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--- |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- NbAiLab/NCC_S |
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metrics: |
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- wer |
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base_model: openai/whisper-tiny |
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model-index: |
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- name: Whisper Tiny Norwegian Bokmål |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: NbAiLab/NCC_S |
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type: NbAiLab/NCC_S |
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config: 'no' |
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split: validation |
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args: 'no' |
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metrics: |
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- type: wer |
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value: 24.878197320341048 |
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name: Wer |
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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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# Whisper Tiny Norwegian Bokmål |
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the NbAiLab/NCC_S dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5100 |
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- Wer: 24.8782 |
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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: 3e-06 |
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- train_batch_size: 256 |
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- eval_batch_size: 64 |
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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: constant_with_warmup |
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- lr_scheduler_warmup_steps: 1000 |
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- training_steps: 100000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:------:|:---------------:|:-------:| |
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| 1.8819 | 0.01 | 1000 | 1.1869 | 61.9671 | |
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| 1.6425 | 0.02 | 2000 | 0.9991 | 53.6541 | |
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| 1.548 | 0.03 | 3000 | 0.9147 | 50.2132 | |
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| 1.4636 | 0.04 | 4000 | 0.8605 | 47.0767 | |
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| 1.4113 | 0.05 | 5000 | 0.8253 | 45.7369 | |
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| 1.3484 | 0.01 | 6000 | 0.7946 | 43.4531 | |
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| 1.3127 | 0.02 | 7000 | 0.7740 | 42.2655 | |
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| 1.2994 | 0.03 | 8000 | 0.7551 | 40.8952 | |
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| 1.265 | 0.04 | 9000 | 0.7378 | 39.8599 | |
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| 1.2458 | 0.05 | 10000 | 0.7257 | 39.8904 | |
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| 1.2257 | 0.06 | 11000 | 0.7114 | 39.7990 | |
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| 1.2126 | 0.07 | 12000 | 0.6972 | 37.8806 | |
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| 1.1971 | 0.08 | 13000 | 0.6871 | 37.3021 | |
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| 1.1786 | 1.01 | 14000 | 0.6786 | 37.4239 | |
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| 1.1486 | 1.02 | 15000 | 0.6703 | 36.9976 | |
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| 1.1505 | 1.03 | 16000 | 0.6647 | 36.3581 | |
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| 1.1238 | 1.04 | 17000 | 0.6559 | 36.3886 | |
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| 1.1184 | 1.05 | 18000 | 0.6509 | 36.5104 | |
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| 1.115 | 1.06 | 19000 | 0.6452 | 35.9927 | |
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| 1.1013 | 1.07 | 20000 | 0.6382 | 34.5006 | |
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| 1.0969 | 1.08 | 21000 | 0.6331 | 34.3484 | |
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| 1.0784 | 2.0 | 22000 | 0.6304 | 34.2875 | |
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| 1.0774 | 2.01 | 23000 | 0.6249 | 34.1048 | |
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| 1.0719 | 2.02 | 24000 | 0.6194 | 33.8307 | |
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| 1.0638 | 2.03 | 25000 | 0.6158 | 32.9781 | |
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| 1.0592 | 2.04 | 26000 | 0.6105 | 32.6431 | |
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| 1.0493 | 2.05 | 27000 | 0.6041 | 32.7345 | |
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| 1.047 | 2.06 | 28000 | 0.6040 | 32.7649 | |
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| 1.0323 | 2.07 | 29000 | 0.5984 | 31.6078 | |
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| 1.0189 | 3.0 | 30000 | 0.5957 | 31.3033 | |
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| 1.0078 | 3.01 | 31000 | 0.5924 | 31.4251 | |
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| 1.0146 | 3.02 | 32000 | 0.5940 | 31.3033 | |
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| 1.0128 | 3.03 | 33000 | 0.5892 | 31.0292 | |
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| 1.0025 | 3.04 | 34000 | 0.5873 | 31.1815 | |
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| 0.999 | 3.05 | 35000 | 0.5838 | 30.6334 | |
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| 1.0045 | 3.06 | 36000 | 0.5799 | 30.4202 | |
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| 1.0005 | 3.07 | 37000 | 0.5770 | 30.1766 | |
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| 1.0017 | 3.08 | 38000 | 0.5733 | 29.6590 | |
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| 0.9878 | 4.01 | 39000 | 0.5745 | 30.2680 | |
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| 0.9854 | 4.02 | 40000 | 0.5720 | 30.0548 | |
