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mt5-summarize-tr

Text2Text Generation
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  1. README.md +149 -0
  2. generation_config.json +10 -0
  3. model.safetensors +1 -1
  4. runs/Dec29_11-43-58_f4948017d950/events.out.tfevents.1703850242.f4948017d950.239.0 +2 -2
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/mt5-small
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - rouge
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+ model-index:
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+ - name: mt5-summarize-tr
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+ results: []
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+ ---
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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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+
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+ # mt5-summarize-tr
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+
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+ This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.0312
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+ - Rouge1: 0.3697
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+ - Rouge2: 0.2183
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+ - Rougel: 0.3248
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+ - Rougelsum: 0.3253
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0005
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+ - train_batch_size: 2
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - gradient_accumulation_steps: 16
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+ - total_train_batch_size: 32
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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: 90
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+ - num_epochs: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
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+ | 4.125 | 0.12 | 100 | 3.0694 | 0.2411 | 0.1271 | 0.2143 | 0.2131 |
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+ | 3.5224 | 0.24 | 200 | 2.7433 | 0.3152 | 0.1669 | 0.2738 | 0.2743 |
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+ | 3.2845 | 0.35 | 300 | 2.5655 | 0.3254 | 0.1738 | 0.2870 | 0.2863 |
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+ | 3.1946 | 0.47 | 400 | 2.5012 | 0.3080 | 0.1659 | 0.2795 | 0.2798 |
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+ | 3.2075 | 0.59 | 500 | 2.4685 | 0.3488 | 0.2036 | 0.3117 | 0.3120 |
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+ | 3.0507 | 0.71 | 600 | 2.4249 | 0.3337 | 0.1947 | 0.2900 | 0.2898 |
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+ | 3.0301 | 0.82 | 700 | 2.4027 | 0.3396 | 0.1949 | 0.2973 | 0.2987 |
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+ | 3.032 | 0.94 | 800 | 2.3871 | 0.3165 | 0.1789 | 0.2777 | 0.2769 |
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+ | 2.8716 | 1.06 | 900 | 2.3811 | 0.3446 | 0.2113 | 0.3104 | 0.3093 |
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+ | 2.8563 | 1.18 | 1000 | 2.3448 | 0.3603 | 0.2248 | 0.3251 | 0.3247 |
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+ | 2.8408 | 1.3 | 1100 | 2.3438 | 0.3387 | 0.1945 | 0.2960 | 0.2963 |
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+ | 2.7755 | 1.41 | 1200 | 2.3275 | 0.3473 | 0.2038 | 0.2910 | 0.2920 |
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+ | 2.8019 | 1.53 | 1300 | 2.2786 | 0.3438 | 0.2021 | 0.3029 | 0.3022 |
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+ | 2.7457 | 1.65 | 1400 | 2.2380 | 0.3308 | 0.1887 | 0.2985 | 0.2985 |
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+ | 2.7216 | 1.77 | 1500 | 2.2517 | 0.3515 | 0.2112 | 0.3079 | 0.3052 |
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+ | 2.71 | 1.88 | 1600 | 2.2712 | 0.3397 | 0.1978 | 0.2955 | 0.2932 |
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+ | 2.6489 | 2.0 | 1700 | 2.2678 | 0.3441 | 0.2059 | 0.3047 | 0.3049 |
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+ | 2.6232 | 2.12 | 1800 | 2.2526 | 0.3242 | 0.1901 | 0.2874 | 0.2864 |
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+ | 2.5419 | 2.24 | 1900 | 2.2526 | 0.2985 | 0.1636 | 0.2611 | 0.2610 |
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+ | 2.6232 | 2.36 | 2000 | 2.2108 | 0.3454 | 0.2053 | 0.2995 | 0.2999 |
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+ | 2.5973 | 2.47 | 2100 | 2.2166 | 0.3363 | 0.1986 | 0.2975 | 0.2968 |
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+ | 2.5738 | 2.59 | 2200 | 2.1973 | 0.3263 | 0.1802 | 0.2803 | 0.2812 |
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+ | 2.5279 | 2.71 | 2300 | 2.1978 | 0.3186 | 0.1778 | 0.2829 | 0.2857 |
