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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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datasets: |
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- fleurs |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-mms-1b-arabic-colab |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: fleurs |
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type: fleurs |
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config: ar_eg |
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split: test |
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args: ar_eg |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.2590647661915479 |
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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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# wav2vec2-large-mms-1b-arabic-colab |
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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 fleurs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2922 |
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- Wer: 0.2591 |
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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: 4 |
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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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- lr_scheduler_warmup_steps: 100 |
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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 | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 18.9344 | 0.19 | 100 | 17.8048 | 1.0 | |
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| 15.6959 | 0.38 | 200 | 14.1448 | 1.0 | |
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| 11.9387 | 0.57 | 300 | 9.8417 | 1.0 | |
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| 7.554 | 0.76 | 400 | 5.3727 | 1.0 | |
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| 4.3953 | 0.95 | 500 | 3.5681 | 1.0 | |
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| 3.3533 | 1.14 | 600 | 3.1439 | 1.0 | |
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| 2.9309 | 1.33 | 700 | 2.5171 | 0.9987 | |
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| 2.1985 | 1.52 | 800 | 1.7128 | 0.8522 | |
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| 1.5126 | 1.71 | 900 | 1.1276 | 0.5744 | |
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| 1.0376 | 1.9 | 1000 | 0.7830 | 0.4400 | |
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| 0.7702 | 2.09 | 1100 | 0.5959 | 0.3765 | |
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| 0.6274 | 2.28 | 1200 | 0.4986 | 0.3363 | |
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| 0.5423 | 2.47 | 1300 | 0.4473 | 0.3197 | |
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| 0.494 | 2.66 | 1400 | 0.4153 | 0.3046 | |
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| 0.4372 | 2.85 | 1500 | 0.3940 | 0.2946 | |
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| 0.4667 | 3.04 | 1600 | 0.3791 | 0.2887 | |
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| 0.4228 | 3.23 | 1700 | 0.3670 | 0.2823 | |
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| 0.4177 | 3.42 | 1800 | 0.3571 | 0.2803 | |
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| 0.3824 | 3.61 | 1900 | 0.3494 | 0.2789 | |
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| 0.4002 | 3.8 | 2000 | 0.3435 | 0.2782 | |
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| 0.4112 | 3.99 | 2100 | 0.3385 | 0.2776 | |
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| 0.3788 | 4.18 | 2200 | 0.3342 | 0.2768 | |
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| 0.4079 | 4.37 | 2300 | 0.3305 | 0.2752 | |
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| 0.3939 | 4.56 | 2400 | 0.3271 | 0.2733 | |
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| 0.3601 | 4.75 | 2500 | 0.3250 | 0.2724 | |
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| 0.3443 | 4.94 | 2600 | 0.3223 | 0.2727 | |
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| 0.3723 | 5.13 | 2700 | 0.3200 | 0.2724 | |
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| 0.3669 | 5.32 | 2800 | 0.3182 | 0.2704 | |
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| 0.3117 | 5.51 | 2900 | 0.3167 | 0.2693 | |
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| 0.3658 | 5.7 | 3000 | 0.3150 | 0.2694 | |
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| 0.3731 | 5.89 | 3100 | 0.3132 | 0.2683 | |
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| 0.3542 | 6.08 | 3200 | 0.3122 | 0.2684 | |
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| 0.3667 | 6.27 | 3300 | 0.3108 | 0.2681 | |
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| 0.3115 | 6.46 | 3400 | 0.3099 | 0.2671 | |
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| 0.3466 | 6.65 | 3500 | 0.3092 | 0.2663 | |
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| 0.3497 | 6.84 | 3600 | 0.3082 | 0.2656 | |
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| 0.3276 | 7.03 | 3700 | 0.3076 | 0.2667 | |
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| 0.3316 | 7.22 | 3800 | 0.3070 | 0.2651 | |
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| 0.3324 | 7.41 | 3900 | 0.3060 | 0.2656 | |
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| 0.323 | 7.6 | 4000 | 0.3054 | 0.2661 | |
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| 0.3411 | 7.79 | 4100 | 0.3045 | 0.2641 | |
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| 0.3583 | 7.98 | 4200 | 0.3037 | 0.2649 | |
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| 0.3299 | 8.17 | 4300 | 0.3035 | 0.2649 | |
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| 0.2899 | 8.37 | 4400 | 0.3030 | 0.2643 | |
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| 0.3432 | 8.56 | 4500 | 0.3025 | 0.2651 | |
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| 0.3275 | 8.75 | 4600 | 0.3018 | 0.2631 | |
