Commit
•
6eda8d9
1
Parent(s):
c556def
Training in progress, step 32000
Browse files- README.md +356 -0
- added_tokens.json +7 -0
- all_results.json +14 -0
- config.json +28 -0
- eval_results.json +9 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +9 -0
- tokenizer.json +0 -0
- tokenizer_config.json +62 -0
- train_results.json +8 -0
- trainer_state.json +3328 -0
- training_args.bin +3 -0
- vocab.json +0 -0
README.md
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| 1 |
+
---
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license: mit
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base_model: cardiffnlp/twitter-roberta-base-2019-90m
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tags:
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- generated_from_trainer
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model-index:
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- name: 2020-Q4-50p-filtered
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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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+
# 2020-Q4-50p-filtered
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2019-90m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.6101
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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: 4.1e-07
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
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- lr_scheduler_type: linear
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- training_steps: 2400000
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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| 48 |
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|:-------------:|:-----:|:-------:|:---------------:|
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| 49 |
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| No log | 0.03 | 8000 | 2.9660 |
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| 50 |
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| 3.1627 | 0.07 | 16000 | 2.8754 |
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| 51 |
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| 3.1627 | 0.1 | 24000 | 2.8263 |
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| 52 |
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| 2.9611 | 0.13 | 32000 | 2.7973 |
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| 53 |
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| 2.9611 | 0.17 | 40000 | 2.7741 |
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| 54 |
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| 2.8986 | 0.2 | 48000 | 2.7574 |
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| 55 |
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| 2.8986 | 0.24 | 56000 | 2.7413 |
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| 56 |
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| 2.8726 | 0.27 | 64000 | 2.7240 |
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| 2.8726 | 0.3 | 72000 | 2.7239 |
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| 58 |
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| 2.8558 | 0.34 | 80000 | 2.7132 |
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| 59 |
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| 2.8558 | 0.37 | 88000 | 2.7030 |
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| 60 |
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| 2.8459 | 0.4 | 96000 | 2.7112 |
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| 61 |
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| 2.8459 | 0.44 | 104000 | 2.6918 |
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| 62 |
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| 2.8379 | 0.47 | 112000 | 2.7017 |
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| 63 |
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| 2.8379 | 0.51 | 120000 | 2.6920 |
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| 64 |
