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jvalline
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randomization_model

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t5
text2text-generation
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text-generation-inference
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@@ -18,10 +18,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.2626
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  - Bleu: 0.0001
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  - Accuracy: 0.0
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- - Gen Len: 18.999
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  ## Model description
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@@ -53,36 +53,66 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Bleu | Accuracy | Gen Len |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:-------:|
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- | 3.0103 | 0.1 | 50 | 2.5132 | 0.0 | 0.0 | 18.9985 |
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- | 2.999 | 0.2 | 100 | 2.4883 | 0.0 | 0.0 | 19.0 |
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- | 2.9457 | 0.3 | 150 | 2.4640 | 0.0 | 0.0 | 19.0 |
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- | 2.8865 | 0.4 | 200 | 2.4431 | 0.0 | 0.0 | 19.0 |
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- | 2.8935 | 0.5 | 250 | 2.4240 | 0.0 | 0.0 | 19.0 |
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- | 2.8983 | 0.6 | 300 | 2.4079 | 0.0 | 0.0 | 19.0 |
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- | 2.8579 | 0.7 | 350 | 2.3933 | 0.0 | 0.0 | 19.0 |
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- | 2.8501 | 0.8 | 400 | 2.3794 | 0.0 | 0.0 | 19.0 |
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- | 2.7892 | 0.9 | 450 | 2.3683 | 0.0 | 0.0 | 19.0 |
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- | 2.7962 | 1.0 | 500 | 2.3561 | 0.0 | 0.0 | 19.0 |
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- | 2.8408 | 1.1 | 550 | 2.3456 | 0.0 | 0.0 | 19.0 |
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- | 2.8049 | 1.2 | 600 | 2.3350 | 0.0001 | 0.0 | 19.0 |
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- | 2.8051 | 1.3 | 650 | 2.3278 | 0.0001 | 0.0 | 19.0 |
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- | 2.8126 | 1.4 | 700 | 2.3192 | 0.0001 | 0.0 | 19.0 |
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- | 2.7689 | 1.5 | 750 | 2.3121 | 0.0001 | 0.0 | 19.0 |
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- | 2.7559 | 1.6 | 800 | 2.3051 | 0.0001 | 0.0 | 18.9995 |
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- | 2.7672 | 1.7 | 850 | 2.2978 | 0.0001 | 0.0 | 18.9985 |
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- | 2.7901 | 1.8 | 900 | 2.2916 | 0.0001 | 0.0 | 18.9995 |
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- | 2.7571 | 1.9 | 950 | 2.2868 | 0.0001 | 0.0 | 18.9985 |
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- | 2.7796 | 2.0 | 1000 | 2.2834 | 0.0001 | 0.0 | 18.9985 |
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- | 2.7393 | 2.1 | 1050 | 2.2798 | 0.0001 | 0.0 | 18.9985 |
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- | 2.7309 | 2.2 | 1100 | 2.2757 | 0.0001 | 0.0 | 18.9985 |
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- | 2.7703 | 2.3 | 1150 | 2.2729 | 0.0001 | 0.0 | 18.999 |
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- | 2.7354 | 2.4 | 1200 | 2.2703 | 0.0001 | 0.0 | 18.999 |
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- | 2.7428 | 2.5 | 1250 | 2.2678 | 0.0001 | 0.0 | 18.999 |
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- | 2.7571 | 2.6 | 1300 | 2.2661 | 0.0001 | 0.0 | 18.999 |
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- | 2.7218 | 2.7 | 1350 | 2.2645 | 0.0001 | 0.0 | 18.999 |
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- | 2.7051 | 2.8 | 1400 | 2.2634 | 0.0001 | 0.0 | 18.999 |
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- | 2.7466 | 2.9 | 1450 | 2.2628 | 0.0001 | 0.0 | 18.999 |
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- | 2.722 | 3.0 | 1500 | 2.2626 | 0.0001 | 0.0 | 18.999 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.2271
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  - Bleu: 0.0001
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  - Accuracy: 0.0
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+ - Gen Len: 18.997
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Bleu | Accuracy | Gen Len |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:-------:|
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+ | 6.7713 | 0.05 | 50 | 4.1342 | 0.0 | 0.0 | 19.0 |
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+ | 4.0962 | 0.1 | 100 | 3.6084 | 0.0 | 0.0 | 19.0 |
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+ | 3.7511 | 0.15 | 150 | 3.3793 | 0.0 | 0.0 | 18.99 |
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+ | 3.6618 | 0.2 | 200 | 3.2099 | 0.0 | 0.0 | 18.9845 |
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+ | 3.5055 | 0.25 | 250 | 3.0655 | 0.0 | 0.0 | 18.9845 |
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+ | 3.4285 | 0.3 | 300 | 2.9465 | 0.0 | 0.0 | 18.986 |
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+ | 3.323 | 0.35 | 350 | 2.8558 | 0.0 | 0.0 | 18.982 |
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+ | 3.257 | 0.4 | 400 | 2.7828 | 0.0 | 0.0 | 18.9845 |
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+ | 3.2148 | 0.45 | 450 | 2.7311 | 0.0 | 0.0 | 18.9915 |
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+ | 3.1964 | 0.5 | 500 | 2.6861 | 0.0 | 0.0 | 18.9905 |
