Initial Commit
Browse files- README.md +171 -0
- config.json +159 -0
- eval_results_ml.json +1 -0
- pytorch_model.bin +3 -0
- training_args.bin +3 -0
README.md
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| 1 |
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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-PO-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-PO-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: 16.8889
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- Accuracy: 0.3392
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- F1: 0.3158
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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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| 53 |
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| 54 |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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| 55 |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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| 56 |
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| No log | 0.28 | 100 | 15.1938 | 0.1113 | 0.0196 |
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| 57 |
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| No log | 0.56 | 200 | 13.0000 | 0.2262 | 0.1178 |
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| No log | 0.83 | 300 | 12.8096 | 0.2600 | 0.1821 |
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| No log | 1.11 | 400 | 13.4776 | 0.2777 | 0.1978 |
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| 8.9308 | 1.39 | 500 | 12.7408 | 0.2941 | 0.2311 |
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| 8.9308 | 1.67 | 600 | 13.4074 | 0.2770 | 0.2356 |
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| 8.9308 | 1.94 | 700 | 13.1908 | 0.2714 | 0.2355 |
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| 8.9308 | 2.22 | 800 | 12.6353 | 0.3305 | 0.2719 |
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| 8.9308 | 2.5 | 900 | 13.1426 | 0.3303 | 0.2656 |
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| 3.601 | 2.78 | 1000 | 13.5699 | 0.2997 | 0.2496 |
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| 3.601 | 3.06 | 1100 | 13.1690 | 0.3353 | 0.2705 |
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| 67 |
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| 3.601 | 3.33 | 1200 | 15.0255 | 0.3000 | 0.2494 |
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| 3.601 | 3.61 | 1300 | 14.5637 | 0.3078 | 0.2639 |
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| 3.601 | 3.89 | 1400 | 14.6327 | 0.3117 | 0.2652 |
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| 2.2762 | 4.17 | 1500 | 13.9581 | 0.3308 | 0.2721 |
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| 2.2762 | 4.44 | 1600 | 14.3766 | 0.3297 | 0.2688 |
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| 2.2762 | 4.72 | 1700 | 14.6858 | 0.3253 | 0.2873 |
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| 2.2762 | 5.0 | 1800 | 14.6089 | 0.3374 | 0.2849 |
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| 2.2762 | 5.28 | 1900 | 16.0731 | 0.3006 | 0.2704 |
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| 1.5736 | 5.56 | 2000 | 16.6390 | 0.2973 | 0.2668 |
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| 1.5736 | 5.83 | 2100 | 16.0277 | 0.3199 | 0.2757 |
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| 1.5736 | 6.11 | 2200 | 15.9727 | 0.3105 | 0.2735 |
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| 1.5736 | 6.39 | 2300 | 15.9141 | 0.3217 | 0.2823 |
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| 1.5736 | 6.67 | 2400 | 16.4168 | 0.3065 | 0.2746 |
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| 1.2249 | 6.94 | 2500 | 16.6765 | 0.3035 | 0.2671 |
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| 1.2249 | 7.22 | 2600 | 18.9168 | 0.2799 | 0.2661 |
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| 1.2249 | 7.5 | 2700 | 16.8271 | 0.3092 | 0.2813 |
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| 1.2249 | 7.78 | 2800 | 16.6488 | 0.3171 | 0.2853 |
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| 1.2249 | 8.06 | 2900 | 15.9239 | 0.3226 | 0.2850 |
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| 0.935 | 8.33 | 3000 | 17.3692 | 0.3010 | 0.2766 |
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| 0.935 | 8.61 | 3100 | 16.5296 | 0.3209 | 0.2933 |
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| 0.935 | 8.89 | 3200 | 16.5901 | 0.3194 | 0.2834 |
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| 0.935 | 9.17 | 3300 | 16.1886 | 0.3336 | 0.2949 |
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| 0.935 | 9.44 | 3400 | 18.8301 | 0.2865 | 0.2701 |
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| 0.7798 | 9.72 | 3500 | 17.3558 | 0.3219 | 0.2914 |
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| 0.7798 | 10.0 | 3600 | 16.5220 | 0.3293 | 0.2971 |
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| 0.7798 | 10.28 | 3700 | 15.8939 | 0.3461 | 0.3132 |
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| 0.7798 | 10.56 | 3800 | 17.0903 | 0.3230 | 0.2900 |
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| 0.7798 | 10.83 | 3900 | 16.6453 | 0.3286 | 0.2949 |
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| 0.6876 | 11.11 | 4000 | 15.7965 | 0.3375 | 0.2966 |
