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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- ratishsp/newshead |
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model-index: |
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- name: Centrum |
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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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# Centrum |
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Centrum is a pretrained model for multi-document summarization, trained with centroid-based pretraining objective on the NewSHead dataset. It is initialized from allenai/led-large-16384. The details of the approach are mentioned in the ACL 2023 Multi-Document Summarization with Centroid-Based Pretraining (Ratish Puduppully, Parag Jain, Nancy F. Chen and Mark Steedman). It achieves the following results on the evaluation set: |
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- Loss: 3.3292 |
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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: 3e-05 |
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- train_batch_size: 1 |
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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: 16 |
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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: linear |
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- lr_scheduler_warmup_steps: 10000 |
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- training_steps: 100000 |
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- mixed_precision_training: Native AMP |
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- label_smoothing_factor: 0.1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:------:|:---------------:| |
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| 3.7884 | 0.05 | 500 | 3.7054 | |
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| 3.6593 | 0.09 | 1000 | 3.6245 | |
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| 3.6425 | 0.14 | 1500 | 3.5841 | |
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| 3.6008 | 0.19 | 2000 | 3.5561 | |
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| 3.5645 | 0.23 | 2500 | 3.5372 | |
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| 3.568 | 0.28 | 3000 | 3.5187 | |
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| 3.5408 | 0.32 | 3500 | 3.5045 | |
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| 3.5447 | 0.37 | 4000 | 3.4951 | |
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| 3.5324 | 0.42 | 4500 | 3.4845 | |
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| 3.5192 | 0.46 | 5000 | 3.4739 | |
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| 3.4841 | 0.51 | 5500 | 3.4684 | |
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| 3.4703 | 0.56 | 6000 | 3.4604 | |
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| 3.4759 | 0.6 | 6500 | 3.4534 | |
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| 3.4647 | 0.65 | 7000 | 3.4476 | |
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| 3.4726 | 0.7 | 7500 | 3.4399 | |
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| 3.4522 | 0.74 | 8000 | 3.4332 | |
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| 3.4454 | 0.79 | 8500 | 3.4277 | |
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| 3.4281 | 0.83 | 9000 | 3.4229 | |
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| 3.4341 | 0.88 | 9500 | 3.4173 | |
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| 3.4563 | 0.93 | 10000 | 3.4161 | |
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| 3.4188 | 0.97 | 10500 | 3.4094 | |
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| 3.3967 | 1.02 | 11000 | 3.4123 | |
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| 3.3647 | 1.07 | 11500 | 3.4061 | |
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| 3.3604 | 1.11 | 12000 | 3.4011 | |
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| 3.3662 | 1.16 | 12500 | 3.4011 | |
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| 3.3698 | 1.21 | 13000 | 3.3918 | |
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| 3.3558 | 1.25 | 13500 | 3.3910 | |
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| 3.3421 | 1.3 | 14000 | 3.3891 | |
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| 3.3468 | 1.34 | 14500 | 3.3894 | |
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| 3.3333 | 1.39 | 15000 | 3.3817 | |
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| 3.3545 | 1.44 | 15500 | 3.3803 | |
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| 3.3411 | 1.48 | 16000 | 3.3784 | |
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| 3.3338 | 1.53 | 16500 | 3.3782 | |
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| 3.3354 | 1.58 | 17000 | 3.3749 | |
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| 3.3341 | 1.62 | 17500 | 3.3714 | |
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| 3.3302 | 1.67 | 18000 | 3.3677 | |