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| 2.8265 | 0.54 | 128000 | 2.6971 |
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| 65 |
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| 2.8265 | 0.57 | 136000 | 2.6924 |
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| 66 |
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| 2.8227 | 0.61 | 144000 | 2.6952 |
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| 67 |
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| 2.8227 | 0.64 | 152000 | 2.6811 |
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| 68 |
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| 2.8209 | 0.67 | 160000 | 2.6829 |
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| 69 |
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| 2.8209 | 0.71 | 168000 | 2.6883 |
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| 70 |
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| 2.8147 | 0.74 | 176000 | 2.6675 |
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| 71 |
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| 2.8147 | 0.77 | 184000 | 2.6674 |
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| 72 |
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| 2.8077 | 0.81 | 192000 | 2.6661 |
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| 73 |
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| 2.8077 | 0.84 | 200000 | 2.6773 |
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| 74 |
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| 2.8058 | 0.88 | 208000 | 2.6734 |
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| 75 |
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| 2.8058 | 0.91 | 216000 | 2.6742 |
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| 2.812 | 0.94 | 224000 | 2.6666 |
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| 2.812 | 0.98 | 232000 | 2.6642 |
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| 2.8025 | 1.01 | 240000 | 2.6681 |
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| 2.8025 | 1.04 | 248000 | 2.6663 |
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| 2.809 | 1.08 | 256000 | 2.6645 |
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| 2.809 | 1.11 | 264000 | 2.6529 |
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| 2.8073 | 1.15 | 272000 | 2.6623 |
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| 2.8073 | 1.18 | 280000 | 2.6551 |
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| 2.8005 | 1.21 | 288000 | 2.6643 |
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| 2.8005 | 1.25 | 296000 | 2.6628 |
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| 2.7988 | 1.28 | 304000 | 2.6583 |
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| 2.7988 | 1.31 | 312000 | 2.6594 |
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| 88 |
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| 2.7887 | 1.35 | 320000 | 2.6544 |
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| 89 |
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| 2.7887 | 1.38 | 328000 | 2.6516 |
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| 90 |
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| 2.7964 | 1.41 | 336000 | 2.6555 |
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| 91 |
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| 2.7964 | 1.45 | 344000 | 2.6551 |
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| 2.7919 | 1.48 | 352000 | 2.6508 |
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| 2.7919 | 1.52 | 360000 | 2.6486 |
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| 2.8058 | 1.55 | 368000 | 2.6484 |
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| 2.8058 | 1.58 | 376000 | 2.6532 |
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| 2.796 | 1.62 | 384000 | 2.6473 |
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| 2.796 | 1.65 | 392000 | 2.6489 |
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| 2.799 | 1.68 | 400000 | 2.6476 |
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| 2.799 | 1.72 | 408000 | 2.6417 |
