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--- |
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license: cc-by-nc-4.0 |
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base_model: facebook/mms-1b-all |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: mms-MGB5 |
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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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# mms-MGB5 |
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This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2328 |
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- Wer: 88.9419 |
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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: 1e-05 |
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- train_batch_size: 14 |
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- eval_batch_size: 8 |
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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: 50 |
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- num_epochs: 25 |
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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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| 7.0257 | 0.16 | 250 | 6.2113 | 1.0041 | |
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| 3.2731 | 0.33 | 500 | 3.2218 | 1.0 | |
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| 2.8821 | 0.49 | 750 | 2.8522 | 1.0 | |
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| 2.1862 | 0.66 | 1000 | 2.0623 | 0.9721 | |
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| 1.6714 | 0.82 | 1250 | 1.6496 | 0.9398 | |
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| 1.7441 | 0.99 | 1500 | 1.5210 | 0.9314 | |
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| 1.4705 | 1.15 | 1750 | 1.4589 | 0.9251 | |
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| 1.4794 | 1.32 | 2000 | 1.4186 | 0.9213 | |
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| 1.4261 | 1.48 | 2250 | 1.3921 | 0.9175 | |
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| 1.3905 | 1.64 | 2500 | 1.3732 | 0.9185 | |
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| 1.4405 | 1.81 | 2750 | 1.3548 | 0.9134 | |
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| 1.3561 | 1.97 | 3000 | 1.3398 | 0.9093 | |
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| 1.4154 | 2.14 | 3250 | 1.3280 | 0.9128 | |
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| 1.361 | 2.3 | 3500 | 1.3161 | 0.9082 | |
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| 1.3421 | 2.47 | 3750 | 1.3064 | 0.9037 | |
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| 1.3615 | 2.63 | 4000 | 1.2976 | 0.9005 | |
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| 1.3517 | 2.8 | 4250 | 1.2996 | 0.9056 | |
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| 1.4316 | 2.96 | 4500 | 1.3136 | 0.9091 | |
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| 1.3946 | 3.12 | 4750 | 1.3534 | 0.9143 | |
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| 1.4888 | 3.29 | 5000 | 1.3797 | 0.9242 | |
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| 1.4223 | 3.45 | 5250 | 1.3707 | 0.9289 | |
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| 1.4616 | 3.62 | 5500 | 1.3463 | 0.9205 | |
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| 1.4196 | 3.78 | 5750 | 1.3362 | 0.9158 | |
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| 1.4163 | 3.95 | 6000 | 1.3392 | 0.9101 | |
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| 1.3408 | 4.11 | 6250 | 1.2765 | 89.9911 | |
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| 1.333 | 4.28 | 6500 | 1.2633 | 89.3855 | |
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| 1.3207 | 4.44 | 6750 | 1.2502 | 89.8381 | |
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| 1.2747 | 4.61 | 7000 | 1.2434 | 89.3478 | |
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| 1.3101 | 4.77 | 7250 | 1.2363 | 88.8864 | |
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| 1.281 | 4.93 | 7500 | 1.2290 | 88.7311 | |
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| 1.3351 | 5.1 | 7750 | 1.2211 | 88.3274 | |
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| 1.2738 | 5.26 | 8000 | 1.2147 | 88.2076 | |
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| 1.253 | 5.43 | 8250 | 1.2092 | 88.0834 | |
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| 1.2188 | 5.59 | 8500 | 1.2039 | 87.5776 | |
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| 1.2393 | 5.76 | 8750 | 1.1976 | 87.7418 | |
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| 1.2605 | 5.92 | 9000 | 1.1927 | 87.7019 | |
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| 1.2279 | 6.09 | 9250 | 1.1878 | 87.7307 | |
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| 1.2538 | 6.25 | 9500 | 1.1826 | 87.4201 | |
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| 1.2139 | 6.41 | 9750 | 1.1771 | 86.8301 | |
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| 1.1945 | 6.58 | 10000 | 1.1719 | 86.9210 | |
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| 1.2752 | 6.74 | 10250 | 1.1673 | 86.7902 | |
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| 1.1857 | 6.91 | 10500 | 1.1642 | 86.5306 | |
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| 1.1545 | 7.07 | 10750 | 1.1626 | 86.4929 | |
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| 1.2422 | 7.24 | 11000 | 1.1677 | 87.3270 | |