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| 3.3179 | 1.71 | 18500 | 3.3659 | |
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| 3.3381 | 1.76 | 19000 | 3.3645 | |
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| 3.3223 | 1.81 | 19500 | 3.3619 | |
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| 3.3079 | 1.85 | 20000 | 3.3593 | |
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| 3.3156 | 1.9 | 20500 | 3.3576 | |
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| 3.3056 | 1.95 | 21000 | 3.3582 | |
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| 3.3117 | 1.99 | 21500 | 3.3552 | |
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| 3.2522 | 2.04 | 22000 | 3.3550 | |
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| 3.2522 | 2.09 | 22500 | 3.3586 | |
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| 3.2386 | 2.13 | 23000 | 3.3548 | |
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| 3.2574 | 2.18 | 23500 | 3.3544 | |
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| 3.239 | 2.22 | 24000 | 3.3566 | |
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| 3.2468 | 2.27 | 24500 | 3.3528 | |
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| 3.2264 | 2.32 | 25000 | 3.3511 | |
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| 3.2501 | 2.36 | 25500 | 3.3482 | |
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| 3.2204 | 2.41 | 26000 | 3.3506 | |
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| 3.2302 | 2.46 | 26500 | 3.3526 | |
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| 3.2353 | 2.5 | 27000 | 3.3492 | |
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| 3.2494 | 2.55 | 27500 | 3.3452 | |
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| 3.2423 | 2.6 | 28000 | 3.3455 | |
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| 3.2233 | 2.64 | 28500 | 3.3447 | |
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| 3.2498 | 2.69 | 29000 | 3.3420 | |
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| 3.2175 | 2.73 | 29500 | 3.3457 | |
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| 3.2398 | 2.78 | 30000 | 3.3402 | |
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| 3.2242 | 2.83 | 30500 | 3.3421 | |
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| 3.2185 | 2.87 | 31000 | 3.3457 | |
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| 3.2274 | 2.92 | 31500 | 3.3419 | |
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| 3.2251 | 2.97 | 32000 | 3.3449 | |
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| 3.1507 | 3.01 | 32500 | 3.3518 | |
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| 3.165 | 3.06 | 33000 | 3.3462 | |
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| 3.1512 | 3.11 | 33500 | 3.3434 | |
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| 3.1598 | 3.15 | 34000 | 3.3433 | |
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| 3.1728 | 3.2 | 34500 | 3.3445 | |
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| 3.1838 | 3.24 | 35000 | 3.3456 | |
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| 3.1649 | 3.29 | 35500 | 3.3442 | |
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| 3.1684 | 3.34 | 36000 | 3.3404 | |
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| 3.1587 | 3.38 | 36500 | 3.3406 | |
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| 3.1586 | 3.43 | 37000 | 3.3442 | |
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| 3.1545 | 3.48 | 37500 | 3.3381 | |
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| 3.1674 | 3.52 | 38000 | 3.3436 | |
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| 3.1717 | 3.57 | 38500 | 3.3373 | |
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| 3.147 | 3.62 | 39000 | 3.3408 | |
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| 3.1462 | 3.66 | 39500 | 3.3374 | |
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| 3.156 | 3.71 | 40000 | 3.3382 | |
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| 3.1354 | 3.75 | 40500 | 3.3366 | |
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| 3.1613 | 3.8 | 41000 | 3.3317 | |
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| 3.143 | 3.85 | 41500 | 3.3347 | |
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| 3.1667 | 3.89 | 42000 | 3.3353 | |
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| 3.1597 | 3.94 | 42500 | 3.3341 | |
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| 3.1566 | 3.99 | 43000 | 3.3357 | |
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| 3.124 | 4.03 | 43500 | 3.3410 | |
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| 3.1035 | 4.08 | 44000 | 3.3434 | |
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| 3.0881 | 4.12 | 44500 | 3.3411 | |
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| 3.1131 | 4.17 | 45000 | 3.3379 | |
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| 3.1191 | 4.22 | 45500 | 3.3468 | |
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| 3.1119 | 4.26 | 46000 | 3.3356 | |