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| 2.7991 | 1.75 | 416000 | 2.6545 |
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| 2.7991 | 1.79 | 424000 | 2.6466 |
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| 2.792 | 1.82 | 432000 | 2.6397 |
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| 2.792 | 1.85 | 440000 | 2.6428 |
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| 2.7972 | 1.89 | 448000 | 2.6446 |
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| 2.7972 | 1.92 | 456000 | 2.6434 |
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| 2.798 | 1.95 | 464000 | 2.6490 |
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| 2.798 | 1.99 | 472000 | 2.6502 |
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| 2.7914 | 2.02 | 480000 | 2.6407 |
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| 2.7914 | 2.05 | 488000 | 2.6284 |
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| 110 |
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| 2.7932 | 2.09 | 496000 | 2.6426 |
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| 111 |
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| 2.7932 | 2.12 | 504000 | 2.6423 |
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| 112 |
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| 2.787 | 2.16 | 512000 | 2.6385 |
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| 113 |
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| 2.787 | 2.19 | 520000 | 2.6388 |
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| 114 |
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| 2.7893 | 2.22 | 528000 | 2.6422 |
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| 115 |
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| 2.7893 | 2.26 | 536000 | 2.6410 |
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| 116 |
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| 2.7889 | 2.29 | 544000 | 2.6337 |
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| 117 |
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| 2.7889 | 2.32 | 552000 | 2.6280 |
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| 118 |
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| 2.791 | 2.36 | 560000 | 2.6364 |
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| 119 |
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| 2.791 | 2.39 | 568000 | 2.6341 |
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| 120 |
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| 2.7883 | 2.43 | 576000 | 2.6317 |
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| 121 |
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| 2.7883 | 2.46 | 584000 | 2.6278 |
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| 122 |
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| 2.7889 | 2.49 | 592000 | 2.6357 |
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| 123 |
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| 2.7889 | 2.53 | 600000 | 2.6341 |
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| 124 |
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| 2.7838 | 2.56 | 608000 | 2.6333 |
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| 125 |
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| 2.7838 | 2.59 | 616000 | 2.6382 |
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| 126 |
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| 2.7873 | 2.63 | 624000 | 2.6275 |
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| 127 |
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| 2.7873 | 2.66 | 632000 | 2.6260 |
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| 128 |
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| 2.7813 | 2.69 | 640000 | 2.6373 |
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| 129 |
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| 2.7813 | 2.73 | 648000 | 2.6349 |
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| 130 |
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| 2.7858 | 2.76 | 656000 | 2.6223 |