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| 0.9624 | 4.03 | 41000 | 0.5703 | 29.5981 | |
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| 0.9639 | 4.04 | 42000 | 0.5681 | 29.5067 | |
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| 0.9569 | 4.05 | 43000 | 0.5679 | 29.6285 | |
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| 0.9682 | 4.06 | 44000 | 0.5643 | 29.5676 | |
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| 0.9539 | 4.07 | 45000 | 0.5601 | 29.5676 | |
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| 0.946 | 4.08 | 46000 | 0.5562 | 29.7199 | |
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| 0.9429 | 5.01 | 47000 | 0.5592 | 29.2935 | |
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| 0.9462 | 5.02 | 48000 | 0.5540 | 29.0804 | |
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| 0.9312 | 5.03 | 49000 | 0.5535 | 29.2935 | |
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| 0.9462 | 5.04 | 50000 | 0.5536 | 28.6845 | |
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| 0.922 | 5.05 | 51000 | 0.5539 | 28.7150 | |
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| 0.9253 | 5.06 | 52000 | 0.5510 | 28.8368 | |
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| 0.9065 | 0.01 | 53000 | 0.5493 | 28.5932 | |
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| 0.9096 | 0.02 | 54000 | 0.5490 | 28.5018 | |
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| 0.9329 | 0.03 | 55000 | 0.5483 | 28.2887 | |
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| 0.9181 | 0.04 | 56000 | 0.5471 | 27.9842 | |
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| 0.914 | 0.05 | 57000 | 0.5457 | 28.4105 | |
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| 0.9149 | 0.06 | 58000 | 0.5449 | 27.5883 | |
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| 0.9092 | 0.07 | 59000 | 0.5405 | 27.8319 | |
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| 0.9101 | 0.08 | 60000 | 0.5402 | 27.3447 | |
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| 0.9046 | 1.01 | 61000 | 0.5374 | 27.5579 | |
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| 0.8917 | 1.02 | 62000 | 0.5390 | 27.7406 | |
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| 0.8993 | 1.03 | 63000 | 0.5386 | 27.4056 | |
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| 0.8875 | 1.04 | 64000 | 0.5361 | 26.8575 | |
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| 0.8892 | 1.05 | 65000 | 0.5358 | 27.3447 | |
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| 0.8929 | 1.06 | 66000 | 0.5346 | 26.7357 | |
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| 0.8703 | 0.01 | 67000 | 0.5332 | 26.8270 | |
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| 0.8709 | 0.02 | 68000 | 0.5336 | 26.7052 | |
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| 0.8917 | 0.03 | 69000 | 0.5329 | 27.0706 | |
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| 0.8867 | 0.04 | 70000 | 0.5323 | 26.3398 | |
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| 0.8778 | 0.05 | 71000 | 0.5315 | 27.2838 | |
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| 0.8757 | 0.06 | 72000 | 0.5317 | 26.2485 | |
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| 0.8726 | 0.07 | 73000 | 0.5269 | 26.6443 | |
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| 0.8792 | 0.08 | 74000 | 0.5268 | 26.1571 | |
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| 0.8706 | 1.01 | 75000 | 0.5247 | 26.1571 | |
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| 0.8585 | 1.02 | 76000 | 0.5265 | 26.3703 | |
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| 0.8659 | 1.03 | 77000 | 0.5262 | 26.7357 | |
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| 0.8551 | 1.04 | 78000 | 0.5249 | 26.0658 | |
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| 0.8572 | 1.05 | 79000 | 0.5249 | 26.2789 | |
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| 0.8612 | 1.06 | 80000 | 0.5235 | 25.7613 | |
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| 0.8598 | 1.07 | 81000 | 0.5208 | 25.7004 | |
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| 0.8686 | 1.08 | 82000 | 0.5214 | 25.7004 | |
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| 0.8503 | 2.0 | 83000 | 0.5214 | 25.7004 | |
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| 0.8545 | 2.01 | 84000 | 0.5215 | 28.2278 | |
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| 0.8594 | 2.02 | 85000 | 0.5186 | 25.6699 | |
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| 0.86 | 2.03 | 86000 | 0.5196 | 25.5786 | |
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| 0.8514 | 2.04 | 87000 | 0.5203 | 25.1827 | |
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| 0.8505 | 2.05 | 88000 | 0.5164 | 28.0146 | |
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| 0.8512 | 2.06 | 89000 | 0.5174 | 25.0914 | |
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| 0.8495 | 2.07 | 90000 | 0.5141 | 25.5481 | |
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| 0.8381 | 3.0 | 91000 | 0.5130 | 24.9695 | |
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| 0.8253 | 3.01 | 92000 | 0.5147 | 25.5786 | |
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| 0.8387 | 3.02 | 93000 | 0.5168 | 24.9086 | |
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| 0.8425 | 3.03 | 94000 | 0.5135 | 25.2436 | |
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| 0.8339 | 3.04 | 95000 | 0.5162 | 25.6699 | |
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| 0.8402 | 3.05 | 96000 | 0.5147 | 25.7308 | |
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| 0.8396 | 3.06 | 97000 | 0.5143 | 25.6699 | |
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| 0.8432 | 3.07 | 98000 | 0.5100 | 24.8782 | |
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| 0.844 | 3.08 | 99000 | 0.5100 | 25.0609 | |
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| 0.8333 | 4.01 | 100000 | 0.5128 | 24.9695 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1.dev0 |
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- Tokenizers 0.13.2 |
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