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childes-segmentation-800k-2-gpt2_lm-model

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

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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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+ - Indonesian
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+ - generated_from_trainer
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+ model-index:
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+ - name: childes-segmentation-800k-2-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-800k-2-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: 2.5236
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+ - Model Preparation Time: 0.0011
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+ - Perplexity: 12.4732
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+ - Bpc: 3.6408
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+ - Spike Seg Type Fscore Entropy: 0.2720
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+ - Spike Seg Boundary Fscore Entropy: 0.5249
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+ - Absolute Seg Type Fscore Entropy: 0.3067
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+ - Absolute Seg Boundary Fscore Entropy: 0.5137
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+ - Spike Seg Type Fscore Increase in entropy: 0.2334
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+ - Spike Seg Boundary Fscore Increase in entropy: 0.5095
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+ - Absolute Seg Type Fscore Increase in entropy: 0.3527
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+ - Absolute Seg Boundary Fscore Increase in entropy: 0.5784
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+ - Spike Seg Type Fscore Loss: 0.2372
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+ - Spike Seg Boundary Fscore Loss: 0.5197
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+ - Absolute Seg Type Fscore Loss: 0.2599
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+ - Absolute Seg Boundary Fscore Loss: 0.4807
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+ - Spike Seg Type Fscore Increase in loss: 0.2657
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+ - Spike Seg Boundary Fscore Increase in loss: 0.5594
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+ - Absolute Seg Type Fscore Increase in loss: 0.3442
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+ - Absolute Seg Boundary Fscore Increase in loss: 0.6019
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+ - Spike Seg Type Fscore Rank: 0.3213
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+ - Spike Seg Boundary Fscore Rank: 0.5976
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+ - Absolute Seg Type Fscore Rank: 0.3376
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+ - Absolute Seg Boundary Fscore Rank: 0.5588
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+ - Spike Seg Type Fscore Increase in rank: 0.3031
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+ - Spike Seg Boundary Fscore Increase in rank: 0.5969
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+ - Absolute Seg Type Fscore Increase in rank: 0.3999
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+ - Absolute Seg Boundary Fscore Increase in rank: 0.6393