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| 131 |
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| 2.7858 | 2.8 | 664000 | 2.6276 |
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| 132 |
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| 2.7895 | 2.83 | 672000 | 2.6355 |
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| 133 |
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| 2.7895 | 2.86 | 680000 | 2.6270 |
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| 134 |
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| 2.7873 | 2.9 | 688000 | 2.6244 |
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| 135 |
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| 2.7873 | 2.93 | 696000 | 2.6397 |
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| 136 |
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| 2.7866 | 2.96 | 704000 | 2.6303 |
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| 137 |
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| 2.7866 | 3.0 | 712000 | 2.6167 |
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| 138 |
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| 2.7865 | 3.03 | 720000 | 2.6265 |
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| 139 |
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| 2.7865 | 3.07 | 728000 | 2.6403 |
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| 140 |
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| 2.7716 | 3.1 | 736000 | 2.6247 |
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| 141 |
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| 2.7716 | 3.13 | 744000 | 2.6255 |
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| 142 |
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| 2.779 | 3.17 | 752000 | 2.6316 |
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| 143 |
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| 2.779 | 3.2 | 760000 | 2.6270 |
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| 144 |
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| 2.7811 | 3.23 | 768000 | 2.6268 |
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| 145 |
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| 2.7811 | 3.27 | 776000 | 2.6147 |
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| 146 |
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| 2.7797 | 3.3 | 784000 | 2.6271 |
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| 2.7797 | 3.33 | 792000 | 2.6243 |
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| 2.7798 | 3.37 | 800000 | 2.6240 |
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| 2.7798 | 3.4 | 808000 | 2.6225 |
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| 2.7774 | 3.44 | 816000 | 2.6232 |
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| 2.7774 | 3.47 | 824000 | 2.6247 |
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| 2.7744 | 3.5 | 832000 | 2.6270 |
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| 2.7744 | 3.54 | 840000 | 2.6175 |
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| 2.7786 | 3.57 | 848000 | 2.6264 |
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| 2.7786 | 3.6 | 856000 | 2.6192 |
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| 2.7829 | 3.64 | 864000 | 2.6278 |
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| 2.7829 | 3.67 | 872000 | 2.6237 |
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| 2.776 | 3.71 | 880000 | 2.6202 |
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| 2.776 | 3.74 | 888000 | 2.6216 |
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| 2.7797 | 3.77 | 896000 | 2.6174 |
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| 2.7797 | 3.81 | 904000 | 2.6239 |
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| 2.7744 | 3.84 | 912000 | 2.6163 |
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| 2.7744 | 3.87 | 920000 | 2.6198 |
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| 2.7713 | 3.91 | 928000 | 2.6236 |
