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childes-segmentation-18M-gpt2_lm-model

Text Generation
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gpt2
English
Generated from Trainer
text-generation-inference
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Final model for experiment English

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  1. README.md +110 -0
  2. generation_config.json +6 -0
  3. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ library_name: transformers
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+ tags:
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+ - English
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+ - generated_from_trainer
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+ model-index:
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+ - name: childes-segmentation-18M-gpt2_lm-model
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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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+ # childes-segmentation-18M-gpt2_lm-model
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+
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+ This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.5598
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+ - Model Preparation Time: 0.0013
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+ - Perplexity: 4.7580
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+ - Bpc: 2.2503
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+ - Spike Seg Type Fscore Entropy: 0.5424
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+ - Spike Seg Boundary Fscore Entropy: 0.7652
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+ - Absolute Seg Type Fscore Entropy: 0.4188
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+ - Absolute Seg Boundary Fscore Entropy: 0.6411
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+ - Spike Seg Type Fscore Increase in entropy: 0.5339
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+ - Spike Seg Boundary Fscore Increase in entropy: 0.7796
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+ - Absolute Seg Type Fscore Increase in entropy: 0.5744
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+ - Absolute Seg Boundary Fscore Increase in entropy: 0.7708
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+ - Spike Seg Type Fscore Loss: 0.4461
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+ - Spike Seg Boundary Fscore Loss: 0.6948
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+ - Absolute Seg Type Fscore Loss: 0.3397
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+ - Absolute Seg Boundary Fscore Loss: 0.6138
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+ - Spike Seg Type Fscore Increase in loss: 0.5024
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+ - Spike Seg Boundary Fscore Increase in loss: 0.7430
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+ - Absolute Seg Type Fscore Increase in loss: 0.5046
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+ - Absolute Seg Boundary Fscore Increase in loss: 0.7437
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+ - Spike Seg Type Fscore Rank: 0.4778
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+ - Spike Seg Boundary Fscore Rank: 0.6585
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+ - Absolute Seg Type Fscore Rank: 0.3314
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+ - Absolute Seg Boundary Fscore Rank: 0.5551
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+ - Spike Seg Type Fscore Increase in rank: 0.4977
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+ - Spike Seg Boundary Fscore Increase in rank: 0.6963
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+ - Absolute Seg Type Fscore Increase in rank: 0.4902