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| 1.2511 | 7.4 | 11250 | 1.2051 | 88.0435 | |
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| 1.2199 | 7.57 | 11500 | 1.2128 | 88.7844 | |
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| 1.2514 | 7.73 | 11750 | 1.2097 | 88.8199 | |
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| 1.2947 | 7.89 | 12000 | 1.2109 | 88.4339 | |
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| 1.3974 | 8.06 | 12250 | 1.2198 | 88.5426 | |
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| 1.2454 | 8.22 | 12500 | 1.2145 | 88.0546 | |
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| 1.2317 | 8.39 | 12750 | 1.2137 | 87.6353 | |
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| 1.3143 | 8.55 | 13000 | 1.2154 | 87.2671 | |
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| 1.3269 | 8.72 | 13250 | 1.2247 | 86.7924 | |
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| 1.2602 | 8.88 | 13500 | 1.2295 | 87.0453 | |
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| 1.2919 | 9.05 | 13750 | 1.2416 | 87.2205 | |
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| 1.2577 | 9.21 | 14000 | 1.2346 | 88.0058 | |
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| 1.2439 | 9.38 | 14250 | 1.2330 | 88.8421 | |
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| 1.4106 | 9.54 | 14500 | 1.2328 | 88.9286 | |
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| 1.2935 | 9.7 | 14750 | 1.2328 | 88.9419 | |
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| 1.2167 | 9.87 | 15000 | 1.2328 | 88.9419 | |
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| 1.3232 | 10.03 | 15250 | 1.2328 | 88.9419 | |
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| 1.2935 | 10.2 | 15500 | 1.2328 | 88.9419 | |
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| 1.2459 | 10.36 | 15750 | 1.2328 | 88.9419 | |
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| 1.2716 | 10.53 | 16000 | 1.2328 | 88.9419 | |
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| 1.2941 | 10.69 | 16250 | 1.2328 | 88.9419 | |
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| 1.2849 | 10.86 | 16500 | 1.2328 | 88.9419 | |
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| 1.2813 | 11.02 | 16750 | 1.2328 | 88.9419 | |
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| 1.351 | 11.18 | 17000 | 1.2328 | 88.9419 | |
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| 1.2302 | 11.35 | 17250 | 1.2328 | 88.9419 | |
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| 1.2724 | 11.51 | 17500 | 1.2328 | 88.9419 | |
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| 1.293 | 11.68 | 17750 | 1.2328 | 88.9419 | |
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| 1.2799 | 11.84 | 18000 | 1.2328 | 88.9419 | |
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| 1.2969 | 12.01 | 18250 | 1.2328 | 88.9419 | |
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| 1.2824 | 12.17 | 18500 | 1.2328 | 88.9419 | |
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| 1.2564 | 12.34 | 18750 | 1.2328 | 88.9419 | |
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| 1.2867 | 12.5 | 19000 | 1.2328 | 88.9419 | |
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| 1.2606 | 12.66 | 19250 | 1.2328 | 88.9419 | |
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| 1.2988 | 12.83 | 19500 | 1.2328 | 88.9419 | |
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| 1.2882 | 12.99 | 19750 | 1.2328 | 88.9419 | |
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| 1.3214 | 13.16 | 20000 | 1.2328 | 88.9419 | |
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| 1.2781 | 13.32 | 20250 | 1.2328 | 88.9419 | |
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| 1.3764 | 13.49 | 20500 | 1.2328 | 88.9419 | |
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| 1.2828 | 13.65 | 20750 | 1.2328 | 88.9419 | |
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| 1.2571 | 13.82 | 21000 | 1.2328 | 88.9419 | |
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| 1.2649 | 13.98 | 21250 | 1.2328 | 88.9419 | |
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| 1.2571 | 14.14 | 21500 | 1.2328 | 88.9419 | |
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| 1.2657 | 14.31 | 21750 | 1.2328 | 88.9419 | |
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| 1.3216 | 14.47 | 22000 | 1.2328 | 88.9419 | |
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| 1.2843 | 14.64 | 22250 | 1.2328 | 88.9419 | |
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| 1.307 | 14.8 | 22500 | 1.2328 | 88.9419 | |
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| 1.3198 | 14.97 | 22750 | 1.2328 | 88.9419 | |
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| 1.3673 | 15.13 | 23000 | 1.2328 | 88.9419 | |
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| 1.3636 | 15.3 | 23250 | 1.2328 | 88.9419 | |
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| 1.2858 | 15.46 | 23500 | 1.2328 | 88.9419 | |
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| 1.2658 | 15.62 | 23750 | 1.2328 | 88.9419 | |
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| 1.2532 | 15.79 | 24000 | 1.2328 | 88.9419 | |
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| 1.2647 | 15.95 | 24250 | 1.2328 | 88.9419 | |
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| 1.2912 | 16.12 | 24500 | 1.2328 | 88.9419 | |
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| 1.2707 | 16.28 | 24750 | 1.2328 | 88.9419 | |
