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--- |
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license: other |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: plant-seedlings-model-mit |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9400785854616895 |
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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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# plant-seedlings-model-mit |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2052 |
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- Accuracy: 0.9401 |
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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: 0.0002 |
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- train_batch_size: 16 |
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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: linear |
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- num_epochs: 20 |
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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 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 2.459 | 0.2 | 100 | 2.4084 | 0.1424 | |
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| 1.7264 | 0.39 | 200 | 1.5604 | 0.4430 | |
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| 1.427 | 0.59 | 300 | 1.2719 | 0.5447 | |
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| 1.1796 | 0.79 | 400 | 0.9608 | 0.6469 | |
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| 0.6449 | 0.98 | 500 | 0.9086 | 0.6783 | |
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| 0.819 | 1.18 | 600 | 0.8235 | 0.7230 | |
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| 0.711 | 1.38 | 700 | 0.8286 | 0.7161 | |
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| 0.6829 | 1.57 | 800 | 0.6853 | 0.7829 | |
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| 0.7093 | 1.77 | 900 | 0.8823 | 0.7112 | |
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| 0.6265 | 1.96 | 1000 | 0.5434 | 0.8129 | |
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| 0.6062 | 2.16 | 1100 | 0.4865 | 0.8301 | |
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| 0.6318 | 2.36 | 1200 | 0.5239 | 0.8256 | |
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| 0.5195 | 2.55 | 1300 | 0.5997 | 0.7809 | |
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| 0.5847 | 2.75 | 1400 | 0.5282 | 0.8099 | |
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| 0.4684 | 2.95 | 1500 | 0.4301 | 0.8502 | |
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| 0.7026 | 3.14 | 1600 | 0.4628 | 0.8522 | |
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| 0.443 | 3.34 | 1700 | 0.4201 | 0.8492 | |
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| 0.6532 | 3.54 | 1800 | 0.4979 | 0.8330 | |
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| 0.5021 | 3.73 | 1900 | 0.5098 | 0.8202 | |
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| 0.4203 | 3.93 | 2000 | 0.4277 | 0.8512 | |
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| 0.4201 | 4.13 | 2100 | 0.4046 | 0.8649 | |
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| 0.397 | 4.32 | 2200 | 0.5747 | 0.8158 | |
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| 0.472 | 4.52 | 2300 | 0.5175 | 0.8237 | |
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| 0.5614 | 4.72 | 2400 | 0.4351 | 0.8443 | |
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| 0.3184 | 4.91 | 2500 | 0.3635 | 0.8787 | |
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| 0.3409 | 5.11 | 2600 | 0.4374 | 0.8571 | |
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| 0.3132 | 5.3 | 2700 | 0.3622 | 0.8767 | |
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| 0.3928 | 5.5 | 2800 | 0.3522 | 0.8797 | |
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| 0.4538 | 5.7 | 2900 | 0.3652 | 0.8718 | |
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| 0.5516 | 5.89 | 3000 | 0.4128 | 0.8689 | |
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| 0.4113 | 6.09 | 3100 | 0.3973 | 0.8649 | |
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| 0.3365 | 6.29 | 3200 | 0.4116 | 0.8635 | |
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| 0.4611 | 6.48 | 3300 | 0.3312 | 0.8846 | |
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| 0.312 | 6.68 | 3400 | 0.3888 | 0.8679 | |
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| 0.3811 | 6.88 | 3500 | 0.3388 | 0.8841 | |
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| 0.3711 | 7.07 | 3600 | 0.3300 | 0.8954 | |
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| 0.4593 | 7.27 | 3700 | 0.3491 | 0.8831 | |
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| 0.5211 | 7.47 | 3800 | 0.3682 | 0.8895 | |
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| 0.2319 | 7.66 | 3900 | 0.3326 | 0.8861 | |
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| 0.3811 | 7.86 | 4000 | 0.3407 | 0.8910 | |
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| 0.4044 | 8.06 | 4100 | 0.3076 | 0.9028 | |
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| 0.367 | 8.25 | 4200 | 0.3126 | 0.9023 | |
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| 0.3862 | 8.45 | 4300 | 0.3281 | 0.8954 | |
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| 0.2489 | 8.64 | 4400 | 0.3166 | 0.8929 | |
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| 0.3197 | 8.84 | 4500 | 0.3564 | 0.8802 | |
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| 0.3114 | 9.04 | 4600 | 0.2978 | 0.8969 | |
