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childes-segmentation-100k-gpt2_lm-model

Text Generation
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gpt2
Dutch
Generated from Trainer
text-generation-inference
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codebyzeb commited on
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Final model for experiment Farsi

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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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+ - Farsi
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+ - generated_from_trainer
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+ model-index:
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+ - name: childes-segmentation-100k-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-100k-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: 2.5822
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+ - Model Preparation Time: 0.0008
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+ - Perplexity: 13.2257
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+ - Bpc: 3.7253
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+ - Spike Seg Type Fscore Entropy: 0.2147
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+ - Spike Seg Boundary Fscore Entropy: 0.3699
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+ - Absolute Seg Type Fscore Entropy: 0.2664
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+ - Absolute Seg Boundary Fscore Entropy: 0.3205
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+ - Spike Seg Type Fscore Increase in entropy: 0.2026
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+ - Spike Seg Boundary Fscore Increase in entropy: 0.3669
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+ - Absolute Seg Type Fscore Increase in entropy: 0.3012
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+ - Absolute Seg Boundary Fscore Increase in entropy: 0.3784
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+ - Spike Seg Type Fscore Loss: 0.2730
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+ - Spike Seg Boundary Fscore Loss: 0.4662
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+ - Absolute Seg Type Fscore Loss: 0.3005
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+ - Absolute Seg Boundary Fscore Loss: 0.4539
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+ - Spike Seg Type Fscore Increase in loss: 0.2485
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+ - Spike Seg Boundary Fscore Increase in loss: 0.4556
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+ - Absolute Seg Type Fscore Increase in loss: 0.3506
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+ - Absolute Seg Boundary Fscore Increase in loss: 0.4909
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+ - Spike Seg Type Fscore Rank: 0.3340
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+ - Spike Seg Boundary Fscore Rank: 0.5171
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+ - Absolute Seg Type Fscore Rank: 0.3037
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+ - Absolute Seg Boundary Fscore Rank: 0.4734
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+ - Spike Seg Type Fscore Increase in rank: 0.2880
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+ - Spike Seg Boundary Fscore Increase in rank: 0.4970
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+ - Absolute Seg Type Fscore Increase in rank: 0.3533
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+ - Absolute Seg Boundary Fscore Increase in rank: 0.5186
