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--- |
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license: mit |
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base_model: haryoaw/scenario-MDBT-TCR_data-en-massive_all_1_1 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- massive |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: scenario-KD-PR-MSV-EN-EN-D2_data-en-massive_all_1_166 |
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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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# scenario-KD-PR-MSV-EN-EN-D2_data-en-massive_all_1_166 |
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This model is a fine-tuned version of [haryoaw/scenario-MDBT-TCR_data-en-massive_all_1_1](https://huggingface.co/haryoaw/scenario-MDBT-TCR_data-en-massive_all_1_1) on the massive dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.4492 |
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- Accuracy: 0.3818 |
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- F1: 0.3581 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 66 |
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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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- num_epochs: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| |
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| No log | 0.28 | 100 | 3.8981 | 0.2168 | 0.1114 | |
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| No log | 0.56 | 200 | 3.5590 | 0.3252 | 0.2326 | |
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| No log | 0.83 | 300 | 3.6191 | 0.3248 | 0.2629 | |
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| No log | 1.11 | 400 | 3.5005 | 0.3542 | 0.2804 | |
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| 2.3711 | 1.39 | 500 | 3.4728 | 0.3569 | 0.2978 | |
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| 2.3711 | 1.67 | 600 | 3.4235 | 0.3693 | 0.3204 | |
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| 2.3711 | 1.94 | 700 | 3.5368 | 0.3430 | 0.3074 | |
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| 2.3711 | 2.22 | 800 | 3.5023 | 0.3635 | 0.3122 | |
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| 2.3711 | 2.5 | 900 | 3.3525 | 0.3822 | 0.3070 | |
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| 1.4454 | 2.78 | 1000 | 3.6046 | 0.3420 | 0.3023 | |
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| 1.4454 | 3.06 | 1100 | 3.4244 | 0.3775 | 0.3410 | |
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| 1.4454 | 3.33 | 1200 | 3.6073 | 0.3529 | 0.3042 | |
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| 1.4454 | 3.61 | 1300 | 3.6553 | 0.3534 | 0.3289 | |
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| 1.4454 | 3.89 | 1400 | 3.6924 | 0.3411 | 0.3067 | |
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| 1.2035 | 4.17 | 1500 | 3.4352 | 0.3786 | 0.3192 | |
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| 1.2035 | 4.44 | 1600 | 3.4389 | 0.3710 | 0.3269 | |
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| 1.2035 | 4.72 | 1700 | 3.5966 | 0.3514 | 0.3190 | |
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| 1.2035 | 5.0 | 1800 | 3.5810 | 0.3547 | 0.3168 | |
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| 1.2035 | 5.28 | 1900 | 3.3785 | 0.3915 | 0.3424 | |
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| 1.0761 | 5.56 | 2000 | 3.4477 | 0.3808 | 0.3318 | |
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| 1.0761 | 5.83 | 2100 | 3.3579 | 0.3894 | 0.3369 | |
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| 1.0761 | 6.11 | 2200 | 3.8225 | 0.3286 | 0.3007 | |
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| 1.0761 | 6.39 | 2300 | 3.6119 | 0.3583 | 0.3126 | |
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| 1.0761 | 6.67 | 2400 | 3.5649 | 0.3672 | 0.3298 | |
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| 1.0182 | 6.94 | 2500 | 3.9692 | 0.3115 | 0.3079 | |
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| 1.0182 | 7.22 | 2600 | 3.8932 | 0.3314 | 0.3159 | |
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| 1.0182 | 7.5 | 2700 | 3.7041 | 0.3464 | 0.3302 | |
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| 1.0182 | 7.78 | 2800 | 3.6965 | 0.3461 | 0.3072 | |