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| 0.3652 | 8.94 | 4700 | 0.3011 | 0.2637 | |
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| 0.3373 | 9.13 | 4800 | 0.3009 | 0.2626 | |
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| 0.3097 | 9.32 | 4900 | 0.3005 | 0.2627 | |
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| 0.3163 | 9.51 | 5000 | 0.2997 | 0.2623 | |
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| 0.3443 | 9.7 | 5100 | 0.2995 | 0.2623 | |
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| 0.346 | 9.89 | 5200 | 0.2989 | 0.2626 | |
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| 0.302 | 10.08 | 5300 | 0.2988 | 0.2624 | |
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| 0.3252 | 10.27 | 5400 | 0.2983 | 0.2623 | |
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| 0.3316 | 10.46 | 5500 | 0.2980 | 0.2632 | |
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| 0.3424 | 10.65 | 5600 | 0.2975 | 0.2629 | |
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| 0.3205 | 10.84 | 5700 | 0.2977 | 0.2622 | |
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| 0.3164 | 11.03 | 5800 | 0.2973 | 0.2618 | |
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| 0.3348 | 11.22 | 5900 | 0.2968 | 0.2619 | |
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| 0.3236 | 11.41 | 6000 | 0.2967 | 0.2612 | |
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| 0.3073 | 11.6 | 6100 | 0.2962 | 0.2627 | |
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| 0.3129 | 11.79 | 6200 | 0.2964 | 0.2623 | |
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| 0.3319 | 11.98 | 6300 | 0.2961 | 0.2621 | |
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| 0.2974 | 12.17 | 6400 | 0.2960 | 0.2613 | |
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| 0.3557 | 12.36 | 6500 | 0.2955 | 0.2612 | |
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| 0.3068 | 12.55 | 6600 | 0.2957 | 0.2619 | |
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| 0.3292 | 12.74 | 6700 | 0.2954 | 0.2619 | |
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| 0.3278 | 12.93 | 6800 | 0.2952 | 0.2612 | |
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| 0.314 | 13.12 | 6900 | 0.2948 | 0.2614 | |
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| 0.3182 | 13.31 | 7000 | 0.2949 | 0.2618 | |
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| 0.3322 | 13.5 | 7100 | 0.2948 | 0.2612 | |
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| 0.3089 | 13.69 | 7200 | 0.2944 | 0.2616 | |
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| 0.3176 | 13.88 | 7300 | 0.2943 | 0.2613 | |
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| 0.3025 | 14.07 | 7400 | 0.2942 | 0.2612 | |
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| 0.3277 | 14.26 | 7500 | 0.2941 | 0.2613 | |
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| 0.3241 | 14.45 | 7600 | 0.2940 | 0.2617 | |
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| 0.3084 | 14.64 | 7700 | 0.2938 | 0.2614 | |
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| 0.324 | 14.83 | 7800 | 0.2935 | 0.2612 | |
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| 0.3229 | 15.02 | 7900 | 0.2934 | 0.2609 | |
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| 0.3224 | 15.21 | 8000 | 0.2933 | 0.2602 | |
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| 0.2859 | 15.4 | 8100 | 0.2932 | 0.2604 | |
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| 0.3173 | 15.59 | 8200 | 0.2931 | 0.2598 | |
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| 0.3399 | 15.78 | 8300 | 0.2931 | 0.2602 | |
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| 0.3176 | 15.97 | 8400 | 0.2930 | 0.2598 | |
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| 0.2993 | 16.16 | 8500 | 0.2930 | 0.2602 | |
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| 0.3289 | 16.35 | 8600 | 0.2930 | 0.2598 | |
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| 0.3149 | 16.54 | 8700 | 0.2928 | 0.2601 | |
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| 0.3172 | 16.73 | 8800 | 0.2927 | 0.2599 | |
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| 0.3204 | 16.92 | 8900 | 0.2926 | 0.2597 | |
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| 0.3117 | 17.11 | 9000 | 0.2926 | 0.2604 | |
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| 0.3051 | 17.3 | 9100 | 0.2927 | 0.2608 | |
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| 0.3296 | 17.49 | 9200 | 0.2927 | 0.2604 | |
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| 0.309 | 17.68 | 9300 | 0.2926 | 0.2602 | |
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| 0.3138 | 17.87 | 9400 | 0.2925 | 0.2593 | |
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| 0.2802 | 18.06 | 9500 | 0.2925 | 0.2594 | |
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| 0.308 | 18.25 | 9600 | 0.2925 | 0.2593 | |
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| 0.3076 | 18.44 | 9700 | 0.2925 | 0.2591 | |
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| 0.312 | 18.63 | 9800 | 0.2923 | 0.2592 | |
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| 0.31 | 18.82 | 9900 | 0.2923 | 0.2593 | |
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| 0.3317 | 19.01 | 10000 | 0.2923 | 0.2592 | |
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| 0.3357 | 19.2 | 10100 | 0.2923 | 0.2593 | |
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| 0.302 | 19.39 | 10200 | 0.2922 | 0.2596 | |
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| 0.294 | 19.58 | 10300 | 0.2923 | 0.2592 | |
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| 0.3158 | 19.77 | 10400 | 0.2923 | 0.2593 | |
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| 0.3025 | 19.96 | 10500 | 0.2922 | 0.2591 | |
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### Framework versions |
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- Transformers 4.35.0.dev0 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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