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+ - Absolute Seg Boundary Fscore Increase in rank: 0.7065
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+ - Spike Seg Type Fscore Boundary prediction: 0.5365
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+ - Spike Seg Boundary Fscore Boundary prediction: 0.8041
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+ - Absolute Seg Type Fscore Boundary prediction: 0.3187
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+ - Absolute Seg Boundary Fscore Boundary prediction: 0.7456
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+ - Spike Seg Type Fscore Increase in boundary prediction: 0.5171
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+ - Spike Seg Boundary Fscore Increase in boundary prediction: 0.7895
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+ - Absolute Seg Type Fscore Increase in boundary prediction: 0.2577
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+ - Absolute Seg Boundary Fscore Increase in boundary prediction: 0.5526
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+ - Spike Seg Type Fscore Majority vote cutoff: 0.6165
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+ - Spike Seg Type Fscore Majority vote spike: 0.4770
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+ - Absolute Seg Type Fscore Majority vote cutoff: 0.5211
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+ - Absolute Seg Type Fscore Majority vote spike: 0.6022
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+ - Spike Seg Boundary Fscore Majority vote cutoff: 0.8101
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+ - Spike Seg Boundary Fscore Majority vote spike: 0.7717
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+ - Absolute Seg Boundary Fscore Majority vote cutoff: 0.7609
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+ - Absolute Seg Boundary Fscore Majority vote spike: 0.8128
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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: 0.001
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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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: 60000
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+ - training_steps: 200000
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Perplexity | Bpc | Spike Seg Type Fscore Entropy | Spike Seg Boundary Fscore Entropy | Absolute Seg Type Fscore Entropy | Absolute Seg Boundary Fscore Entropy | Spike Seg Type Fscore Increase in entropy | Spike Seg Boundary Fscore Increase in entropy | Absolute Seg Type Fscore Increase in entropy | Absolute Seg Boundary Fscore Increase in entropy | Spike Seg Type Fscore Loss | Spike Seg Boundary Fscore Loss | Absolute Seg Type Fscore Loss | Absolute Seg Boundary Fscore Loss | Spike Seg Type Fscore Increase in loss | Spike Seg Boundary Fscore Increase in loss | Absolute Seg Type Fscore Increase in loss | Absolute Seg Boundary Fscore Increase in loss | Spike Seg Type Fscore Rank | Spike Seg Boundary Fscore Rank | Absolute Seg Type Fscore Rank | Absolute Seg Boundary Fscore Rank | Spike Seg Type Fscore Increase in rank | Spike Seg Boundary Fscore Increase in rank | Absolute Seg Type Fscore Increase in rank | Absolute Seg Boundary Fscore Increase in rank | Spike Seg Type Fscore Boundary prediction | Spike Seg Boundary Fscore Boundary prediction | Absolute Seg Type Fscore Boundary prediction | Absolute Seg Boundary Fscore Boundary prediction | Spike Seg Type Fscore Increase in boundary prediction | Spike Seg Boundary Fscore Increase in boundary prediction | Absolute Seg Type Fscore Increase in boundary prediction | Absolute Seg Boundary Fscore Increase in boundary prediction | Spike Seg Type Fscore Majority vote cutoff | Spike Seg Type Fscore Majority vote spike | Absolute Seg Type Fscore Majority vote cutoff | Absolute Seg Type Fscore Majority vote spike | Spike Seg Boundary Fscore Majority vote cutoff | Spike Seg Boundary Fscore Majority vote spike | Absolute Seg Boundary Fscore Majority vote cutoff | Absolute Seg Boundary Fscore Majority vote spike |
