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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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- 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-base-16384](https://huggingface.co/allenai/led-base-16384). The details of the approach are mentioned in the preprint [Multi-Document Summarization with Centroid-Based Pretraining](https://arxiv.org/abs/2208.01006) (Ratish Puduppully and Mark Steedman). |
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It achieves the following results on the evaluation set: |
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- Loss: 3.5568 |
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## Model description |
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The script for training and inference of Centrum is available on https://github.com/ratishsp/centrum |
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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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| 4.1628 | 0.05 | 500 | 4.0732 | |
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| 4.0278 | 0.09 | 1000 | 3.9800 | |
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| 4.0008 | 0.14 | 1500 | 3.9283 | |
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| 3.9564 | 0.19 | 2000 | 3.8941 | |
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| 3.9193 | 0.23 | 2500 | 3.8780 | |
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| 3.9185 | 0.28 | 3000 | 3.8501 | |
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| 3.8881 | 0.32 | 3500 | 3.8334 | |
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| 3.8869 | 0.37 | 4000 | 3.8211 | |
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| 3.876 | 0.42 | 4500 | 3.8057 | |
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| 3.8552 | 0.46 | 5000 | 3.7954 | |
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| 3.8198 | 0.51 | 5500 | 3.7861 | |
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| 3.8016 | 0.56 | 6000 | 3.7750 | |
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| 3.8033 | 0.6 | 6500 | 3.7651 | |
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| 3.7927 | 0.65 | 7000 | 3.7528 | |
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| 3.7978 | 0.7 | 7500 | 3.7429 | |
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| 3.7727 | 0.74 | 8000 | 3.7367 | |
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| 3.7634 | 0.79 | 8500 | 3.7275 | |
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| 3.7395 | 0.83 | 9000 | 3.7158 | |
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| 3.7432 | 0.88 | 9500 | 3.7066 | |
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| 3.7623 | 0.93 | 10000 | 3.7039 | |
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| 3.7182 | 0.97 | 10500 | 3.6904 | |
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| 3.7146 | 1.02 | 11000 | 3.6881 | |
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| 3.681 | 1.07 | 11500 | 3.6797 | |
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| 3.6745 | 1.11 | 12000 | 3.6750 | |
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| 3.6794 | 1.16 | 12500 | 3.6748 | |
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| 3.6802 | 1.21 | 13000 | 3.6696 | |
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| 3.665 | 1.25 | 13500 | 3.6609 | |
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| 3.6516 | 1.3 | 14000 | 3.6633 | |
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| 3.6577 | 1.34 | 14500 | 3.6573 | |
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| 3.6409 | 1.39 | 15000 | 3.6519 | |
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| 3.6691 | 1.44 | 15500 | 3.6490 | |
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| 3.6521 | 1.48 | 16000 | 3.6475 | |
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| 3.6435 | 1.53 | 16500 | 3.6465 | |
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| 3.6466 | 1.58 | 17000 | 3.6392 | |
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| 3.644 | 1.62 | 17500 | 3.6419 | |
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| 3.6347 | 1.67 | 18000 | 3.6347 | |
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| 3.6205 | 1.71 | 18500 | 3.6328 | |
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| 3.6451 | 1.76 | 19000 | 3.6310 | |
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| 3.6327 | 1.81 | 19500 | 3.6284 | |
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| 3.6166 | 1.85 | 20000 | 3.6267 | |
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| 3.622 | 1.9 | 20500 | 3.6212 | |
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| 3.6164 | 1.95 | 21000 | 3.6199 | |
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| 3.6178 | 1.99 | 21500 | 3.6201 | |
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| 3.5892 | 2.04 | 22000 | 3.6201 | |
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| 3.5855 | 2.09 | 22500 | 3.6221 | |
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| 3.5658 | 2.13 | 23000 | 3.6193 | |
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| 3.5916 | 2.18 | 23500 | 3.6144 | |
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| 3.5767 | 2.22 | 24000 | 3.6101 | |
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| 3.5809 | 2.27 | 24500 | 3.6115 | |
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| 3.5561 | 2.32 | 25000 | 3.6110 | |
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| 3.5831 | 2.36 | 25500 | 3.6080 | |
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| 3.5551 | 2.41 | 26000 | 3.6121 | |
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| 3.5588 | 2.46 | 26500 | 3.6072 | |
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| 3.5645 | 2.5 | 27000 | 3.6056 | |
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| 3.5804 | 2.55 | 27500 | 3.6038 | |
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| 3.5712 | 2.6 | 28000 | 3.6052 | |
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| 3.5494 | 2.64 | 28500 | 3.6014 | |
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| 3.582 | 2.69 | 29000 | 3.5995 | |
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| 3.5487 | 2.73 | 29500 | 3.6051 | |
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| 3.5709 | 2.78 | 30000 | 3.5954 | |
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| 3.5546 | 2.83 | 30500 | 3.5941 | |