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+ - Spike Seg Type Fscore Boundary prediction: 0.2822
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+ - Spike Seg Boundary Fscore Boundary prediction: 0.4492
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+ - Absolute Seg Type Fscore Boundary prediction: 0.3900
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+ - Absolute Seg Boundary Fscore Boundary prediction: 0.4825
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+ - Spike Seg Type Fscore Increase in boundary prediction: 0.2559
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+ - Spike Seg Boundary Fscore Increase in boundary prediction: 0.4277
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+ - Absolute Seg Type Fscore Increase in boundary prediction: 0.3831
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+ - Absolute Seg Boundary Fscore Increase in boundary prediction: 0.5119
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+ - Spike Seg Type Fscore Majority vote cutoff: 0.3535
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+ - Spike Seg Type Fscore Majority vote spike: 0.2767
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+ - Absolute Seg Type Fscore Majority vote cutoff: 0.3948
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+ - Absolute Seg Type Fscore Majority vote spike: 0.3462
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+ - Spike Seg Boundary Fscore Majority vote cutoff: 0.5237
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+ - Spike Seg Boundary Fscore Majority vote spike: 0.4786
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+ - Absolute Seg Boundary Fscore Majority vote cutoff: 0.5578
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+ - Absolute Seg Boundary Fscore Majority vote spike: 0.5272
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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: 30000
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+ - training_steps: 100000
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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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+ | 1.6826 | 384.6154 | 10000 | 1.7302 | 0.0008 | 5.6416 | 2.4961 | 0.2332 | 0.4283 | 0.294 | 0.3923 | 0.1835 | 0.4010 | 0.3243 | 0.4165 | 0.2734 | 0.4959 | 0.3570 | 0.4712 | 0.2582 | 0.4915 | 0.3736 | 0.5280 | 0.3512 | 0.5176 | 0.3576 | 0.4885 | 0.2703 | 0.4800 | 0.4004 | 0.4945 | 0.2874 | 0.4678 | 0.3595 | 0.4466 | 0.2533 | 0.4417 | 0.3677 | 0.4670 | 0.3824 | 0.2551 | 0.3844 | 0.3634 | 0.5378 | 0.4915 | 0.5556 | 0.5485 |
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+ | 1.2519 | 769.2308 | 20000 | 1.9310 | 0.0008 | 6.8966 | 2.7859 | 0.2336 | 0.4211 | 0.2940 | 0.3732 | 0.2122 | 0.4251 | 0.2895 | 0.4324 | 0.2639 | 0.4672 | 0.2822 | 0.4313 | 0.2561 | 0.4750 | 0.3049 | 0.4958 | 0.3398 | 0.5081 | 0.3398 | 0.4833 | 0.2744 | 0.4846 | 0.3640 | 0.4978 | 0.2805 | 0.4713 | 0.4 | 0.5025 | 0.2554 | 0.4591 | 0.3921 | 0.4812 | 0.3480 | 0.2516 | 0.3945 | 0.3533 | 0.5479 | 0.4874 | 0.5636 | 0.5456 |
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+ | 1.103 | 1153.8462 | 30000 | 2.1048 | 0.0008 | 8.2053 | 3.0366 | 0.2435 | 0.4124 | 0.2877 | 0.3427 | 0.2166 | 0.4002 | 0.3004 | 0.4311 | 0.2608 | 0.4719 | 0.3249 | 0.4287 | 0.2513 | 0.4762 | 0.3092 | 0.4925 | 0.3202 | 0.5149 | 0.3899 | 0.4426 | 0.2950 | 0.4961 | 0.3649 | 0.5244 | 0.2719 | 0.4621 | 0.3801 | 0.5106 | 0.2507 | 0.4447 | 0.3962 | 0.5509 | 0.3538 | 0.2866 | 0.3761 | 0.3337 | 0.5365 | 0.4971 | 0.5496 | 0.5406 |