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+ |:-------------:|:-------:|:------:|:---------------:|:----------------------:|:----------:|:------:|:-----------------------------:|:---------------------------------:|:--------------------------------:|:------------------------------------:|:-----------------------------------------:|:---------------------------------------------:|:--------------------------------------------:|:------------------------------------------------:|:--------------------------:|:------------------------------:|:-----------------------------:|:---------------------------------:|:--------------------------------------:|:------------------------------------------:|:-----------------------------------------:|:---------------------------------------------:|:--------------------------:|:------------------------------:|:-----------------------------:|:---------------------------------:|:--------------------------------------:|:------------------------------------------:|:-----------------------------------------:|:---------------------------------------------:|:-----------------------------------------:|:---------------------------------------------:|:--------------------------------------------:|:------------------------------------------------:|:-----------------------------------------------------:|:---------------------------------------------------------:|:--------------------------------------------------------:|:------------------------------------------------------------:|:------------------------------------------:|:-----------------------------------------:|:---------------------------------------------:|:--------------------------------------------:|:----------------------------------------------:|:---------------------------------------------:|:-------------------------------------------------:|:------------------------------------------------:|
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+ | 1.418 | 4.5290 | 20000 | 1.5456 | 0.0013 | 4.6908 | 2.2298 | 0.5202 | 0.7537 | 0.3779 | 0.6326 | 0.4886 | 0.7542 | 0.5462 | 0.7705 | 0.4673 | 0.7125 | 0.1852 | 0.6119 | 0.5 | 0.7439 | 0.5140 | 0.7503 | 0.4580 | 0.6515 | 0.3252 | 0.5828 | 0.4965 | 0.6950 | 0.5032 | 0.6947 | 0.5137 | 0.7850 | 0.3688 | 0.5036 | 0.4720 | 0.7564 | 0.2699 | 0.7468 | 0.6117 | 0.4695 | 0.4865 | 0.5951 | 0.8190 | 0.7707 | 0.7754 | 0.8128 |
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+ | 1.3419 | 9.0580 | 40000 | 1.5062 | 0.0013 | 4.5097 | 2.1730 | 0.5334 | 0.7731 | 0.4017 | 0.6446 | 0.4934 | 0.7641 | 0.5823 | 0.7738 | 0.4661 | 0.7199 | 0.3633 | 0.6170 | 0.5182 | 0.7655 | 0.5230 | 0.7541 | 0.4670 | 0.6554 | 0.3283 | 0.5868 | 0.5086 | 0.7047 | 0.5374 | 0.7079 | 0.5384 | 0.8 | 0.2665 | 0.7782 | 0.4865 | 0.7603 | 0.2625 | 0.7599 | 0.6162 | 0.4752 | 0.5467 | 0.6404 | 0.8207 | 0.7733 | 0.8083 | 0.8297 |
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+ | 1.2911 | 13.5870 | 60000 | 1.4740 | 0.0013 | 4.3665 | 2.1265 | 0.5431 | 0.7827 | 0.4017 | 0.6226 | 0.5042 | 0.7663 | 0.5776 | 0.7816 | 0.4832 | 0.7214 | 0.2106 | 0.6109 | 0.5060 | 0.7533 | 0.5344 | 0.7594 | 0.4732 | 0.6519 | 0.3198 | 0.5685 | 0.4923 | 0.6900 | 0.4931 | 0.6954 | 0.5379 | 0.8083 | 0.3506 | 0.4930 | 0.5008 | 0.7768 | 0.2621 | 0.7390 | 0.6045 | 0.4492 | 0.4242 | 0.6183 | 0.8186 | 0.7659 | 0.7554 | 0.8234 |
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+ | 1.2397 | 18.1159 | 80000 | 1.4710 | 0.0013 | 4.3537 | 2.1222 | 0.5355 | 0.7742 | 0.4044 | 0.6203 | 0.5169 | 0.7687 | 0.5692 | 0.7722 | 0.4724 | 0.7140 | 0.3523 | 0.6225 | 0.5088 | 0.7554 | 0.5271 | 0.7526 | 0.4918 | 0.6667 | 0.3442 | 0.5695 | 0.4949 | 0.6899 | 0.5318 | 0.7059 | 0.5409 | 0.8024 | 0.2643 | 0.785 | 0.5060 | 0.7725 | 0.2590 | 0.7676 | 0.6034 | 0.4954 | 0.5495 | 0.6285 | 0.8290 | 0.7749 | 0.8150 | 0.8230 |