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| 2.7713 | 3.94 | 936000 | 2.6226 |
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| 2.7853 | 3.97 | 944000 | 2.6175 |
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| 2.7853 | 4.01 | 952000 | 2.6189 |
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| 2.7766 | 4.04 | 960000 | 2.6192 |
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| 2.7766 | 4.08 | 968000 | 2.6318 |
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| 2.7851 | 4.11 | 976000 | 2.6210 |
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| 171 |
+
| 2.7851 | 4.14 | 984000 | 2.6172 |
|
| 172 |
+
| 2.7804 | 4.18 | 992000 | 2.6200 |
|
| 173 |
+
| 2.7804 | 4.21 | 1000000 | 2.6157 |
|
| 174 |
+
| 2.773 | 4.24 | 1008000 | 2.6098 |
|
| 175 |
+
| 2.773 | 4.28 | 1016000 | 2.6156 |
|
| 176 |
+
| 2.7818 | 4.31 | 1024000 | 2.6149 |
|
| 177 |
+
| 2.7818 | 4.35 | 1032000 | 2.6121 |
|
| 178 |
+
| 2.7736 | 4.38 | 1040000 | 2.6150 |
|
| 179 |
+
| 2.7736 | 4.41 | 1048000 | 2.6156 |
|
| 180 |
+
| 2.7761 | 4.45 | 1056000 | 2.6171 |
|
| 181 |
+
| 2.7761 | 4.48 | 1064000 | 2.6124 |
|
| 182 |
+
| 2.7789 | 4.51 | 1072000 | 2.6277 |
|
| 183 |
+
| 2.7789 | 4.55 | 1080000 | 2.6138 |
|
| 184 |
+
| 2.7744 | 4.58 | 1088000 | 2.6081 |
|
| 185 |
+
| 2.7744 | 4.61 | 1096000 | 2.6201 |
|
| 186 |
+
| 2.77 | 4.65 | 1104000 | 2.6171 |
|
| 187 |
+
| 2.77 | 4.68 | 1112000 | 2.6099 |
|
| 188 |
+
| 2.772 | 4.72 | 1120000 | 2.6141 |
|
| 189 |
+
| 2.772 | 4.75 | 1128000 | 2.6174 |
|
| 190 |
+
| 2.7709 | 4.78 | 1136000 | 2.6200 |
|
| 191 |
+
| 2.7709 | 4.82 | 1144000 | 2.6150 |
|
| 192 |
+
| 2.7724 | 4.85 | 1152000 | 2.6042 |
|
| 193 |
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| 2.7724 | 4.88 | 1160000 | 2.6158 |
|
| 194 |
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| 2.7763 | 4.92 | 1168000 | 2.6167 |
|
| 195 |
+
| 2.7763 | 4.95 | 1176000 | 2.6174 |
|
| 196 |
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| 2.7736 | 4.99 | 1184000 | 2.6099 |
|
| 197 |
+
| 2.7736 | 5.02 | 1192000 | 2.6076 |
|
| 198 |
+
| 2.7692 | 5.05 | 1200000 | 2.6088 |
|
| 199 |
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| 2.7692 | 5.09 | 1208000 | 2.6174 |
|
| 200 |
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| 2.7794 | 5.12 | 1216000 | 2.6041 |
|
| 201 |
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| 2.7794 | 5.15 | 1224000 | 2.6051 |
|
| 202 |
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| 2.7709 | 5.19 | 1232000 | 2.6093 |
|
| 203 |
+
| 2.7709 | 5.22 | 1240000 | 2.6062 |
|
| 204 |
+
| 2.7727 | 5.25 | 1248000 | 2.6052 |
|
| 205 |
+
| 2.7727 | 5.29 | 1256000 | 2.6126 |
|
| 206 |
+
| 2.7686 | 5.32 | 1264000 | 2.6099 |
|
| 207 |
+
| 2.7686 | 5.36 | 1272000 | 2.6192 |
|
| 208 |
+
| 2.7668 | 5.39 | 1280000 | 2.6166 |
|
| 209 |
+
| 2.7668 | 5.42 | 1288000 | 2.6042 |
|
| 210 |
+
| 2.7777 | 5.46 | 1296000 | 2.6038 |
|
| 211 |
+
| 2.7777 | 5.49 | 1304000 | 2.6119 |
|
| 212 |
+
| 2.7737 | 5.52 | 1312000 | 2.6155 |
|
| 213 |
+
| 2.7737 | 5.56 | 1320000 | 2.6236 |
|
| 214 |
+
| 2.7757 | 5.59 | 1328000 | 2.6124 |
|
| 215 |
+
| 2.7757 | 5.63 | 1336000 | 2.5993 |
|
| 216 |
+
| 2.7757 | 5.66 | 1344000 | 2.6132 |
|
| 217 |
+
| 2.7757 | 5.69 | 1352000 | 2.6063 |
|
| 218 |
+
| 2.7748 | 5.73 | 1360000 | 2.6130 |
|
| 219 |
+
| 2.7748 | 5.76 | 1368000 | 2.6100 |
|
| 220 |
+
| 2.769 | 5.79 | 1376000 | 2.6024 |
|
| 221 |
+
| 2.769 | 5.83 | 1384000 | 2.6062 |
|
| 222 |
+
| 2.7713 | 5.86 | 1392000 | 2.6138 |
|
| 223 |
+
| 2.7713 | 5.89 | 1400000 | 2.6025 |
|
| 224 |
+
| 2.7766 | 5.93 | 1408000 | 2.6088 |
|
| 225 |
+
| 2.7766 | 5.96 | 1416000 | 2.6138 |
|
| 226 |
+
| 2.7727 | 6.0 | 1424000 | 2.6048 |
|
| 227 |
+
| 2.7727 | 6.03 | 1432000 | 2.6068 |
|
| 228 |
+
| 2.7737 | 6.06 | 1440000 | 2.6144 |
|
| 229 |
+
| 2.7737 | 6.1 | 1448000 | 2.6051 |
|
| 230 |
+
| 2.778 | 6.13 | 1456000 | 2.6158 |
|
| 231 |
+
| 2.778 | 6.16 | 1464000 | 2.6152 |
|
| 232 |
+
| 2.7767 | 6.2 | 1472000 | 2.6019 |
|
| 233 |
+
| 2.7767 | 6.23 | 1480000 | 2.6117 |
|
| 234 |
+
| 2.7706 | 6.27 | 1488000 | 2.6065 |
|
| 235 |
+