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| 0.3589 | 9.23 | 4700 | 0.3438 | 0.8895 | |
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| 0.3075 | 9.43 | 4800 | 0.2894 | 0.9082 | |
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| 0.3862 | 9.63 | 4900 | 0.2880 | 0.9047 | |
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| 0.3319 | 9.82 | 5000 | 0.3628 | 0.8915 | |
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| 0.3022 | 10.02 | 5100 | 0.2624 | 0.9145 | |
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| 0.2697 | 10.22 | 5200 | 0.3866 | 0.8851 | |
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| 0.218 | 10.41 | 5300 | 0.2632 | 0.9101 | |
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| 0.3331 | 10.61 | 5400 | 0.3117 | 0.9023 | |
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| 0.3043 | 10.81 | 5500 | 0.3604 | 0.8900 | |
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| 0.3105 | 11.0 | 5600 | 0.2847 | 0.9111 | |
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| 0.1758 | 11.2 | 5700 | 0.3144 | 0.9082 | |
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| 0.2081 | 11.39 | 5800 | 0.2898 | 0.9101 | |
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| 0.4005 | 11.59 | 5900 | 0.3138 | 0.8998 | |
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| 0.264 | 11.79 | 6000 | 0.2792 | 0.9136 | |
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| 0.2765 | 11.98 | 6100 | 0.3021 | 0.9003 | |
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| 0.2595 | 12.18 | 6200 | 0.2625 | 0.9091 | |
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| 0.2745 | 12.38 | 6300 | 0.3078 | 0.9057 | |
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| 0.2437 | 12.57 | 6400 | 0.2533 | 0.9194 | |
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| 0.3765 | 12.77 | 6500 | 0.2972 | 0.9008 | |
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| 0.2911 | 12.97 | 6600 | 0.2909 | 0.9096 | |
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| 0.2335 | 13.16 | 6700 | 0.2684 | 0.9136 | |
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| 0.3099 | 13.36 | 6800 | 0.3057 | 0.9086 | |
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| 0.2377 | 13.56 | 6900 | 0.2862 | 0.9140 | |
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| 0.3159 | 13.75 | 7000 | 0.2271 | 0.9273 | |
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| 0.1893 | 13.95 | 7100 | 0.2519 | 0.9244 | |
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| 0.1703 | 14.15 | 7200 | 0.2616 | 0.9209 | |
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| 0.2527 | 14.34 | 7300 | 0.2393 | 0.9293 | |
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| 0.3772 | 14.54 | 7400 | 0.2662 | 0.9160 | |
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| 0.2574 | 14.73 | 7500 | 0.2724 | 0.9155 | |
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| 0.1803 | 14.93 | 7600 | 0.2549 | 0.9199 | |
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| 0.2935 | 15.13 | 7700 | 0.2561 | 0.9185 | |
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| 0.2105 | 15.32 | 7800 | 0.2202 | 0.9244 | |
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| 0.2877 | 15.52 | 7900 | 0.2428 | 0.9234 | |
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| 0.2467 | 15.72 | 8000 | 0.2531 | 0.9229 | |
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| 0.2955 | 15.91 | 8100 | 0.3258 | 0.9194 | |
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| 0.3136 | 16.11 | 8200 | 0.2430 | 0.9263 | |
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| 0.2543 | 16.31 | 8300 | 0.2502 | 0.9204 | |
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| 0.161 | 16.5 | 8400 | 0.2241 | 0.9352 | |
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| 0.194 | 16.7 | 8500 | 0.2313 | 0.9298 | |
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| 0.1951 | 16.9 | 8600 | 0.2446 | 0.9219 | |
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| 0.2515 | 17.09 | 8700 | 0.2476 | 0.9224 | |
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| 0.1274 | 17.29 | 8800 | 0.2445 | 0.9273 | |
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| 0.3035 | 17.49 | 8900 | 0.2704 | 0.9239 | |
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| 0.2253 | 17.68 | 9000 | 0.2436 | 0.9332 | |
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| 0.0982 | 17.88 | 9100 | 0.2523 | 0.9327 | |
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| 0.1778 | 18.07 | 9200 | 0.2425 | 0.9322 | |
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| 0.1362 | 18.27 | 9300 | 0.2653 | 0.9219 | |
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| 0.2342 | 18.47 | 9400 | 0.2076 | 0.9401 | |
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| 0.2231 | 18.66 | 9500 | 0.2238 | 0.9361 | |
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| 0.2159 | 18.86 | 9600 | 0.2115 | 0.9357 | |
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| 0.1826 | 19.06 | 9700 | 0.2079 | 0.9332 | |
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| 0.2221 | 19.25 | 9800 | 0.2003 | 0.9366 | |
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| 0.136 | 19.45 | 9900 | 0.2170 | 0.9401 | |
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| 0.0959 | 19.65 | 10000 | 0.1891 | 0.9440 | |
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| 0.1525 | 19.84 | 10100 | 0.2052 | 0.9401 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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