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| 0.6876 | 11.39 | 4100 | 16.1060 | 0.3280 | 0.2889 |
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| 0.6876 | 11.67 | 4200 | 18.4275 | 0.2940 | 0.2797 |
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| 0.6876 | 11.94 | 4300 | 18.2792 | 0.2964 | 0.2803 |
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| 0.6876 | 12.22 | 4400 | 16.5417 | 0.3282 | 0.2978 |
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| 0.595 | 12.5 | 4500 | 16.8369 | 0.3299 | 0.2911 |
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| 0.595 | 12.78 | 4600 | 16.6338 | 0.3297 | 0.2974 |
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| 0.595 | 13.06 | 4700 | 16.5216 | 0.3386 | 0.3025 |
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| 0.595 | 13.33 | 4800 | 16.7872 | 0.3307 | 0.2950 |
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| 0.595 | 13.61 | 4900 | 16.9420 | 0.3279 | 0.3052 |
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| 0.5474 | 13.89 | 5000 | 17.4468 | 0.3224 | 0.3019 |
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| 0.5474 | 14.17 | 5100 | 17.1031 | 0.3285 | 0.3070 |
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| 107 |
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| 0.5474 | 14.44 | 5200 | 17.0544 | 0.3265 | 0.3123 |
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| 0.5474 | 14.72 | 5300 | 17.7777 | 0.3198 | 0.3001 |
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| 109 |
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| 0.5474 | 15.0 | 5400 | 17.9026 | 0.3179 | 0.2953 |
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| 0.4809 | 15.28 | 5500 | 17.2419 | 0.3215 | 0.2941 |
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| 111 |
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| 0.4809 | 15.56 | 5600 | 16.8154 | 0.3323 | 0.3078 |
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| 112 |
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| 0.4809 | 15.83 | 5700 | 16.8620 | 0.3366 | 0.3085 |
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| 113 |
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| 0.4809 | 16.11 | 5800 | 16.7189 | 0.3355 | 0.3119 |
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| 0.4809 | 16.39 | 5900 | 17.1307 | 0.3322 | 0.3116 |
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| 115 |
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| 0.4578 | 16.67 | 6000 | 16.7557 | 0.3358 | 0.3104 |
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| 116 |
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| 0.4578 | 16.94 | 6100 | 17.2668 | 0.3258 | 0.3090 |
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| 117 |
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| 0.4578 | 17.22 | 6200 | 17.0697 | 0.3290 | 0.3061 |
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| 118 |
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| 0.4578 | 17.5 | 6300 | 17.3693 | 0.3319 | 0.2995 |
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| 119 |
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| 0.4578 | 17.78 | 6400 | 17.9128 | 0.3163 | 0.2962 |
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| 120 |
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| 0.4219 | 18.06 | 6500 | 17.1696 | 0.3292 | 0.3030 |
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| 121 |
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| 0.4219 | 18.33 | 6600 | 17.5121 | 0.3223 | 0.3048 |
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| 122 |
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| 0.4219 | 18.61 | 6700 | 16.7103 | 0.3346 | 0.3100 |
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| 123 |
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| 0.4219 | 18.89 | 6800 | 15.9694 | 0.3506 | 0.3125 |
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| 124 |
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| 0.4219 | 19.17 | 6900 | 16.8935 | 0.3388 | 0.3058 |
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| 0.4054 | 19.44 | 7000 | 17.2889 | 0.3291 | 0.3050 |
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| 126 |
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| 0.4054 | 19.72 | 7100 | 16.3730 | 0.3460 | 0.3132 |
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| 127 |
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| 0.4054 | 20.0 | 7200 | 17.1059 | 0.3341 | 0.3043 |
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| 128 |
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| 0.4054 | 20.28 | 7300 | 17.5861 | 0.3258 | 0.2939 |
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| 129 |
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| 0.4054 | 20.56 | 7400 | 17.8314 | 0.3224 | 0.3066 |
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| 130 |
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| 0.3868 | 20.83 | 7500 | 17.1402 | 0.3341 | 0.3131 |
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| 131 |
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| 0.3868 | 21.11 | 7600 | 16.9637 | 0.3403 | 0.3133 |
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| 132 |
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| 0.3868 | 21.39 | 7700 | 16.8744 | 0.3366 | 0.3143 |
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| 133 |
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| 0.3868 | 21.67 | 7800 | 17.2058 | 0.3349 | 0.3105 |