| 2.7706 | 6.3 | 1496000 | 2.6122 |
|
| 236 |
+
| 2.7775 | 6.33 | 1504000 | 2.6100 |
|
| 237 |
+
| 2.7775 | 6.37 | 1512000 | 2.6100 |
|
| 238 |
+
| 2.7753 | 6.4 | 1520000 | 2.6051 |
|
| 239 |
+
| 2.7753 | 6.43 | 1528000 | 2.6037 |
|
| 240 |
+
| 2.7691 | 6.47 | 1536000 | 2.6037 |
|
| 241 |
+
| 2.7691 | 6.5 | 1544000 | 2.5992 |
|
| 242 |
+
| 2.758 | 6.53 | 1552000 | 2.6080 |
|
| 243 |
+
| 2.758 | 6.57 | 1560000 | 2.6139 |
|
| 244 |
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| 2.7722 | 6.6 | 1568000 | 2.6000 |
|
| 245 |
+
| 2.7722 | 6.64 | 1576000 | 2.6107 |
|
| 246 |
+
| 2.7737 | 6.67 | 1584000 | 2.6057 |
|
| 247 |
+
| 2.7737 | 6.7 | 1592000 | 2.6063 |
|
| 248 |
+
| 2.7722 | 6.74 | 1600000 | 2.6028 |
|
| 249 |
+
| 2.7722 | 6.77 | 1608000 | 2.5995 |
|
| 250 |
+
| 2.7659 | 6.8 | 1616000 | 2.6042 |
|
| 251 |
+
| 2.7659 | 6.84 | 1624000 | 2.6013 |
|
| 252 |
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| 2.7769 | 6.87 | 1632000 | 2.6028 |
|
| 253 |
+
| 2.7769 | 6.91 | 1640000 | 2.6080 |
|
| 254 |
+
| 2.7732 | 6.94 | 1648000 | 2.5994 |
|
| 255 |
+
| 2.7732 | 6.97 | 1656000 | 2.6063 |
|
| 256 |
+
| 2.7708 | 7.01 | 1664000 | 2.6120 |
|
| 257 |
+
| 2.7708 | 7.04 | 1672000 | 2.6023 |
|
| 258 |
+
| 2.7614 | 7.07 | 1680000 | 2.6091 |
|
| 259 |
+
| 2.7614 | 7.11 | 1688000 | 2.6003 |
|
| 260 |
+
| 2.7655 | 7.14 | 1696000 | 2.6016 |
|
| 261 |
+
| 2.7655 | 7.17 | 1704000 | 2.6058 |
|
| 262 |
+
| 2.7747 | 7.21 | 1712000 | 2.6045 |
|
| 263 |
+
| 2.7747 | 7.24 | 1720000 | 2.6097 |
|
| 264 |
+
| 2.7685 | 7.28 | 1728000 | 2.6068 |
|
| 265 |
+
| 2.7685 | 7.31 | 1736000 | 2.6037 |
|
| 266 |
+
| 2.7736 | 7.34 | 1744000 | 2.6125 |
|
| 267 |
+
| 2.7736 | 7.38 | 1752000 | 2.6113 |
|
| 268 |
+
| 2.7666 | 7.41 | 1760000 | 2.5972 |
|
| 269 |
+
| 2.7666 | 7.44 | 1768000 | 2.6081 |
|
| 270 |
+
| 2.7658 | 7.48 | 1776000 | 2.6090 |
|
| 271 |
+
| 2.7658 | 7.51 | 1784000 | 2.6126 |
|
| 272 |
+
| 2.7802 | 7.55 | 1792000 | 2.6021 |
|
| 273 |
+
| 2.7802 | 7.58 | 1800000 | 2.6087 |
|
| 274 |
+
| 2.7749 | 7.61 | 1808000 | 2.5986 |
|
| 275 |
+
| 2.7749 | 7.65 | 1816000 | 2.6002 |
|
| 276 |
+
| 2.7689 | 7.68 | 1824000 | 2.6023 |
|
| 277 |
+
| 2.7689 | 7.71 | 1832000 | 2.5969 |
|
| 278 |
+
| 2.7699 | 7.75 | 1840000 | 2.5975 |
|
| 279 |
+
| 2.7699 | 7.78 | 1848000 | 2.6070 |
|
| 280 |
+
| 2.7715 | 7.81 | 1856000 | 2.6035 |
|
| 281 |
+
| 2.7715 | 7.85 | 1864000 | 2.6049 |
|
| 282 |
+
| 2.7653 | 7.88 | 1872000 | 2.6129 |
|
| 283 |
+
| 2.7653 | 7.92 | 1880000 | 2.6027 |
|
| 284 |
+
| 2.7729 | 7.95 | 1888000 | 2.6000 |
|
| 285 |
+
| 2.7729 | 7.98 | 1896000 | 2.6138 |
|
| 286 |
+
| 2.7693 | 8.02 | 1904000 | 2.6052 |
|
| 287 |
+
| 2.7693 | 8.05 | 1912000 | 2.6060 |
|
| 288 |
+
| 2.7585 | 8.08 | 1920000 | 2.6065 |
|
| 289 |
+
| 2.7585 | 8.12 | 1928000 | 2.6105 |
|
| 290 |
+
| 2.7652 | 8.15 | 1936000 | 2.6075 |
|
| 291 |
+
| 2.7652 | 8.19 | 1944000 | 2.6076 |
|
| 292 |
+
| 2.7508 | 8.22 | 1952000 | 2.6083 |
|
| 293 |
+
| 2.7508 | 8.25 | 1960000 | 2.6112 |
|
| 294 |
+
| 2.7678 | 8.29 | 1968000 | 2.6019 |
|
| 295 |
+
| 2.7678 | 8.32 | 1976000 | 2.6029 |
|
| 296 |
+
| 2.7653 | 8.35 | 1984000 | 2.6087 |
|
| 297 |
+
| 2.7653 | 8.39 | 1992000 | 2.6064 |
|
| 298 |
+
| 2.7661 | 8.42 | 2000000 | 2.6031 |
|
| 299 |
+
| 2.7661 | 8.45 | 2008000 | 2.6051 |
|
| 300 |
+
| 2.7742 | 8.49 | 2016000 | 2.6091 |
|
| 301 |
+
| 2.7742 | 8.52 | 2024000 | 2.5978 |
|
| 302 |
+
| 2.7748 | 8.56 | 2032000 | 2.6131 |
|
| 303 |
+
| 2.7748 | 8.59 | 2040000 | 2.6030 |
|
| 304 |
+
| 2.7706 | 8.62 | 2048000 | 2.6036 |
|
| 305 |
+
| 2.7706 | 8.66 | 2056000 | 2.5998 |
|
| 306 |
+
| 2.769 | 8.69 | 2064000 | 2.6013 |
|
| 307 |
+
| 2.769 | 8.72 | 2072000 | 2.6000 |
|
| 308 |
+
| 2.7733 | 8.76 | 2080000 | 2.6062 |
|
| 309 |
+
| 2.7733 | 8.79 | 2088000 | 2.6057 |
|
| 310 |
+
| 2.7714 | 8.83 | 2096000 | 2.6021 |
|
| 311 |
+
| 2.7714 | 8.86 | 2104000 | 2.6028 |
|
| 312 |
+
| 2.7754 | 8.89 | 2112000 | 2.5964 |
|
| 313 |
+
| 2.7754 | 8.93 | 2120000 | 2.6015 |
|
| 314 |
+
| 2.7683 | 8.96 | 2128000 | 2.6060 |
|
| 315 |
+
| 2.7683 | 8.99 | 2136000 | 2.6082 |