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+ - Spike Seg Type Fscore Boundary prediction: 0.2994
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+ - Spike Seg Boundary Fscore Boundary prediction: 0.5846
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+ - Absolute Seg Type Fscore Boundary prediction: 0.4113
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+ - Absolute Seg Boundary Fscore Boundary prediction: 0.6424
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+ - Spike Seg Type Fscore Increase in boundary prediction: 0.2702
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+ - Spike Seg Boundary Fscore Increase in boundary prediction: 0.5732
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+ - Absolute Seg Type Fscore Increase in boundary prediction: 0.4120
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+ - Absolute Seg Boundary Fscore Increase in boundary prediction: 0.6470
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+ - Spike Seg Type Fscore Majority vote cutoff: 0.3837
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+ - Spike Seg Type Fscore Majority vote spike: 0.2849
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+ - Absolute Seg Type Fscore Majority vote cutoff: 0.4198
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+ - Absolute Seg Type Fscore Majority vote spike: 0.3837
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+ - Spike Seg Boundary Fscore Majority vote cutoff: 0.6500
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+ - Spike Seg Boundary Fscore Majority vote spike: 0.5765
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+ - Absolute Seg Boundary Fscore Majority vote cutoff: 0.6828
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+ - Absolute Seg Boundary Fscore Majority vote spike: 0.6410
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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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+ | 1.7197 | 444.4444 | 20000 | 1.9075 | 0.0011 | 6.7365 | 2.7520 | 0.2683 | 0.5620 | 0.3317 | 0.5663 | 0.2081 | 0.5293 | 0.3641 | 0.6164 | 0.2522 | 0.5532 | 0.2744 | 0.5385 | 0.2585 | 0.5796 | 0.3695 | 0.6574 | 0.3268 | 0.6021 | 0.3337 | 0.5460 | 0.3000 | 0.6061 | 0.3887 | 0.6477 | 0.2885 | 0.5815 | 0.4086 | 0.6469 | 0.2565 | 0.5752 | 0.4113 | 0.6630 | 0.3696 | 0.2869 | 0.4734 | 0.3821 | 0.6641 | 0.5879 | 0.7247 | 0.6540 |
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+ | 1.4275 | 888.8889 | 40000 | 2.0272 | 0.0011 | 7.5925 | 2.9246 | 0.2958 | 0.5692 | 0.3512 | 0.5630 | 0.2311 | 0.5243 | 0.3676 | 0.6254 | 0.2496 | 0.5471 | 0.2596 | 0.5115 | 0.2698 | 0.5714 | 0.3546 | 0.6353 | 0.3286 | 0.6099 | 0.3399 | 0.5494 | 0.3051 | 0.6075 | 0.3992 | 0.6497 | 0.3167 | 0.6084 | 0.4629 | 0.6847 | 0.2851 | 0.5937 | 0.4638 | 0.6945 | 0.3963 | 0.2878 | 0.4904 | 0.4059 | 0.6714 | 0.5854 | 0.7403 | 0.6596 |
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+ | 1.2989 | 1333.3333 | 60000 | 2.1440 | 0.0011 | 8.5336 | 3.0931 | 0.2823 | 0.5497 | 0.3502 | 0.5655 | 0.2245 | 0.5209 | 0.3755 | 0.6069 | 0.2482 | 0.5336 | 0.2610 | 0.5062 | 0.2625 | 0.5636 | 0.3430 | 0.6245 | 0.3227 | 0.5978 | 0.3427 | 0.5468 | 0.2970 | 0.6023 | 0.4121 | 0.6463 | 0.3117 | 0.6013 | 0.4146 | 0.6640 | 0.2781 | 0.5834 | 0.4440 | 0.6717 | 0.3872 | 0.2834 | 0.4737 | 0.3979 | 0.6574 | 0.5806 | 0.7210 | 0.6498 |