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| 3.5525 | 2.87 | 31000 | 3.5952 | |
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| 3.5603 | 2.92 | 31500 | 3.5972 | |
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| 3.5572 | 2.97 | 32000 | 3.5947 | |
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| 3.5106 | 3.01 | 32500 | 3.5952 | |
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| 3.5142 | 3.06 | 33000 | 3.5937 | |
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| 3.506 | 3.11 | 33500 | 3.5965 | |
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| 3.515 | 3.15 | 34000 | 3.5932 | |
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| 3.5247 | 3.2 | 34500 | 3.5951 | |
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| 3.5384 | 3.24 | 35000 | 3.5917 | |
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| 3.5165 | 3.29 | 35500 | 3.5887 | |
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| 3.5187 | 3.34 | 36000 | 3.5866 | |
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| 3.5097 | 3.38 | 36500 | 3.5895 | |
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| 3.5136 | 3.43 | 37000 | 3.5878 | |
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| 3.5095 | 3.48 | 37500 | 3.5839 | |
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| 3.5226 | 3.52 | 38000 | 3.5859 | |
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| 3.5277 | 3.57 | 38500 | 3.5827 | |
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| 3.4959 | 3.62 | 39000 | 3.5846 | |
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| 3.5003 | 3.66 | 39500 | 3.5823 | |
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| 3.5095 | 3.71 | 40000 | 3.5820 | |
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| 3.4814 | 3.75 | 40500 | 3.5854 | |
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| 3.5173 | 3.8 | 41000 | 3.5796 | |
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| 3.4968 | 3.85 | 41500 | 3.5810 | |
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| 3.5183 | 3.89 | 42000 | 3.5783 | |
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| 3.512 | 3.94 | 42500 | 3.5784 | |
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| 3.5069 | 3.99 | 43000 | 3.5775 | |
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| 3.5014 | 4.03 | 43500 | 3.5819 | |
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| 3.4787 | 4.08 | 44000 | 3.5836 | |
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| 3.4625 | 4.12 | 44500 | 3.5788 | |
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| 3.4902 | 4.17 | 45000 | 3.5784 | |
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| 3.4927 | 4.22 | 45500 | 3.5773 | |
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| 3.4813 | 4.26 | 46000 | 3.5769 | |
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| 3.4637 | 4.31 | 46500 | 3.5761 | |
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| 3.4731 | 4.36 | 47000 | 3.5771 | |
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| 3.4856 | 4.4 | 47500 | 3.5786 | |
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| 3.4579 | 4.45 | 48000 | 3.5790 | |
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| 3.5032 | 4.5 | 48500 | 3.5738 | |
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| 3.4826 | 4.54 | 49000 | 3.5749 | |
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| 3.4709 | 4.59 | 49500 | 3.5746 | |
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| 3.4916 | 4.63 | 50000 | 3.5745 | |
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| 3.4715 | 4.68 | 50500 | 3.5706 | |
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| 3.4926 | 4.73 | 51000 | 3.5729 | |
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| 3.4974 | 4.77 | 51500 | 3.5725 | |
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| 3.4796 | 4.82 | 52000 | 3.5683 | |
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| 3.4817 | 4.87 | 52500 | 3.5707 | |
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| 3.4683 | 4.91 | 53000 | 3.5721 | |
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| 3.4986 | 4.96 | 53500 | 3.5689 | |
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| 3.4763 | 5.01 | 54000 | 3.5716 | |
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| 3.4668 | 5.05 | 54500 | 3.5700 | |
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| 3.4274 | 5.1 | 55000 | 3.5724 | |
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| 3.4499 | 5.14 | 55500 | 3.5717 | |
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| 3.4507 | 5.19 | 56000 | 3.5706 | |
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| 3.4343 | 5.24 | 56500 | 3.5697 | |
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| 3.4151 | 5.28 | 57000 | 3.5710 | |
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| 3.4469 | 5.33 | 57500 | 3.5712 | |
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| 3.458 | 5.38 | 58000 | 3.5692 | |
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| 3.4559 | 5.42 | 58500 | 3.5680 | |
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| 3.4354 | 5.47 | 59000 | 3.5683 | |
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| 3.4479 | 5.52 | 59500 | 3.5703 | |
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| 3.4627 | 5.56 | 60000 | 3.5678 | |
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| 3.4478 | 5.61 | 60500 | 3.5659 | |
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| 3.4645 | 5.65 | 61000 | 3.5675 | |
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| 3.4658 | 5.7 | 61500 | 3.5666 | |
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| 3.4657 | 5.75 | 62000 | 3.5658 | |
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| 3.4618 | 5.79 | 62500 | 3.5653 | |
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| 3.4541 | 5.84 | 63000 | 3.5653 | |
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| 3.4552 | 5.89 | 63500 | 3.5648 | |
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| 3.4679 | 5.93 | 64000 | 3.5648 | |
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| 3.4423 | 5.98 | 64500 | 3.5652 | |
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| 3.3893 | 6.03 | 65000 | 3.5646 | |
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| 3.4239 | 6.07 | 65500 | 3.5668 | |
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| 3.4329 | 6.12 | 66000 | 3.5639 | |