|
| 316 |
+
| 2.7758 | 9.03 | 2144000 | 2.6130 |
|
| 317 |
+
| 2.7758 | 9.06 | 2152000 | 2.6071 |
|
| 318 |
+
| 2.768 | 9.09 | 2160000 | 2.6141 |
|
| 319 |
+
| 2.768 | 9.13 | 2168000 | 2.6003 |
|
| 320 |
+
| 2.7653 | 9.16 | 2176000 | 2.5987 |
|
| 321 |
+
| 2.7653 | 9.2 | 2184000 | 2.6066 |
|
| 322 |
+
| 2.7621 | 9.23 | 2192000 | 2.6041 |
|
| 323 |
+
| 2.7621 | 9.26 | 2200000 | 2.6060 |
|
| 324 |
+
| 2.7712 | 9.3 | 2208000 | 2.6144 |
|
| 325 |
+
| 2.7712 | 9.33 | 2216000 | 2.5990 |
|
| 326 |
+
| 2.7718 | 9.36 | 2224000 | 2.6039 |
|
| 327 |
+
| 2.7718 | 9.4 | 2232000 | 2.5931 |
|
| 328 |
+
| 2.774 | 9.43 | 2240000 | 2.6129 |
|
| 329 |
+
| 2.774 | 9.47 | 2248000 | 2.6095 |
|
| 330 |
+
| 2.765 | 9.5 | 2256000 | 2.5932 |
|
| 331 |
+
| 2.765 | 9.53 | 2264000 | 2.6010 |
|
| 332 |
+
| 2.7754 | 9.57 | 2272000 | 2.6078 |
|
| 333 |
+
| 2.7754 | 9.6 | 2280000 | 2.5981 |
|
| 334 |
+
| 2.771 | 9.63 | 2288000 | 2.6052 |
|
| 335 |
+
| 2.771 | 9.67 | 2296000 | 2.5944 |
|
| 336 |
+
| 2.7757 | 9.7 | 2304000 | 2.6045 |
|
| 337 |
+
| 2.7757 | 9.73 | 2312000 | 2.5971 |
|
| 338 |
+
| 2.7685 | 9.77 | 2320000 | 2.6101 |
|
| 339 |
+
| 2.7685 | 9.8 | 2328000 | 2.5964 |
|
| 340 |
+
| 2.7708 | 9.84 | 2336000 | 2.5974 |
|
| 341 |
+
| 2.7708 | 9.87 | 2344000 | 2.5953 |
|
| 342 |
+
| 2.7695 | 9.9 | 2352000 | 2.5981 |
|
| 343 |
+
| 2.7695 | 9.94 | 2360000 | 2.6095 |
|
| 344 |
+
| 2.7702 | 9.97 | 2368000 | 2.6042 |
|
| 345 |
+
| 2.7702 | 10.0 | 2376000 | 2.6095 |
|
| 346 |
+
| 2.7614 | 10.04 | 2384000 | 2.6007 |
|
| 347 |
+
| 2.7614 | 10.07 | 2392000 | 2.6017 |
|
| 348 |
+
| 2.7708 | 10.11 | 2400000 | 2.6114 |
|
| 349 |
+
|
| 350 |
+
|
| 351 |
+
### Framework versions
|
| 352 |
+
|
| 353 |
+
- Transformers 4.35.0.dev0
|
| 354 |
+
- Pytorch 2.0.1+cu117
|
| 355 |
+
- Datasets 2.14.5
|
| 356 |
+
- Tokenizers 0.14.0
|
added_tokens.json
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"</s>": 2,
|
| 3 |
+
"<mask>": 50264,
|
| 4 |
+
"<pad>": 1,
|
| 5 |
+
"<s>": 0,
|
| 6 |
+
"<unk>": 3
|
| 7 |
+
}
|
all_results.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"epoch": 10.11,
|
| 3 |
+
"eval_loss": 2.6100757122039795,
|
| 4 |
+
"eval_runtime": 417.4399,
|
| 5 |
+
"eval_samples": 199998,
|
| 6 |
+
"eval_samples_per_second": 479.106,
|
| 7 |
+
"eval_steps_per_second": 29.944,
|
| 8 |
+
"perplexity": 13.600080504921108,
|
| 9 |
+
"train_loss": 2.7854965201822917,
|
| 10 |
+
"train_runtime": 398406.2107,
|
| 11 |
+
"train_samples": 3799959,
|
| 12 |
+
"train_samples_per_second": 96.384,
|
| 13 |
+
"train_steps_per_second": 6.024
|
| 14 |
+
}
|
config.json
ADDED
|
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "cardiffnlp/twitter-roberta-base-2019-90m",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"RobertaForMaskedLM"
|
| 5 |
+
],
|
| 6 |
+
"attention_probs_dropout_prob": 0.1,
|
| 7 |
+
"bos_token_id": 0,
|
| 8 |
+
"classifier_dropout": null,
|
| 9 |
+
"eos_token_id": 2,
|
| 10 |
+
"gradient_checkpointing": false,
|
| 11 |
+
"hidden_act": "gelu",
|
| 12 |
+
"hidden_dropout_prob": 0.1,
|
| 13 |
+
"hidden_size": 768,
|
| 14 |
+
"initializer_range": 0.02,
|
| 15 |
+
"intermediate_size": 3072,
|
| 16 |
+
"layer_norm_eps": 1e-05,
|
| 17 |
+
"max_position_embeddings": 514,
|
| 18 |
+
"model_type": "roberta",
|
| 19 |
+
"num_attention_heads": 12,
|
| 20 |
+
"num_hidden_layers": 12,
|
| 21 |
+
"pad_token_id": 1,
|
| 22 |
+
"position_embedding_type": "absolute",
|
| 23 |
+
"torch_dtype": "float32",
|
| 24 |
+
"transformers_version": "4.35.0.dev0",
|
| 25 |
+
"type_vocab_size": 1,
|
| 26 |
+
"use_cache": true,
|
| 27 |
+
"vocab_size": 50265
|
| 28 |
+
}
|
eval_results.json
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"epoch": 10.11,
|
| 3 |
+
"eval_loss": 2.6100757122039795,
|
| 4 |
+
"eval_runtime": 417.4399,
|
| 5 |
+
"eval_samples": 199998,
|
| 6 |
+
"eval_samples_per_second": 479.106,
|
| 7 |
+
"eval_steps_per_second": 29.944,
|
| 8 |
+
"perplexity": 13.600080504921108
|
| 9 |
+
}
|
merges.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