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| 1.3161 | 16.45 | 25000 | 1.2328 | 88.9419 | |
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| 1.3061 | 16.61 | 25250 | 1.2328 | 88.9419 | |
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| 1.2688 | 16.78 | 25500 | 1.2328 | 88.9419 | |
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| 1.2528 | 16.94 | 25750 | 1.2328 | 88.9419 | |
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| 1.2585 | 17.11 | 26000 | 1.2328 | 88.9419 | |
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| 1.2681 | 17.27 | 26250 | 1.2328 | 88.9419 | |
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| 1.2705 | 17.43 | 26500 | 1.2328 | 88.9419 | |
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| 1.2662 | 17.6 | 26750 | 1.2328 | 88.9419 | |
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| 1.3273 | 17.76 | 27000 | 1.2328 | 88.9419 | |
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| 1.2774 | 17.93 | 27250 | 1.2328 | 88.9419 | |
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| 1.293 | 18.09 | 27500 | 1.2328 | 88.9419 | |
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| 1.2305 | 18.26 | 27750 | 1.2328 | 88.9419 | |
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| 1.3117 | 18.42 | 28000 | 1.2328 | 88.9419 | |
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| 1.397 | 18.59 | 28250 | 1.2328 | 88.9419 | |
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| 1.2693 | 18.75 | 28500 | 1.2328 | 88.9419 | |
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| 1.3219 | 18.91 | 28750 | 1.2328 | 88.9419 | |
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| 1.3352 | 19.08 | 29000 | 1.2328 | 88.9419 | |
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| 1.3321 | 19.24 | 29250 | 1.2328 | 88.9419 | |
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| 1.2534 | 19.41 | 29500 | 1.2328 | 88.9419 | |
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| 1.2507 | 19.57 | 29750 | 1.2328 | 88.9419 | |
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| 1.273 | 19.74 | 30000 | 1.2328 | 88.9419 | |
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| 1.2856 | 19.9 | 30250 | 1.2328 | 88.9419 | |
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| 1.3163 | 20.07 | 30500 | 1.2328 | 88.9419 | |
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| 1.2463 | 20.23 | 30750 | 1.2328 | 88.9419 | |
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| 1.3017 | 20.39 | 31000 | 1.2328 | 88.9419 | |
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| 1.3236 | 20.56 | 31250 | 1.2328 | 88.9419 | |
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| 1.3012 | 20.72 | 31500 | 1.2328 | 88.9419 | |
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| 1.3296 | 20.89 | 31750 | 1.2328 | 88.9419 | |
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| 1.362 | 21.05 | 32000 | 1.2328 | 88.9419 | |
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| 1.2816 | 21.22 | 32250 | 1.2328 | 88.9419 | |
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| 1.2383 | 21.38 | 32500 | 1.2328 | 88.9419 | |
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| 1.2127 | 21.55 | 32750 | 1.2328 | 88.9419 | |
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| 1.2443 | 21.71 | 33000 | 1.2328 | 88.9419 | |
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| 1.2553 | 21.88 | 33250 | 1.2328 | 88.9419 | |
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| 1.3227 | 22.04 | 33500 | 1.2328 | 88.9419 | |
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| 1.2923 | 22.2 | 33750 | 1.2328 | 88.9419 | |
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| 1.2954 | 22.37 | 34000 | 1.2328 | 88.9419 | |
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| 1.3123 | 22.53 | 34250 | 1.2328 | 88.9419 | |
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| 1.2675 | 22.7 | 34500 | 1.2328 | 88.9419 | |
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| 1.2565 | 22.86 | 34750 | 1.2328 | 88.9419 | |
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| 1.2139 | 23.03 | 35000 | 1.2328 | 88.9419 | |
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| 1.3014 | 23.19 | 35250 | 1.2328 | 88.9419 | |
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| 1.2949 | 23.36 | 35500 | 1.2328 | 88.9419 | |
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| 1.2854 | 23.52 | 35750 | 1.2328 | 88.9419 | |
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| 1.2639 | 23.68 | 36000 | 1.2328 | 88.9419 | |
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| 1.2692 | 23.85 | 36250 | 1.2328 | 88.9419 | |
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| 1.2935 | 24.01 | 36500 | 1.2328 | 88.9419 | |
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| 1.4062 | 24.18 | 36750 | 1.2328 | 88.9419 | |
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| 1.2626 | 24.34 | 37000 | 1.2328 | 88.9419 | |
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| 1.2555 | 24.51 | 37250 | 1.2328 | 88.9419 | |
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| 1.2876 | 24.67 | 37500 | 1.2328 | 88.9419 | |
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| 1.3362 | 24.84 | 37750 | 1.2328 | 88.9419 | |
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| 1.2625 | 25.0 | 38000 | 1.2328 | 88.9419 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.19.1 |
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- Tokenizers 0.13.3 |
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