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hubert-classifier-aug-fold-7-best

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: facebook/hubert-base-ls960
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: hubert-classifier-aug-fold-7
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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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+ # hubert-classifier-aug-fold-7
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+
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+ This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4362
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+ - Accuracy: 0.8976
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+ - Precision: 0.9089
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+ - Recall: 0.8976
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+ - F1: 0.8953
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+ - Binary: 0.9294
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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: 0.0001
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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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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 128
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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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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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+ | No log | 0.19 | 50 | 4.0533 | 0.0324 | 0.0014 | 0.0324 | 0.0027 | 0.2927 |
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+ | No log | 0.38 | 100 | 3.5885 | 0.0514 | 0.0169 | 0.0514 | 0.0106 | 0.3262 |
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+ | No log | 0.58 | 150 | 3.3501 | 0.0541 | 0.0206 | 0.0541 | 0.0140 | 0.3330 |
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+ | No log | 0.77 | 200 | 3.2675 | 0.0649 | 0.0154 | 0.0649 | 0.0203 | 0.3405 |
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+ | No log | 0.96 | 250 | 3.0965 | 0.1162 | 0.0330 | 0.1162 | 0.0456 | 0.3797 |
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+ | 3.7373 | 1.15 | 300 | 2.9467 | 0.1514 | 0.0616 | 0.1514 | 0.0770 | 0.4035 |
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+ | 3.7373 | 1.34 | 350 | 2.7676 | 0.2 | 0.1022 | 0.2 | 0.1111 | 0.4384 |
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+ | 3.7373 | 1.53 | 400 | 2.5435 | 0.2649 | 0.1888 | 0.2649 | 0.1783 | 0.4805 |
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+ | 3.7373 | 1.73 | 450 | 2.3812 | 0.2973 | 0.1993 | 0.2973 | 0.1965 | 0.5068 |
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+ | 3.7373 | 1.92 | 500 | 2.1573 | 0.3946 | 0.3075 | 0.3946 | 0.3120 | 0.5749 |
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+ | 2.9139 | 2.11 | 550 | 1.9561 | 0.4486 | 0.4077 | 0.4486 | 0.3798 | 0.6132 |
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+ | 2.9139 | 2.3 | 600 | 1.7966 | 0.4784 | 0.4652 | 0.4784 | 0.4273 | 0.6324 |
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+ | 2.9139 | 2.49 | 650 | 1.7610 | 0.5270 | 0.5083 | 0.5270 | 0.4675 | 0.6632 |
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+ | 2.9139 | 2.68 | 700 | 1.5796 | 0.5351 | 0.4840 | 0.5351 | 0.4750 | 0.6741 |
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+ | 2.9139 | 2.88 | 750 | 1.4707 | 0.5676 | 0.5624 | 0.5676 | 0.5239 | 0.6935 |
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+ | 2.2164 | 3.07 | 800 | 1.3680 | 0.6162 | 0.6049 | 0.6162 | 0.5829 | 0.7286 |
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+ | 2.2164 | 3.26 | 850 | 1.2484 | 0.6162 | 0.6078 | 0.6162 | 0.5800 | 0.7319 |
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+ | 2.2164 | 3.45 | 900 | 1.1271 | 0.6649 | 0.6659 | 0.6649 | 0.6400 | 0.7646 |
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+ | 2.2164 | 3.64 | 950 | 1.0343 | 0.7108 | 0.7139 | 0.7108 | 0.6851 | 0.7959 |