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+ | 0.9883 | 1538.4615 | 40000 | 2.2380 | 0.0008 | 9.3742 | 3.2287 | 0.2224 | 0.3862 | 0.2940 | 0.3445 | 0.2167 | 0.3904 | 0.2743 | 0.4030 | 0.2780 | 0.4676 | 0.3298 | 0.4298 | 0.2465 | 0.4583 | 0.3443 | 0.4889 | 0.3291 | 0.5216 | 0.3575 | 0.4506 | 0.2717 | 0.4902 | 0.3488 | 0.5229 | 0.2822 | 0.4524 | 0.3686 | 0.4866 | 0.2621 | 0.4354 | 0.3662 | 0.4680 | 0.3406 | 0.2749 | 0.3548 | 0.3527 | 0.5290 | 0.4790 | 0.5251 | 0.5324 |
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+ | 0.9244 | 1923.0769 | 50000 | 2.3482 | 0.0008 | 10.4671 | 3.3878 | 0.2264 | 0.3775 | 0.2530 | 0.3560 | 0.2108 | 0.3881 | 0.2659 | 0.4046 | 0.2613 | 0.4648 | 0.3373 | 0.4432 | 0.252 | 0.4637 | 0.3496 | 0.4915 | 0.3295 | 0.5205 | 0.3060 | 0.4438 | 0.2815 | 0.4893 | 0.3608 | 0.5148 | 0.2760 | 0.4474 | 0.3833 | 0.4605 | 0.2521 | 0.4380 | 0.3862 | 0.4467 | 0.3386 | 0.2571 | 0.3885 | 0.3671 | 0.5364 | 0.4747 | 0.5370 | 0.5416 |
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+ | 0.8837 | 2307.6923 | 60000 | 2.4229 | 0.0008 | 11.2783 | 3.4955 | 0.2284 | 0.3627 | 0.2575 | 0.3255 | 0.2032 | 0.3790 | 0.2890 | 0.3915 | 0.2731 | 0.4666 | 0.2921 | 0.4476 | 0.2455 | 0.4535 | 0.3594 | 0.4932 | 0.3410 | 0.5251 | 0.3104 | 0.4766 | 0.2789 | 0.4848 | 0.3421 | 0.5058 | 0.2854 | 0.4603 | 0.3971 | 0.4923 | 0.2636 | 0.4444 | 0.4021 | 0.4650 | 0.3515 | 0.2529 | 0.3790 | 0.3591 | 0.5312 | 0.4750 | 0.5330 | 0.5336 |
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+ | 0.8519 | 2692.3077 | 70000 | 2.4612 | 0.0008 | 11.7190 | 3.5508 | 0.2246 | 0.3689 | 0.2718 | 0.3261 | 0.2092 | 0.3721 | 0.2849 | 0.3755 | 0.2749 | 0.4653 | 0.3509 | 0.4594 | 0.2534 | 0.4558 | 0.3469 | 0.4849 | 0.3189 | 0.5143 | 0.3079 | 0.4711 | 0.2951 | 0.5 | 0.3635 | 0.5247 | 0.2971 | 0.4582 | 0.3996 | 0.4986 | 0.2662 | 0.4419 | 0.4049 | 0.5126 | 0.3244 | 0.2640 | 0.3818 | 0.3604 | 0.5321 | 0.4809 | 0.5681 | 0.5347 |
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+ | 0.8278 | 3076.9231 | 80000 | 2.5177 | 0.0008 | 12.4001 | 3.6323 | 0.2254 | 0.3696 | 0.2586 | 0.3270 | 0.2085 | 0.3773 | 0.2848 | 0.3861 | 0.2833 | 0.4766 | 0.3310 | 0.4503 | 0.2429 | 0.4492 | 0.3580 | 0.4916 | 0.3314 | 0.5184 | 0.3237 | 0.4797 | 0.2823 | 0.4935 | 0.3527 | 0.5269 | 0.2782 | 0.4476 | 0.4016 | 0.5046 | 0.2565 | 0.4330 | 0.4065 | 0.4622 | 0.3471 | 0.2689 | 0.3861 | 0.3340 | 0.5368 | 0.4792 | 0.5540 | 0.5321 |
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+ | 0.8061 | 3461.5385 | 90000 | 2.5572 | 0.0008 | 12.8992 | 3.6892 | 0.2118 | 0.3622 | 0.2697 | 0.3221 | 0.2004 | 0.3627 | 0.2740 | 0.3728 | 0.2806 | 0.4686 | 0.3246 | 0.4505 | 0.2568 | 0.4491 | 0.3599 | 0.4903 | 0.3190 | 0.5155 | 0.3007 | 0.4738 | 0.2964 | 0.4979 | 0.3523 | 0.5199 | 0.2830 | 0.4449 | 0.3893 | 0.4766 | 0.2533 | 0.4277 | 0.3793 | 0.4798 | 0.3538 | 0.2623 | 0.3920 | 0.3604 | 0.5315 | 0.4729 | 0.5392 | 0.5293 |
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+ | 0.7891 | 3846.1538 | 100000 | 2.5822 | 0.0008 | 13.2257 | 3.7253 | 0.2147 | 0.3699 | 0.2664 | 0.3205 | 0.2026 | 0.3669 | 0.3012 | 0.3784 | 0.2730 | 0.4662 | 0.3005 | 0.4539 | 0.2485 | 0.4556 | 0.3506 | 0.4909 | 0.3340 | 0.5171 | 0.3037 | 0.4734 | 0.2880 | 0.4970 | 0.3533 | 0.5186 | 0.2822 | 0.4492 | 0.3900 | 0.4825 | 0.2559 | 0.4277 | 0.3831 | 0.5119 | 0.3535 | 0.2767 | 0.3948 | 0.3462 | 0.5237 | 0.4786 | 0.5578 | 0.5272 |
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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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