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| 134 |
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| 0.3868 | 21.94 | 7900 | 16.8000 | 0.3422 | 0.3083 |
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| 135 |
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| 0.372 | 22.22 | 8000 | 16.6248 | 0.3444 | 0.3098 |
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| 136 |
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| 0.372 | 22.5 | 8100 | 16.9822 | 0.3362 | 0.3098 |
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| 137 |
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| 0.372 | 22.78 | 8200 | 17.1206 | 0.3321 | 0.3039 |
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| 138 |
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| 0.372 | 23.06 | 8300 | 16.0653 | 0.3542 | 0.3197 |
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| 139 |
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| 0.372 | 23.33 | 8400 | 16.7092 | 0.3397 | 0.3160 |
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| 140 |
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| 0.3619 | 23.61 | 8500 | 17.0533 | 0.3343 | 0.3094 |
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| 141 |
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| 0.3619 | 23.89 | 8600 | 17.5675 | 0.3268 | 0.3066 |
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| 142 |
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| 0.3619 | 24.17 | 8700 | 17.1220 | 0.3334 | 0.3087 |
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| 143 |
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| 0.3619 | 24.44 | 8800 | 17.1167 | 0.3313 | 0.3042 |
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| 144 |
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| 0.3619 | 24.72 | 8900 | 16.9051 | 0.3366 | 0.3097 |
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| 145 |
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| 0.3504 | 25.0 | 9000 | 16.5664 | 0.3426 | 0.3134 |
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| 146 |
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| 0.3504 | 25.28 | 9100 | 16.3712 | 0.3456 | 0.3179 |
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| 147 |
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| 0.3504 | 25.56 | 9200 | 16.8119 | 0.3372 | 0.3092 |
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| 148 |
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| 0.3504 | 25.83 | 9300 | 16.9497 | 0.3382 | 0.3107 |
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| 149 |
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| 0.3504 | 26.11 | 9400 | 16.7615 | 0.3420 | 0.3144 |
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| 150 |
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| 0.3431 | 26.39 | 9500 | 16.5107 | 0.3457 | 0.3224 |
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| 151 |
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| 0.3431 | 26.67 | 9600 | 16.8506 | 0.3412 | 0.3156 |
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| 152 |
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| 0.3431 | 26.94 | 9700 | 16.8854 | 0.3398 | 0.3143 |
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| 153 |
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| 0.3431 | 27.22 | 9800 | 16.6477 | 0.3424 | 0.3151 |
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| 154 |
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| 0.3431 | 27.5 | 9900 | 16.8462 | 0.3398 | 0.3137 |
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| 155 |
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| 0.3382 | 27.78 | 10000 | 17.1089 | 0.3358 | 0.3113 |
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| 156 |
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| 0.3382 | 28.06 | 10100 | 17.0908 | 0.3360 | 0.3097 |
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| 157 |
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| 0.3382 | 28.33 | 10200 | 16.9382 | 0.3399 | 0.3169 |
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| 158 |
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| 0.3382 | 28.61 | 10300 | 16.8872 | 0.3392 | 0.3118 |
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| 159 |
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| 0.3382 | 28.89 | 10400 | 16.9511 | 0.3389 | 0.3145 |
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| 160 |
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| 0.3333 | 29.17 | 10500 | 16.7141 | 0.3420 | 0.3135 |
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| 161 |
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| 0.3333 | 29.44 | 10600 | 16.9588 | 0.3383 | 0.3119 |
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| 162 |
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| 0.3333 | 29.72 | 10700 | 16.9446 | 0.3379 | 0.3138 |
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| 163 |
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| 0.3333 | 30.0 | 10800 | 16.8889 | 0.3392 | 0.3158 |
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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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config.json
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eval_results_ml.json
ADDED
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|
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ADDED
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