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+ | 2.2164 | 3.84 | 1000 | 1.0379 | 0.7027 | 0.7076 | 0.7027 | 0.6733 | 0.7922 |
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+ | 1.8319 | 4.03 | 1050 | 1.0744 | 0.7 | 0.7494 | 0.7 | 0.6818 | 0.7935 |
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+ | 1.8319 | 4.22 | 1100 | 0.9615 | 0.7324 | 0.7692 | 0.7324 | 0.7194 | 0.8141 |
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+ | 1.8319 | 4.41 | 1150 | 0.8683 | 0.7514 | 0.7827 | 0.7514 | 0.7341 | 0.8251 |
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+ | 1.8319 | 4.6 | 1200 | 0.8870 | 0.7432 | 0.7827 | 0.7432 | 0.7307 | 0.8195 |
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+ | 1.8319 | 4.79 | 1250 | 0.8191 | 0.7676 | 0.7874 | 0.7676 | 0.7516 | 0.8357 |
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+ | 1.8319 | 4.99 | 1300 | 0.7923 | 0.7784 | 0.8235 | 0.7784 | 0.7701 | 0.8441 |
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+ | 1.5844 | 5.18 | 1350 | 0.7525 | 0.8 | 0.8203 | 0.8 | 0.7905 | 0.8605 |
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+ | 1.5844 | 5.37 | 1400 | 0.7352 | 0.8 | 0.8401 | 0.8 | 0.7994 | 0.8603 |
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+ | 1.5844 | 5.56 | 1450 | 0.6931 | 0.8081 | 0.8423 | 0.8081 | 0.8017 | 0.8649 |
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+ | 1.5844 | 5.75 | 1500 | 0.6872 | 0.8081 | 0.8367 | 0.8081 | 0.8005 | 0.8670 |
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+ | 1.5844 | 5.94 | 1550 | 0.6630 | 0.8189 | 0.8507 | 0.8189 | 0.8133 | 0.8735 |
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+ | 1.4042 | 6.14 | 1600 | 0.6284 | 0.8216 | 0.8424 | 0.8216 | 0.8153 | 0.8757 |
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+ | 1.4042 | 6.33 | 1650 | 0.7190 | 0.7865 | 0.8274 | 0.7865 | 0.7788 | 0.8508 |
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+ | 1.4042 | 6.52 | 1700 | 0.6470 | 0.8216 | 0.8428 | 0.8216 | 0.8163 | 0.8754 |
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+ | 1.4042 | 6.71 | 1750 | 0.6415 | 0.8324 | 0.8655 | 0.8324 | 0.8277 | 0.8822 |
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+ | 1.4042 | 6.9 | 1800 | 0.6644 | 0.8216 | 0.8554 | 0.8216 | 0.8133 | 0.8735 |
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+ | 1.2826 | 7.09 | 1850 | 0.6328 | 0.8243 | 0.8607 | 0.8243 | 0.8217 | 0.8781 |
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+ | 1.2826 | 7.29 | 1900 | 0.6106 | 0.8351 | 0.8673 | 0.8351 | 0.8284 | 0.8857 |
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+ | 1.2826 | 7.48 | 1950 | 0.6186 | 0.8297 | 0.8686 | 0.8297 | 0.8248 | 0.8803 |
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+ | 1.2826 | 7.67 | 2000 | 0.6167 | 0.8351 | 0.8709 | 0.8351 | 0.8321 | 0.8838 |
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+ | 1.2826 | 7.86 | 2050 | 0.5680 | 0.8378 | 0.8691 | 0.8378 | 0.8352 | 0.8857 |
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+ | 1.1959 | 8.05 | 2100 | 0.5415 | 0.8541 | 0.8849 | 0.8541 | 0.8512 | 0.8978 |
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+ | 1.1959 | 8.25 | 2150 | 0.5322 | 0.8568 | 0.8910 | 0.8568 | 0.8552 | 0.8997 |
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+ | 1.1959 | 8.44 | 2200 | 0.5865 | 0.8432 | 0.8675 | 0.8432 | 0.8373 | 0.8914 |
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+ | 1.1959 | 8.63 | 2250 | 0.5779 | 0.8541 | 0.8865 | 0.8541 | 0.8512 | 0.9000 |
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+ | 1.1959 | 8.82 | 2300 | 0.5011 | 0.8757 | 0.9080 | 0.8757 | 0.8752 | 0.9154 |
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+ | 1.1236 | 9.01 | 2350 | 0.5108 | 0.8514 | 0.8804 | 0.8514 | 0.8498 | 0.8981 |
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+ | 1.1236 | 9.2 | 2400 | 0.5375 | 0.8486 | 0.8772 | 0.8486 | 0.8459 | 0.8962 |
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+ | 1.1236 | 9.4 | 2450 | 0.5775 | 0.8459 | 0.8746 | 0.8459 | 0.8473 | 0.8943 |