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| 3.0957 | 4.31 | 46500 | 3.3417 | |
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| 3.1024 | 4.36 | 47000 | 3.3380 | |
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| 3.1141 | 4.4 | 47500 | 3.3472 | |
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| 3.0851 | 4.45 | 48000 | 3.3513 | |
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| 3.1252 | 4.5 | 48500 | 3.3351 | |
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| 3.1125 | 4.54 | 49000 | 3.3423 | |
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| 3.1019 | 4.59 | 49500 | 3.3396 | |
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| 3.1185 | 4.63 | 50000 | 3.3349 | |
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| 3.1042 | 4.68 | 50500 | 3.3350 | |
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| 3.1153 | 4.73 | 51000 | 3.3345 | |
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| 3.1289 | 4.77 | 51500 | 3.3356 | |
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| 3.1075 | 4.82 | 52000 | 3.3335 | |
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| 3.1151 | 4.87 | 52500 | 3.3385 | |
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| 3.094 | 4.91 | 53000 | 3.3292 | |
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| 3.1272 | 4.96 | 53500 | 3.3349 | |
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| 3.0847 | 5.01 | 54000 | 3.3407 | |
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| 3.0662 | 5.05 | 54500 | 3.3378 | |
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| 3.0345 | 5.1 | 55000 | 3.3481 | |
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| 3.0611 | 5.14 | 55500 | 3.3410 | |
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| 3.0566 | 5.19 | 56000 | 3.3424 | |
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| 3.0413 | 5.24 | 56500 | 3.3466 | |
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| 3.0291 | 5.28 | 57000 | 3.3453 | |
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| 3.0569 | 5.33 | 57500 | 3.3491 | |
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| 3.0645 | 5.38 | 58000 | 3.3378 | |
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| 3.0646 | 5.42 | 58500 | 3.3434 | |
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| 3.045 | 5.47 | 59000 | 3.3418 | |
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| 3.0551 | 5.52 | 59500 | 3.3426 | |
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| 3.0706 | 5.56 | 60000 | 3.3378 | |
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| 3.0556 | 5.61 | 60500 | 3.3407 | |
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| 3.0743 | 5.65 | 61000 | 3.3520 | |
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| 3.0764 | 5.7 | 61500 | 3.3320 | |
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| 3.0723 | 5.75 | 62000 | 3.3352 | |
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| 3.0716 | 5.79 | 62500 | 3.3327 | |
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| 3.0618 | 5.84 | 63000 | 3.3447 | |
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| 3.0662 | 5.89 | 63500 | 3.3312 | |
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| 3.0758 | 5.93 | 64000 | 3.3323 | |
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| 3.0501 | 5.98 | 64500 | 3.3400 | |
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| 2.978 | 6.03 | 65000 | 3.3473 | |
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| 3.0131 | 6.07 | 65500 | 3.3440 | |
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| 3.0212 | 6.12 | 66000 | 3.3401 | |
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| 3.0095 | 6.16 | 66500 | 3.3361 | |
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| 3.0118 | 6.21 | 67000 | 3.3352 | |
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| 3.0249 | 6.26 | 67500 | 3.3398 | |
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| 3.0107 | 6.3 | 68000 | 3.3444 | |
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| 3.0175 | 6.35 | 68500 | 3.3490 | |
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| 3.0241 | 6.4 | 69000 | 3.3402 | |
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| 3.0094 | 6.44 | 69500 | 3.3437 | |
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| 3.0286 | 6.49 | 70000 | 3.3355 | |
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| 3.0391 | 6.54 | 70500 | 3.3385 | |
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| 3.0243 | 6.58 | 71000 | 3.3395 | |
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| 3.0232 | 6.63 | 71500 | 3.3370 | |
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| 3.0168 | 6.67 | 72000 | 3.3458 | |
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| 3.0432 | 6.72 | 72500 | 3.3400 | |
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| 3.0121 | 6.77 | 73000 | 3.3420 | |
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| 3.0137 | 6.81 | 73500 | 3.3436 | |
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| 3.0333 | 6.86 | 74000 | 3.3362 | |