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+ | 1.1906 | 22.6449 | 100000 | 1.4768 | 0.0013 | 4.3788 | 2.1305 | 0.5342 | 0.7807 | 0.4052 | 0.6284 | 0.5238 | 0.7770 | 0.5770 | 0.7649 | 0.4817 | 0.7269 | 0.3506 | 0.6181 | 0.5196 | 0.7627 | 0.5321 | 0.7583 | 0.4850 | 0.6691 | 0.3317 | 0.5690 | 0.5012 | 0.6983 | 0.4975 | 0.7142 | 0.5420 | 0.8090 | 0.2637 | 0.7085 | 0.5230 | 0.7840 | 0.2821 | 0.4171 | 0.6129 | 0.4882 | 0.5175 | 0.6171 | 0.8043 | 0.7814 | 0.7775 | 0.8289 |
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+ | 1.1539 | 27.1739 | 120000 | 1.4986 | 0.0013 | 4.4756 | 2.1621 | 0.5355 | 0.7782 | 0.4135 | 0.6490 | 0.5242 | 0.7819 | 0.5790 | 0.7795 | 0.4570 | 0.7061 | 0.3286 | 0.6123 | 0.4988 | 0.7528 | 0.5187 | 0.7281 | 0.4779 | 0.6674 | 0.3452 | 0.5604 | 0.4854 | 0.6910 | 0.5449 | 0.7106 | 0.5502 | 0.8088 | 0.2884 | 0.8028 | 0.5251 | 0.7881 | 0.3504 | 0.7872 | 0.6119 | 0.4789 | 0.5543 | 0.6131 | 0.8316 | 0.7727 | 0.7959 | 0.8165 |
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+ | 1.1198 | 31.7029 | 140000 | 1.4979 | 0.0013 | 4.4723 | 2.1610 | 0.5628 | 0.7849 | 0.4080 | 0.5883 | 0.5267 | 0.7764 | 0.5820 | 0.7557 | 0.4490 | 0.6987 | 0.3389 | 0.6187 | 0.4901 | 0.7447 | 0.5149 | 0.7496 | 0.4686 | 0.6553 | 0.3383 | 0.5647 | 0.5059 | 0.6940 | 0.5319 | 0.7036 | 0.5503 | 0.8056 | 0.2686 | 0.7966 | 0.5293 | 0.7900 | 0.2607 | 0.7840 | 0.6003 | 0.4854 | 0.5448 | 0.6101 | 0.8329 | 0.7729 | 0.8068 | 0.8146 |
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+ | 1.0878 | 36.2319 | 160000 | 1.5223 | 0.0013 | 4.5827 | 2.1962 | 0.5553 | 0.7755 | 0.4237 | 0.6483 | 0.5196 | 0.7746 | 0.5848 | 0.7763 | 0.4497 | 0.6927 | 0.3273 | 0.6138 | 0.4858 | 0.7384 | 0.5113 | 0.7470 | 0.4716 | 0.6550 | 0.3289 | 0.5669 | 0.5098 | 0.69 | 0.5040 | 0.6965 | 0.5400 | 0.8044 | 0.3216 | 0.7546 | 0.5179 | 0.7898 | 0.5233 | 0.7859 | 0.6214 | 0.4608 | 0.5760 | 0.6141 | 0.8290 | 0.7650 | 0.8015 | 0.8115 |
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+ | 1.0617 | 40.7609 | 180000 | 1.5411 | 0.0013 | 4.6699 | 2.2234 | 0.5562 | 0.7730 | 0.4066 | 0.6411 | 0.5280 | 0.7766 | 0.5836 | 0.7781 | 0.4479 | 0.6957 | 0.3336 | 0.6154 | 0.4893 | 0.7420 | 0.4984 | 0.7377 | 0.4808 | 0.6601 | 0.3386 | 0.5917 | 0.4836 | 0.6912 | 0.4857 | 0.7079 | 0.5423 | 0.8068 | 0.3296 | 0.7652 | 0.5232 | 0.7876 | 0.5623 | 0.4156 | 0.6383 | 0.4685 | 0.5665 | 0.6055 | 0.8162 | 0.7709 | 0.7762 | 0.8144 |
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+ | 1.0394 | 45.2899 | 200000 | 1.5598 | 0.0013 | 4.7580 | 2.2503 | 0.5424 | 0.7652 | 0.4188 | 0.6411 | 0.5339 | 0.7796 | 0.5744 | 0.7708 | 0.4461 | 0.6948 | 0.3397 | 0.6138 | 0.5024 | 0.7430 | 0.5046 | 0.7437 | 0.4778 | 0.6585 | 0.3314 | 0.5551 | 0.4977 | 0.6963 | 0.4902 | 0.7065 | 0.5365 | 0.8041 | 0.3187 | 0.7456 | 0.5171 | 0.7895 | 0.2577 | 0.5526 | 0.6165 | 0.4770 | 0.5211 | 0.6022 | 0.8101 | 0.7717 | 0.7609 | 0.8128 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.2
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+ - Pytorch 2.4.0+cu118
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+ - Datasets 2.18.0
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+ - Tokenizers 0.19.1
generation_config.json ADDED
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+ {
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+ "_from_model_config": true,
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+ "bos_token_id": 3,
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+ "eos_token_id": 3,
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+ "transformers_version": "4.44.2"
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+ }
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