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| 1.0182 | 8.06 | 2900 | 3.8919 | 0.3247 | 0.3219 | |
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| 0.9596 | 8.33 | 3000 | 3.8834 | 0.3147 | 0.3119 | |
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| 0.9596 | 8.61 | 3100 | 3.6113 | 0.3597 | 0.3412 | |
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| 0.9596 | 8.89 | 3200 | 3.8647 | 0.3239 | 0.3064 | |
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| 0.9596 | 9.17 | 3300 | 3.5087 | 0.3684 | 0.3281 | |
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| 0.9596 | 9.44 | 3400 | 3.7126 | 0.3427 | 0.3211 | |
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| 0.9317 | 9.72 | 3500 | 3.5315 | 0.3774 | 0.3439 | |
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| 0.9317 | 10.0 | 3600 | 3.7289 | 0.3479 | 0.3339 | |
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| 0.9317 | 10.28 | 3700 | 3.6699 | 0.3538 | 0.3343 | |
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| 0.9317 | 10.56 | 3800 | 3.4747 | 0.3796 | 0.3378 | |
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| 0.9317 | 10.83 | 3900 | 3.6562 | 0.3548 | 0.3186 | |
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| 0.91 | 11.11 | 4000 | 3.4031 | 0.3896 | 0.3478 | |
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| 0.91 | 11.39 | 4100 | 3.5768 | 0.3557 | 0.3296 | |
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| 0.91 | 11.67 | 4200 | 3.5617 | 0.3642 | 0.3370 | |
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| 0.91 | 11.94 | 4300 | 3.7519 | 0.3351 | 0.3163 | |
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| 0.91 | 12.22 | 4400 | 3.5107 | 0.3755 | 0.3402 | |
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| 0.892 | 12.5 | 4500 | 3.6299 | 0.3625 | 0.3322 | |
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| 0.892 | 12.78 | 4600 | 3.5303 | 0.3773 | 0.3394 | |
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| 0.892 | 13.06 | 4700 | 3.6772 | 0.3513 | 0.3279 | |
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| 0.892 | 13.33 | 4800 | 3.6287 | 0.3550 | 0.3322 | |
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| 0.892 | 13.61 | 4900 | 3.6046 | 0.3608 | 0.3363 | |
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| 0.8782 | 13.89 | 5000 | 3.5809 | 0.3728 | 0.3401 | |
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| 0.8782 | 14.17 | 5100 | 3.6615 | 0.3544 | 0.3327 | |
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| 0.8782 | 14.44 | 5200 | 3.4584 | 0.3782 | 0.3471 | |
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| 0.8782 | 14.72 | 5300 | 3.6412 | 0.3705 | 0.3456 | |
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| 0.8782 | 15.0 | 5400 | 3.6166 | 0.3647 | 0.3482 | |
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| 0.8675 | 15.28 | 5500 | 3.7989 | 0.3411 | 0.3259 | |
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| 0.8675 | 15.56 | 5600 | 3.5574 | 0.3703 | 0.3332 | |
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| 0.8675 | 15.83 | 5700 | 3.5888 | 0.3649 | 0.3332 | |
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| 0.8675 | 16.11 | 5800 | 3.3744 | 0.3900 | 0.3450 | |
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| 0.8675 | 16.39 | 5900 | 3.6122 | 0.3645 | 0.3442 | |
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| 0.862 | 16.67 | 6000 | 3.3953 | 0.3876 | 0.3457 | |
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| 0.862 | 16.94 | 6100 | 3.3995 | 0.3945 | 0.3594 | |
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| 0.862 | 17.22 | 6200 | 3.4168 | 0.3880 | 0.3463 | |
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| 0.862 | 17.5 | 6300 | 3.6119 | 0.3668 | 0.3461 | |
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| 0.862 | 17.78 | 6400 | 3.5063 | 0.3735 | 0.3325 | |
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| 0.8544 | 18.06 | 6500 | 3.6581 | 0.3539 | 0.3344 | |
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| 0.8544 | 18.33 | 6600 | 3.5380 | 0.3673 | 0.3352 | |
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| 0.8544 | 18.61 | 6700 | 3.5699 | 0.3613 | 0.3399 | |
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| 0.8544 | 18.89 | 6800 | 3.4977 | 0.3703 | 0.3441 | |
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| 0.8544 | 19.17 | 6900 | 3.5746 | 0.3664 | 0.3401 | |