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| 3.0194 | 6.91 | 74500 | 3.3355 | |
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| 3.0198 | 6.95 | 75000 | 3.3434 | |
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| 3.0105 | 7.0 | 75500 | 3.3346 | |
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| 2.9833 | 7.04 | 76000 | 3.3492 | |
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| 2.9876 | 7.09 | 76500 | 3.3351 | |
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| 2.9918 | 7.14 | 77000 | 3.3466 | |
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| 2.9983 | 7.18 | 77500 | 3.3422 | |
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| 2.9893 | 7.23 | 78000 | 3.3364 | |
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| 2.9946 | 7.28 | 78500 | 3.3365 | |
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| 2.9851 | 7.32 | 79000 | 3.3402 | |
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| 2.9797 | 7.37 | 79500 | 3.3450 | |
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| 2.9888 | 7.42 | 80000 | 3.3423 | |
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| 3.0182 | 7.46 | 80500 | 3.3429 | |
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| 2.983 | 7.51 | 81000 | 3.3345 | |
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| 2.9959 | 7.55 | 81500 | 3.3397 | |
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| 2.9935 | 7.6 | 82000 | 3.3389 | |
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| 3.0008 | 7.65 | 82500 | 3.3442 | |
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| 2.9898 | 7.69 | 83000 | 3.3418 | |
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| 2.9989 | 7.74 | 83500 | 3.3387 | |
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| 2.985 | 7.79 | 84000 | 3.3482 | |
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| 2.963 | 7.83 | 84500 | 3.3369 | |
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| 3.0009 | 7.88 | 85000 | 3.3355 | |
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| 2.9925 | 7.93 | 85500 | 3.3434 | |
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| 2.9616 | 7.97 | 86000 | 3.3346 | |
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| 2.9769 | 8.02 | 86500 | 3.3430 | |
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| 2.9663 | 8.06 | 87000 | 3.3407 | |
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| 2.9872 | 8.11 | 87500 | 3.3448 | |
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| 2.9892 | 8.16 | 88000 | 3.3354 | |
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| 2.9526 | 8.2 | 88500 | 3.3445 | |
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| 2.9426 | 8.25 | 89000 | 3.3405 | |
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| 2.9528 | 8.3 | 89500 | 3.3466 | |
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| 2.9541 | 8.34 | 90000 | 3.3434 | |
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| 2.9643 | 8.39 | 90500 | 3.3475 | |
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| 2.9893 | 8.44 | 91000 | 3.3434 | |
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| 2.9655 | 8.48 | 91500 | 3.3433 | |
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| 2.9735 | 8.53 | 92000 | 3.3416 | |
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| 2.9722 | 8.57 | 92500 | 3.3443 | |
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| 2.9639 | 8.62 | 93000 | 3.3410 | |
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| 2.972 | 8.67 | 93500 | 3.3407 | |
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| 2.9586 | 8.71 | 94000 | 3.3393 | |
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| 2.9591 | 8.76 | 94500 | 3.3412 | |
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| 2.9523 | 8.81 | 95000 | 3.3411 | |
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| 2.9572 | 8.85 | 95500 | 3.3393 | |
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| 2.9435 | 8.9 | 96000 | 3.3414 | |
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| 2.9667 | 8.95 | 96500 | 3.3392 | |
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| 2.9824 | 8.99 | 97000 | 3.3428 | |
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| 2.9265 | 9.04 | 97500 | 3.3417 | |
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| 2.9409 | 9.08 | 98000 | 3.3435 | |
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| 2.9387 | 9.13 | 98500 | 3.3425 | |
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| 2.9635 | 9.18 | 99000 | 3.3420 | |
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| 2.9527 | 9.22 | 99500 | 3.3421 | |
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| 2.9755 | 9.27 | 100000 | 3.3430 | |
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### Framework versions |
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- Transformers 4.23.0.dev0 |
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- Pytorch 1.12.1 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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