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+ | 1.1236 | 9.59 | 2500 | 0.5318 | 0.8514 | 0.8862 | 0.8514 | 0.8497 | 0.9003 |
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+ | 1.1236 | 9.78 | 2550 | 0.5484 | 0.8459 | 0.8761 | 0.8459 | 0.8439 | 0.8976 |
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+ | 1.1236 | 9.97 | 2600 | 0.5733 | 0.8486 | 0.8849 | 0.8486 | 0.8474 | 0.8951 |
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+ | 1.0544 | 10.16 | 2650 | 0.5349 | 0.8541 | 0.8818 | 0.8541 | 0.8496 | 0.9000 |
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+ | 1.0544 | 10.35 | 2700 | 0.5435 | 0.8459 | 0.8777 | 0.8459 | 0.8388 | 0.8932 |
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+ | 1.0544 | 10.55 | 2750 | 0.4787 | 0.8595 | 0.8822 | 0.8595 | 0.8563 | 0.9027 |
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+ | 1.0544 | 10.74 | 2800 | 0.4678 | 0.8595 | 0.8880 | 0.8595 | 0.8562 | 0.9027 |
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+ | 1.0544 | 10.93 | 2850 | 0.4572 | 0.8730 | 0.9001 | 0.8730 | 0.8707 | 0.9103 |
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+ | 1.0171 | 11.12 | 2900 | 0.5138 | 0.8568 | 0.8876 | 0.8568 | 0.8529 | 0.8997 |
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+ | 1.0171 | 11.31 | 2950 | 0.5102 | 0.8757 | 0.8980 | 0.8757 | 0.8750 | 0.9130 |
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+ | 1.0171 | 11.51 | 3000 | 0.5265 | 0.8676 | 0.8921 | 0.8676 | 0.8649 | 0.9076 |
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+ | 1.0171 | 11.7 | 3050 | 0.4659 | 0.8730 | 0.8961 | 0.8730 | 0.8733 | 0.9132 |
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+ | 1.0171 | 11.89 | 3100 | 0.4995 | 0.8676 | 0.8917 | 0.8676 | 0.8621 | 0.9084 |
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+ | 0.9541 | 12.08 | 3150 | 0.4533 | 0.8811 | 0.8996 | 0.8811 | 0.8788 | 0.9168 |
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+ | 0.9541 | 12.27 | 3200 | 0.4571 | 0.8865 | 0.9085 | 0.8865 | 0.8866 | 0.9205 |
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+ | 0.9541 | 12.46 | 3250 | 0.4846 | 0.8622 | 0.8908 | 0.8622 | 0.8596 | 0.9035 |
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+ | 0.9541 | 12.66 | 3300 | 0.4850 | 0.8730 | 0.8989 | 0.8730 | 0.8710 | 0.9111 |
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+ | 0.9541 | 12.85 | 3350 | 0.4826 | 0.8568 | 0.8834 | 0.8568 | 0.8522 | 0.8997 |
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+ | 0.9149 | 13.04 | 3400 | 0.4680 | 0.8730 | 0.8938 | 0.8730 | 0.8717 | 0.9103 |
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+ | 0.9149 | 13.23 | 3450 | 0.5733 | 0.8486 | 0.8769 | 0.8486 | 0.8468 | 0.8941 |
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+ | 0.9149 | 13.42 | 3500 | 0.5068 | 0.8730 | 0.8975 | 0.8730 | 0.8718 | 0.9111 |
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+ | 0.9149 | 13.61 | 3550 | 0.4816 | 0.8730 | 0.8991 | 0.8730 | 0.8721 | 0.9122 |
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+ | 0.9149 | 13.81 | 3600 | 0.5007 | 0.8676 | 0.8944 | 0.8676 | 0.8677 | 0.9095 |
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+ | 0.9149 | 14.0 | 3650 | 0.4674 | 0.8811 | 0.9061 | 0.8811 | 0.8796 | 0.9168 |
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+ | 0.8802 | 14.19 | 3700 | 0.4997 | 0.8622 | 0.8860 | 0.8622 | 0.8608 | 0.9035 |
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+ | 0.8802 | 14.38 | 3750 | 0.4425 | 0.8784 | 0.9036 | 0.8784 | 0.8768 | 0.9149 |
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+ | 0.8802 | 14.57 | 3800 | 0.5111 | 0.8811 | 0.9088 | 0.8811 | 0.8808 | 0.9170 |
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+ | 0.8802 | 14.77 | 3850 | 0.4408 | 0.8811 | 0.9036 | 0.8811 | 0.8794 | 0.9168 |
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+ | 0.8802 | 14.96 | 3900 | 0.5053 | 0.8622 | 0.8855 | 0.8622 | 0.8570 | 0.9035 |
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+ | 0.8475 | 15.15 | 3950 | 0.5046 | 0.8622 | 0.8897 | 0.8622 | 0.8599 | 0.9038 |
