 | 4400 | 0.0453 | |
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| 0.0359 | 0.89 | 4450 | 0.0453 | |
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| 0.0357 | 0.9 | 4500 | 0.0455 | |
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| 0.0357 | 0.91 | 4550 | 0.0454 | |
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| 0.0351 | 0.92 | 4600 | 0.0464 | |
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| 0.0356 | 0.93 | 4650 | 0.0458 | |
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| 0.0353 | 0.94 | 4700 | 0.0460 | |
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| 0.0351 | 0.95 | 4750 | 0.0456 | |
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| 0.0348 | 0.96 | 4800 | 0.0456 | |
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| 0.0355 | 0.97 | 4850 | 0.0455 | |
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| 0.0354 | 0.98 | 4900 | 0.0454 | |
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| 0.0357 | 0.99 | 4950 | 0.0453 | |
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| 0.0356 | 1.0 | 5000 | 0.0454 | |
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### Framework versions |
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- Transformers 4.44.0.dev0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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