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| 0.8494 | 19.44 | 7000 | 3.3279 | 0.4027 | 0.3671 | |
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| 0.8494 | 19.72 | 7100 | 3.6689 | 0.3596 | 0.3504 | |
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| 0.8494 | 20.0 | 7200 | 3.5632 | 0.3626 | 0.3439 | |
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| 0.8494 | 20.28 | 7300 | 3.5577 | 0.3693 | 0.3394 | |
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| 0.8494 | 20.56 | 7400 | 3.5795 | 0.3634 | 0.3458 | |
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| 0.8452 | 20.83 | 7500 | 3.4764 | 0.3766 | 0.3444 | |
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| 0.8452 | 21.11 | 7600 | 3.3944 | 0.3893 | 0.3569 | |
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| 0.8452 | 21.39 | 7700 | 3.4161 | 0.3913 | 0.3588 | |
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| 0.8452 | 21.67 | 7800 | 3.5015 | 0.3791 | 0.3527 | |
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| 0.8452 | 21.94 | 7900 | 3.5177 | 0.3766 | 0.3493 | |
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| 0.8413 | 22.22 | 8000 | 3.4390 | 0.3803 | 0.3547 | |
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| 0.8413 | 22.5 | 8100 | 3.4736 | 0.3765 | 0.3537 | |
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| 0.8413 | 22.78 | 8200 | 3.6093 | 0.3602 | 0.3438 | |
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| 0.8413 | 23.06 | 8300 | 3.3350 | 0.3965 | 0.3576 | |
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| 0.8413 | 23.33 | 8400 | 3.5186 | 0.3725 | 0.3478 | |
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| 0.8393 | 23.61 | 8500 | 3.4701 | 0.3836 | 0.3639 | |
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| 0.8393 | 23.89 | 8600 | 3.5562 | 0.3667 | 0.3485 | |
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| 0.8393 | 24.17 | 8700 | 3.5092 | 0.3765 | 0.3536 | |
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| 0.8393 | 24.44 | 8800 | 3.5955 | 0.3642 | 0.3451 | |
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| 0.8393 | 24.72 | 8900 | 3.5135 | 0.3728 | 0.3542 | |
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| 0.8363 | 25.0 | 9000 | 3.4253 | 0.3870 | 0.3583 | |
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| 0.8363 | 25.28 | 9100 | 3.4027 | 0.3868 | 0.3585 | |
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| 0.8363 | 25.56 | 9200 | 3.4741 | 0.3803 | 0.3568 | |
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| 0.8363 | 25.83 | 9300 | 3.4929 | 0.3790 | 0.3585 | |
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| 0.8363 | 26.11 | 9400 | 3.4803 | 0.3782 | 0.3549 | |
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| 0.8328 | 26.39 | 9500 | 3.4915 | 0.3757 | 0.3557 | |
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| 0.8328 | 26.67 | 9600 | 3.4388 | 0.3839 | 0.3603 | |
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| 0.8328 | 26.94 | 9700 | 3.5595 | 0.3679 | 0.3510 | |
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| 0.8328 | 27.22 | 9800 | 3.5496 | 0.3679 | 0.3530 | |
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| 0.8328 | 27.5 | 9900 | 3.4764 | 0.3767 | 0.3540 | |
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| 0.833 | 27.78 | 10000 | 3.5036 | 0.3759 | 0.3553 | |
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| 0.833 | 28.06 | 10100 | 3.5016 | 0.3769 | 0.3546 | |
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| 0.833 | 28.33 | 10200 | 3.5109 | 0.3752 | 0.3561 | |
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| 0.833 | 28.61 | 10300 | 3.4405 | 0.3844 | 0.3602 | |
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| 0.833 | 28.89 | 10400 | 3.4607 | 0.3835 | 0.3616 | |
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| 0.8304 | 29.17 | 10500 | 3.4678 | 0.3791 | 0.3591 | |
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| 0.8304 | 29.44 | 10600 | 3.4306 | 0.3858 | 0.3608 | |
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| 0.8304 | 29.72 | 10700 | 3.4509 | 0.3818 | 0.3604 | |
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| 0.8304 | 30.0 | 10800 | 3.4492 | 0.3818 | 0.3581 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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