pytorch_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:879b14f84d083b35c2fc0578e6070282ecf93dcfba68cccecbc69eb4219f2923
|
| 3 |
+
size 498859189
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": "<s>",
|
| 3 |
+
"cls_token": "<s>",
|
| 4 |
+
"eos_token": "</s>",
|
| 5 |
+
"mask_token": "<mask>",
|
| 6 |
+
"pad_token": "<pad>",
|
| 7 |
+
"sep_token": "</s>",
|
| 8 |
+
"unk_token": "<unk>"
|
| 9 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,62 @@
|
|
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|
|
|
|
|
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|
|
|
|
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|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"added_tokens_decoder": {
|
| 4 |
+
"0": {
|
| 5 |
+
"content": "<s>",
|
| 6 |
+
"lstrip": false,
|
| 7 |
+
"normalized": false,
|
| 8 |
+
"rstrip": false,
|
| 9 |
+
"single_word": false,
|
| 10 |
+
"special": true
|
| 11 |
+
},
|
| 12 |
+
"1": {
|
| 13 |
+
"content": "<pad>",
|
| 14 |
+
"lstrip": false,
|
| 15 |
+
"normalized": false,
|
| 16 |
+
"rstrip": false,
|
| 17 |
+
"single_word": false,
|
| 18 |
+
"special": true
|
| 19 |
+
},
|
| 20 |
+
"2": {
|
| 21 |
+
"content": "</s>",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": false,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false,
|
| 26 |
+
"special": true
|
| 27 |
+
},
|
| 28 |
+
"3": {
|
| 29 |
+
"content": "<unk>",
|
| 30 |
+
"lstrip": false,
|
| 31 |
+
"normalized": false,
|
| 32 |
+
"rstrip": false,
|
| 33 |
+
"single_word": false,
|
| 34 |
+
"special": true
|
| 35 |
+
},
|
| 36 |
+
"50264": {
|
| 37 |
+
"content": "<mask>",
|
| 38 |
+
"lstrip": true,
|
| 39 |
+
"normalized": false,
|
| 40 |
+
"rstrip": false,
|
| 41 |
+
"single_word": false,
|
| 42 |
+
"special": true
|
| 43 |
+
}
|
| 44 |
+
},
|
| 45 |
+
"additional_special_tokens": [],
|
| 46 |
+
"bos_token": "<s>",
|
| 47 |
+
"clean_up_tokenization_spaces": true,
|
| 48 |
+
"cls_token": "<s>",
|
| 49 |
+
"eos_token": "</s>",
|
| 50 |
+
"errors": "replace",
|
| 51 |
+
"mask_token": "<mask>",
|
| 52 |
+
"max_length": 512,
|
| 53 |
+
"model_max_length": 512,
|
| 54 |
+
"pad_token": "<pad>",
|
| 55 |
+
"sep_token": "</s>",
|
| 56 |
+
"stride": 0,
|
| 57 |
+
"tokenizer_class": "RobertaTokenizer",
|
| 58 |
+
"trim_offsets": true,
|
| 59 |
+
"truncation_side": "right",
|
| 60 |
+
"truncation_strategy": "longest_first",
|
| 61 |
+
"unk_token": "<unk>"
|
| 62 |
+
}
|
train_results.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"epoch": 10.11,
|
| 3 |
+
"train_loss": 2.7854965201822917,
|
| 4 |
+
"train_runtime": 398406.2107,
|
| 5 |
+
"train_samples": 3799959,
|
| 6 |
+
"train_samples_per_second": 96.384,
|
| 7 |
+
"train_steps_per_second": 6.024
|
| 8 |
+
}
|
trainer_state.json
ADDED
|
@@ -0,0 +1,3328 @@
|
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|
| 1 |
+
{
|
| 2 |
+
"best_metric": 2.596395254135132,
|
| 3 |
+
"best_model_checkpoint": "./model_tweets_2020_Q4_50/checkpoint-2112000",
|
| 4 |
+
"epoch": 10.105348255564257,
|
| 5 |
+
"eval_steps": 8000,
|
| 6 |
+
"global_step": 2400000,
|
| 7 |
+
"is_hyper_param_search": false,
|
| 8 |
+
"is_local_process_zero": true,
|
| 9 |
+
"is_world_process_zero": true,
|
| 10 |
+
"log_history": [
|
| 11 |
+
{
|
| 12 |
+
"epoch": 0.03,
|
| 13 |
+
"eval_loss": 2.9659674167633057,
|
| 14 |
+
"eval_runtime": 427.2887,
|
| 15 |
+
"eval_samples_per_second": 468.063,
|
| 16 |
+
"eval_steps_per_second": 29.254,
|
| 17 |
+
"step": 8000
|
| 18 |
+
},
|
| 19 |
+
{
|
| 20 |
+
"epoch": 0.07,
|
| 21 |
+
"learning_rate": 4.0726666666666665e-07,
|
| 22 |
+
"loss": 3.1627,
|
| 23 |
+
"step": 16000
|
| 24 |
+
},
|
| 25 |
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{
|
| 26 |
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"epoch": 0.07,
|
| 27 |
+
"eval_loss": 2.875443458557129,
|
| 28 |
+
"eval_runtime": 428.9511,
|
| 29 |
+
"eval_samples_per_second": 466.249,
|
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training_args.bin
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vocab.json
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