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+ | 0.8475 | 15.34 | 4000 | 0.4560 | 0.8649 | 0.8849 | 0.8649 | 0.8635 | 0.9068 |
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+ | 0.8475 | 15.53 | 4050 | 0.4562 | 0.8730 | 0.8944 | 0.8730 | 0.8722 | 0.9124 |
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+ | 0.8475 | 15.72 | 4100 | 0.4827 | 0.8622 | 0.8932 | 0.8622 | 0.8611 | 0.9027 |
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+ | 0.8475 | 15.92 | 4150 | 0.4750 | 0.8784 | 0.9039 | 0.8784 | 0.8775 | 0.9159 |
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+ | 0.8235 | 16.11 | 4200 | 0.4789 | 0.8703 | 0.8998 | 0.8703 | 0.8689 | 0.9092 |
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+ | 0.8235 | 16.3 | 4250 | 0.4445 | 0.8892 | 0.9136 | 0.8892 | 0.8875 | 0.9227 |
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+ | 0.8235 | 16.49 | 4300 | 0.4804 | 0.8703 | 0.8950 | 0.8703 | 0.8690 | 0.9086 |
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+ | 0.8235 | 16.68 | 4350 | 0.4556 | 0.8676 | 0.8878 | 0.8676 | 0.8639 | 0.9076 |
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+ | 0.8235 | 16.87 | 4400 | 0.5254 | 0.8622 | 0.8844 | 0.8622 | 0.8571 | 0.9030 |
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+ | 0.7913 | 17.07 | 4450 | 0.4432 | 0.8946 | 0.9105 | 0.8946 | 0.8916 | 0.9273 |
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+ | 0.7913 | 17.26 | 4500 | 0.4991 | 0.8622 | 0.8906 | 0.8622 | 0.8603 | 0.9035 |
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+ | 0.7913 | 17.45 | 4550 | 0.4480 | 0.8865 | 0.9067 | 0.8865 | 0.8836 | 0.9205 |
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+ | 0.7913 | 17.64 | 4600 | 0.4408 | 0.8757 | 0.8954 | 0.8757 | 0.8748 | 0.9130 |
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+ | 0.7913 | 17.83 | 4650 | 0.4559 | 0.8811 | 0.9033 | 0.8811 | 0.8804 | 0.9189 |
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+ | 0.7769 | 18.02 | 4700 | 0.4716 | 0.8919 | 0.9136 | 0.8919 | 0.8914 | 0.9254 |
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+ | 0.7769 | 18.22 | 4750 | 0.4492 | 0.8811 | 0.9059 | 0.8811 | 0.8785 | 0.9170 |
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+ | 0.7769 | 18.41 | 4800 | 0.4714 | 0.8811 | 0.9062 | 0.8811 | 0.8798 | 0.9170 |
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+ | 0.7769 | 18.6 | 4850 | 0.4849 | 0.8757 | 0.9015 | 0.8757 | 0.8745 | 0.9122 |
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+ | 0.7769 | 18.79 | 4900 | 0.4156 | 0.8946 | 0.9140 | 0.8946 | 0.8918 | 0.9262 |
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+ | 0.7769 | 18.98 | 4950 | 0.4333 | 0.8892 | 0.9066 | 0.8892 | 0.8862 | 0.9227 |
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+ | 0.7461 | 19.18 | 5000 | 0.4054 | 0.9054 | 0.9220 | 0.9054 | 0.9033 | 0.9341 |
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+ | 0.7461 | 19.37 | 5050 | 0.4613 | 0.8757 | 0.8999 | 0.8757 | 0.8699 | 0.9132 |
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+ | 0.7461 | 19.56 | 5100 | 0.4379 | 0.8865 | 0.9112 | 0.8865 | 0.8854 | 0.9219 |
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+ | 0.7461 | 19.75 | 5150 | 0.4349 | 0.8946 | 0.9120 | 0.8946 | 0.8934 | 0.9262 |
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+ | 0.7461 | 19.94 | 5200 | 0.4647 | 0.8811 | 0.9009 | 0.8811 | 0.8794 | 0.9181 |
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+ | 0.7216 | 20.13 | 5250 | 0.4346 | 0.9027 | 0.9189 | 0.9027 | 0.9017 | 0.9322 |
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+ | 0.7216 | 20.33 | 5300 | 0.4577 | 0.9 | 0.9156 | 0.9 | 0.8984 | 0.9322 |
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+ | 0.7216 | 20.52 | 5350 | 0.4712 | 0.8946 | 0.9152 | 0.8946 | 0.8944 | 0.9276 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.2
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+ - Pytorch 2.3.0
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+ - Datasets 2.19.1
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+ - Tokenizers 0.15.1
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