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Centrum

Text2Text Generation
Transformers
PyTorch
led
Generated from Trainer
Inference Endpoints
Model card Files Community
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  1. README.md +262 -0
  2. added_tokens.json +3 -0
  3. all_results.json +13 -0
  4. config.json +54 -0
  5. eval_results.json +8 -0
  6. merges.txt +0 -0
  7. pytorch_model.bin +3 -0
  8. special_tokens_map.json +51 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +65 -0
  11. train_results.json +8 -0
  12. trainer_state.json +0 -0
  13. training_args.bin +3 -0
  14. vocab.json +0 -0
README.md CHANGED
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  ---
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  license: apache-2.0
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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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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+
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+ # Centrum
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+
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+ This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the NewSHead dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 3.5568
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+ | 3.5572 | 2.97 | 32000 | 3.5947 |
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+ | 3.5142 | 3.06 | 33000 | 3.5937 |
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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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+ | 3.5136 | 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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+ | 3.3914 | 9.22 | 99500 | 3.5573 |
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+ | 3.4136 | 9.27 | 100000 | 3.5575 |
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+
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+
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+ ### Framework versions
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+
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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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+ {
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+ "use_cache": true,
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+ "vocab_size": 50266
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+ }
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