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| 3.4151 | 6.16 | 66500 | 3.5649 | |
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| 3.4181 | 6.21 | 67000 | 3.5682 | |
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| 3.4314 | 6.26 | 67500 | 3.5669 | |
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| 3.4245 | 6.3 | 68000 | 3.5629 | |
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| 3.421 | 6.35 | 68500 | 3.5663 | |
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| 3.4329 | 6.4 | 69000 | 3.5660 | |
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| 3.4122 | 6.44 | 69500 | 3.5651 | |
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| 3.4362 | 6.49 | 70000 | 3.5628 | |
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| 3.4497 | 6.54 | 70500 | 3.5648 | |
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| 3.431 | 6.58 | 71000 | 3.5626 | |
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| 3.432 | 6.63 | 71500 | 3.5648 | |
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| 3.4208 | 6.67 | 72000 | 3.5635 | |
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| 3.4526 | 6.72 | 72500 | 3.5645 | |
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| 3.4139 | 6.77 | 73000 | 3.5621 | |
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| 3.4212 | 6.81 | 73500 | 3.5629 | |
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| 3.4352 | 6.86 | 74000 | 3.5597 | |
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| 3.4242 | 6.91 | 74500 | 3.5597 | |
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| 3.429 | 6.95 | 75000 | 3.5619 | |
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| 3.4133 | 7.0 | 75500 | 3.5592 | |
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| 3.4086 | 7.04 | 76000 | 3.5621 | |
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| 3.4056 | 7.09 | 76500 | 3.5604 | |
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| 3.4158 | 7.14 | 77000 | 3.5629 | |
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| 3.4153 | 7.18 | 77500 | 3.5609 | |
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| 3.4155 | 7.23 | 78000 | 3.5621 | |
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| 3.4117 | 7.28 | 78500 | 3.5626 | |
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| 3.407 | 7.32 | 79000 | 3.5638 | |
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| 3.3977 | 7.37 | 79500 | 3.5604 | |
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| 3.4134 | 7.42 | 80000 | 3.5611 | |
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| 3.4403 | 7.46 | 80500 | 3.5630 | |
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| 3.4002 | 7.51 | 81000 | 3.5601 | |
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| 3.4147 | 7.55 | 81500 | 3.5577 | |
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| 3.4068 | 7.6 | 82000 | 3.5588 | |
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| 3.4165 | 7.65 | 82500 | 3.5613 | |
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| 3.409 | 7.69 | 83000 | 3.5596 | |
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| 3.4213 | 7.74 | 83500 | 3.5583 | |
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| 3.403 | 7.79 | 84000 | 3.5601 | |
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| 3.3819 | 7.83 | 84500 | 3.5580 | |
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| 3.4182 | 7.88 | 85000 | 3.5570 | |
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| 3.4099 | 7.93 | 85500 | 3.5570 | |
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| 3.3845 | 7.97 | 86000 | 3.5582 | |
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| 3.411 | 8.02 | 86500 | 3.5610 | |
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| 3.3952 | 8.06 | 87000 | 3.5588 | |
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| 3.4211 | 8.11 | 87500 | 3.5588 | |
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| 3.4171 | 8.16 | 88000 | 3.5570 | |
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| 3.3825 | 8.2 | 88500 | 3.5607 | |
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| 3.3807 | 8.25 | 89000 | 3.5579 | |
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| 3.3842 | 8.3 | 89500 | 3.5583 | |
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| 3.3809 | 8.34 | 90000 | 3.5596 | |
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| 3.4033 | 8.39 | 90500 | 3.5590 | |
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| 3.4156 | 8.44 | 91000 | 3.5577 | |
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| 3.3927 | 8.48 | 91500 | 3.5585 | |
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| 3.4041 | 8.53 | 92000 | 3.5596 | |
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| 3.4006 | 8.57 | 92500 | 3.5600 | |
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| 3.4007 | 8.62 | 93000 | 3.5578 | |
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| 3.4047 | 8.67 | 93500 | 3.5572 | |
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| 3.3904 | 8.71 | 94000 | 3.5571 | |
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| 3.3888 | 8.76 | 94500 | 3.5581 | |
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| 3.3876 | 8.81 | 95000 | 3.5572 | |
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| 3.3872 | 8.85 | 95500 | 3.5575 | |
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| 3.3753 | 8.9 | 96000 | 3.5577 | |
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| 3.3961 | 8.95 | 96500 | 3.5568 | |
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| 3.4131 | 8.99 | 97000 | 3.5579 | |
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| 3.3647 | 9.04 | 97500 | 3.5573 | |
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| 3.3792 | 9.08 | 98000 | 3.5576 | |
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| 3.3755 | 9.13 | 98500 | 3.5575 | |
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| 3.3981 | 9.18 | 99000 | 3.5573 | |
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| 3.3914 | 9.22 | 99500 | 3.5573 | |
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| 3.4136 | 9.27 | 100000 | 3.5575 | |
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### Framework versions |
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- Transformers 4.20.0.dev0 |
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- Pytorch 1.11.0 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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