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+ | 1.2001 | 1777.7778 | 80000 | 2.2706 | 0.0011 | 9.6851 | 3.2758 | 0.2827 | 0.5480 | 0.3294 | 0.5439 | 0.2265 | 0.5181 | 0.3592 | 0.5955 | 0.2385 | 0.5355 | 0.2670 | 0.5029 | 0.2589 | 0.5681 | 0.3275 | 0.6169 | 0.3182 | 0.6100 | 0.3466 | 0.5502 | 0.3004 | 0.6036 | 0.4125 | 0.6557 | 0.3106 | 0.5923 | 0.4384 | 0.6630 | 0.2796 | 0.5786 | 0.3400 | 0.6535 | 0.3716 | 0.2902 | 0.4560 | 0.3899 | 0.6560 | 0.5856 | 0.7129 | 0.6513 |
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+ | 1.1469 | 2222.2222 | 100000 | 2.3491 | 0.0011 | 10.4761 | 3.3890 | 0.2766 | 0.5404 | 0.3309 | 0.5306 | 0.2338 | 0.5191 | 0.3542 | 0.5908 | 0.2476 | 0.5248 | 0.2602 | 0.4956 | 0.2616 | 0.5623 | 0.3363 | 0.6133 | 0.3159 | 0.5971 | 0.3453 | 0.5575 | 0.2921 | 0.5953 | 0.4083 | 0.6405 | 0.3016 | 0.5939 | 0.4286 | 0.6515 | 0.2728 | 0.5799 | 0.3306 | 0.6476 | 0.3920 | 0.2746 | 0.4163 | 0.3839 | 0.6559 | 0.5807 | 0.6921 | 0.6475 |
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+ | 1.1124 | 2666.6667 | 120000 | 2.3954 | 0.0011 | 10.9721 | 3.4558 | 0.2774 | 0.5381 | 0.3320 | 0.5230 | 0.2303 | 0.5161 | 0.3565 | 0.5822 | 0.2420 | 0.5224 | 0.2589 | 0.4934 | 0.2661 | 0.5608 | 0.3391 | 0.6065 | 0.3206 | 0.6014 | 0.3434 | 0.5671 | 0.2940 | 0.5970 | 0.3977 | 0.6409 | 0.3057 | 0.5885 | 0.4027 | 0.6443 | 0.2819 | 0.5772 | 0.4060 | 0.6623 | 0.3891 | 0.2762 | 0.4236 | 0.3822 | 0.6517 | 0.5775 | 0.7032 | 0.6450 |
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+ | 1.0861 | 3111.1111 | 140000 | 2.4356 | 0.0011 | 11.4232 | 3.5139 | 0.2731 | 0.5321 | 0.3266 | 0.5177 | 0.2283 | 0.5132 | 0.3649 | 0.5778 | 0.2430 | 0.5250 | 0.2535 | 0.4892 | 0.2610 | 0.5622 | 0.3313 | 0.6081 | 0.3187 | 0.6020 | 0.3428 | 0.5572 | 0.2963 | 0.5959 | 0.3981 | 0.6405 | 0.3017 | 0.5894 | 0.4174 | 0.6293 | 0.2754 | 0.5781 | 0.3311 | 0.6359 | 0.3927 | 0.2789 | 0.3956 | 0.3903 | 0.6519 | 0.5799 | 0.6775 | 0.6482 |
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+ | 1.0633 | 3555.5556 | 160000 | 2.4701 | 0.0011 | 11.8237 | 3.5636 | 0.2788 | 0.5353 | 0.3296 | 0.5187 | 0.2316 | 0.5089 | 0.3548 | 0.5789 | 0.2394 | 0.5231 | 0.2539 | 0.4849 | 0.2578 | 0.5565 | 0.3363 | 0.6038 | 0.3161 | 0.6005 | 0.3352 | 0.5565 | 0.2916 | 0.5976 | 0.4039 | 0.6434 | 0.3028 | 0.5876 | 0.4122 | 0.6375 | 0.2731 | 0.5748 | 0.4164 | 0.6406 | 0.3948 | 0.2797 | 0.4242 | 0.3852 | 0.6582 | 0.5764 | 0.6734 | 0.6445 |
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+ | 1.046 | 4000.0 | 180000 | 2.5000 | 0.0011 | 12.1827 | 3.6068 | 0.2692 | 0.5290 | 0.3200 | 0.5132 | 0.2333 | 0.5119 | 0.3553 | 0.5719 | 0.2366 | 0.5187 | 0.2601 | 0.4830 | 0.2628 | 0.5595 | 0.3329 | 0.6046 | 0.3169 | 0.5970 | 0.3384 | 0.5570 | 0.2982 | 0.5946 | 0.4074 | 0.6342 | 0.2931 | 0.5837 | 0.3996 | 0.6395 | 0.2700 | 0.5735 | 0.4142 | 0.6164 | 0.3791 | 0.2828 | 0.4222 | 0.3878 | 0.6558 | 0.5784 | 0.6742 | 0.6445 |
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+ | 1.031 | 4444.4444 | 200000 | 2.5236 | 0.0011 | 12.4732 | 3.6408 | 0.2720 | 0.5249 | 0.3067 | 0.5137 | 0.2334 | 0.5095 | 0.3527 | 0.5784 | 0.2372 | 0.5197 | 0.2599 | 0.4807 | 0.2657 | 0.5594 | 0.3442 | 0.6019 | 0.3213 | 0.5976 | 0.3376 | 0.5588 | 0.3031 | 0.5969 | 0.3999 | 0.6393 | 0.2994 | 0.5846 | 0.4113 | 0.6424 | 0.2702 | 0.5732 | 0.4120 | 0.6470 | 0.3837 | 0.2849 | 0.4198 | 0.3837 | 0.6500 | 0.5765 | 0.6828 | 0.6410 |
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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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