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+ | 3.108 | 0.55 | 550 | 2.6446 | 0.0 | 0.0 | 18.9905 |
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+ | 3.0801 | 0.6 | 600 | 2.6087 | 0.0 | 0.0 | 18.9925 |
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+ | 3.0439 | 0.65 | 650 | 2.5770 | 0.0 | 0.0 | 18.9915 |
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+ | 3.0683 | 0.7 | 700 | 2.5504 | 0.0 | 0.0 | 18.9955 |
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+ | 3.0283 | 0.75 | 750 | 2.5223 | 0.0 | 0.0 | 18.9955 |
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+ | 3.0292 | 0.8 | 800 | 2.5003 | 0.0 | 0.0 | 18.9935 |
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+ | 2.9533 | 0.85 | 850 | 2.4797 | 0.0 | 0.0 | 18.9965 |
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+ | 3.006 | 0.9 | 900 | 2.4627 | 0.0 | 0.0 | 19.0 |
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+ | 2.9028 | 0.95 | 950 | 2.4463 | 0.0 | 0.0 | 18.997 |
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+ | 2.9219 | 1.0 | 1000 | 2.4287 | 0.0 | 0.0 | 18.996 |
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+ | 2.8995 | 1.05 | 1050 | 2.4120 | 0.0 | 0.0 | 18.9995 |
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+ | 2.8857 | 1.1 | 1100 | 2.3988 | 0.0 | 0.0 | 19.0 |
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+ | 2.8971 | 1.15 | 1150 | 2.3861 | 0.0 | 0.0 | 18.998 |
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+ | 2.8882 | 1.2 | 1200 | 2.3747 | 0.0 | 0.0 | 18.998 |
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+ | 2.8425 | 1.25 | 1250 | 2.3632 | 0.0 | 0.0 | 18.998 |
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+ | 2.865 | 1.3 | 1300 | 2.3544 | 0.0 | 0.0 | 18.998 |
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+ | 2.8245 | 1.35 | 1350 | 2.3440 | 0.0 | 0.0 | 18.998 |
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+ | 2.8208 | 1.4 | 1400 | 2.3373 | 0.0 | 0.0 | 18.998 |
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+ | 2.8397 | 1.45 | 1450 | 2.3286 | 0.0 | 0.0 | 18.998 |
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+ | 2.8103 | 1.5 | 1500 | 2.3203 | 0.0 | 0.0 | 18.998 |
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+ | 2.82 | 1.55 | 1550 | 2.3133 | 0.0 | 0.0 | 18.9995 |
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+ | 2.7653 | 1.6 | 1600 | 2.3058 | 0.0 | 0.0 | 18.998 |
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+ | 2.7945 | 1.65 | 1650 | 2.2998 | 0.0 | 0.0 | 18.998 |
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+ | 2.7758 | 1.7 | 1700 | 2.2940 | 0.0001 | 0.0 | 18.998 |
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+ | 2.8035 | 1.75 | 1750 | 2.2886 | 0.0001 | 0.0 | 18.998 |
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+ | 2.8045 | 1.8 | 1800 | 2.2827 | 0.0001 | 0.0 | 18.998 |
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+ | 2.7667 | 1.85 | 1850 | 2.2777 | 0.0001 | 0.0 | 18.9965 |
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+ | 2.7792 | 1.9 | 1900 | 2.2730 | 0.0001 | 0.0 | 18.9965 |
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+ | 2.7337 | 1.95 | 1950 | 2.2683 | 0.0001 | 0.0 | 18.9965 |
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+ | 2.7634 | 2.0 | 2000 | 2.2647 | 0.0001 | 0.0 | 18.9965 |
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+ | 2.7357 | 2.05 | 2050 | 2.2607 | 0.0001 | 0.0 | 18.998 |
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+ | 2.7261 | 2.1 | 2100 | 2.2569 | 0.0001 | 0.0 | 18.998 |
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+ | 2.7827 | 2.15 | 2150 | 2.2539 | 0.0001 | 0.0 | 18.998 |
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+ | 2.7363 | 2.2 | 2200 | 2.2509 | 0.0001 | 0.0 | 18.998 |
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+ | 2.7647 | 2.25 | 2250 | 2.2480 | 0.0001 | 0.0 | 18.998 |
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+ | 2.737 | 2.3 | 2300 | 2.2449 | 0.0001 | 0.0 | 18.998 |
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+ | 2.72 | 2.35 | 2350 | 2.2422 | 0.0001 | 0.0 | 18.998 |
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+ | 2.7312 | 2.4 | 2400 | 2.2403 | 0.0001 | 0.0 | 18.998 |
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+ | 2.7345 | 2.45 | 2450 | 2.2382 | 0.0001 | 0.0 | 18.998 |
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+ | 2.6951 | 2.5 | 2500 | 2.2363 | 0.0001 | 0.0 | 18.998 |
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+ | 2.7591 | 2.55 | 2550 | 2.2349 | 0.0001 | 0.0 | 18.998 |
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+ | 2.7018 | 2.6 | 2600 | 2.2333 | 0.0001 | 0.0 | 18.998 |
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+ | 2.6993 | 2.65 | 2650 | 2.2321 | 0.0001 | 0.0 | 18.998 |
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+ | 2.7006 | 2.7 | 2700 | 2.2309 | 0.0001 | 0.0 | 18.998 |
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+ | 2.6694 | 2.75 | 2750 | 2.2296 | 0.0001 | 0.0 | 18.998 |
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+ | 2.7018 | 2.8 | 2800 | 2.2287 | 0.0001 | 0.0 | 18.998 |
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+ | 2.7285 | 2.85 | 2850 | 2.2281 | 0.0001 | 0.0 | 18.998 |
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+ | 2.6873 | 2.9 | 2900 | 2.2276 | 0.0001 | 0.0 | 18.997 |
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+ | 2.7121 | 2.95 | 2950 | 2.2273 | 0.0001 | 0.0 | 18.997 |
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+ | 2.7176 | 3.0 | 3000 | 2.2271 | 0.0001 | 0.0 | 18.997 |
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  ### Framework versions