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+ | 2.5613 | 2.83 | 2400 | 2.1921 | 0.3539 | 0.2126 | 0.3125 | 0.3166 |
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+ | 2.5856 | 2.94 | 2500 | 2.1830 | 0.3719 | 0.2293 | 0.3295 | 0.3307 |
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+ | 2.5181 | 3.06 | 2600 | 2.1531 | 0.3581 | 0.2177 | 0.3245 | 0.3239 |
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+ | 2.4885 | 3.18 | 2700 | 2.1702 | 0.3409 | 0.1980 | 0.3034 | 0.3051 |
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+ | 2.4172 | 3.3 | 2800 | 2.1562 | 0.3387 | 0.1872 | 0.2927 | 0.2925 |
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+ | 2.5102 | 3.41 | 2900 | 2.1398 | 0.3387 | 0.2004 | 0.2947 | 0.2944 |
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+ | 2.5086 | 3.53 | 3000 | 2.1253 | 0.3453 | 0.1935 | 0.3031 | 0.3029 |
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+ | 2.4235 | 3.65 | 3100 | 2.1530 | 0.3495 | 0.1990 | 0.2932 | 0.2936 |
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+ | 2.364 | 3.77 | 3200 | 2.1215 | 0.3564 | 0.2057 | 0.3049 | 0.3072 |
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+ | 2.4752 | 3.89 | 3300 | 2.1223 | 0.3250 | 0.1818 | 0.2764 | 0.2772 |
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+ | 2.3531 | 4.0 | 3400 | 2.1100 | 0.3453 | 0.1945 | 0.2931 | 0.2943 |
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+ | 2.3746 | 4.12 | 3500 | 2.1162 | 0.3494 | 0.1976 | 0.2991 | 0.2998 |
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+ | 2.3519 | 4.24 | 3600 | 2.1069 | 0.3496 | 0.2118 | 0.3061 | 0.3052 |
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+ | 2.3154 | 4.36 | 3700 | 2.1217 | 0.3544 | 0.2170 | 0.3257 | 0.3269 |
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+ | 2.3163 | 4.47 | 3800 | 2.1092 | 0.3445 | 0.2007 | 0.3044 | 0.3045 |
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+ | 2.3641 | 4.59 | 3900 | 2.1098 | 0.3484 | 0.2072 | 0.3127 | 0.3126 |
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+ | 2.298 | 4.71 | 4000 | 2.1019 | 0.3571 | 0.2190 | 0.3211 | 0.3224 |
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+ | 2.3414 | 4.83 | 4100 | 2.0955 | 0.3534 | 0.2002 | 0.2949 | 0.2957 |
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+ | 2.3812 | 4.95 | 4200 | 2.1027 | 0.3531 | 0.2058 | 0.3009 | 0.3011 |
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+ | 2.2614 | 5.06 | 4300 | 2.1107 | 0.3379 | 0.1902 | 0.2891 | 0.2886 |
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+ | 2.2528 | 5.18 | 4400 | 2.0953 | 0.3789 | 0.2229 | 0.3263 | 0.3243 |
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+ | 2.3287 | 5.3 | 4500 | 2.0977 | 0.3671 | 0.2120 | 0.3200 | 0.3180 |
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+ | 2.2855 | 5.42 | 4600 | 2.1044 | 0.3567 | 0.2085 | 0.3030 | 0.3057 |
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+ | 2.2969 | 5.53 | 4700 | 2.0925 | 0.3459 | 0.2103 | 0.3126 | 0.3119 |
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+ | 2.2438 | 5.65 | 4800 | 2.0879 | 0.3577 | 0.2020 | 0.2999 | 0.2996 |
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+ | 2.2088 | 5.77 | 4900 | 2.0862 | 0.3662 | 0.2212 | 0.3222 | 0.3259 |
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+ | 2.2319 | 5.89 | 5000 | 2.0839 | 0.3530 | 0.2040 | 0.3108 | 0.3130 |
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+ | 2.2879 | 6.01 | 5100 | 2.0761 | 0.3693 | 0.2236 | 0.3285 | 0.3288 |
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+ | 2.2508 | 6.12 | 5200 | 2.0810 | 0.3924 | 0.2424 | 0.3440 | 0.3448 |
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+ | 2.2634 | 6.24 | 5300 | 2.0760 | 0.3722 | 0.2170 | 0.3162 | 0.3165 |
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+ | 2.1484 | 6.36 | 5400 | 2.0725 | 0.3624 | 0.2110 | 0.3097 | 0.3115 |
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+ | 2.1863 | 6.48 | 5500 | 2.0663 | 0.3820 | 0.2332 | 0.3338 | 0.3348 |
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+ | 2.1935 | 6.59 | 5600 | 2.0762 | 0.3906 | 0.2299 | 0.3421 | 0.3425 |
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+ | 2.224 | 6.71 | 5700 | 2.0536 | 0.3788 | 0.2362 | 0.3337 | 0.3336 |
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+ | 2.1938 | 6.83 | 5800 | 2.0663 | 0.3551 | 0.2017 | 0.3047 | 0.3044 |
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+ | 2.2589 | 6.95 | 5900 | 2.0602 | 0.3782 | 0.2298 | 0.3365 | 0.3360 |
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+ | 2.1185 | 7.07 | 6000 | 2.0632 | 0.3617 | 0.2170 | 0.3237 | 0.3239 |
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+ | 2.1924 | 7.18 | 6100 | 2.0508 | 0.3771 | 0.2211 | 0.3284 | 0.3295 |
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+ | 2.2012 | 7.3 | 6200 | 2.0602 | 0.3714 | 0.2184 | 0.3315 | 0.3315 |
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+ | 2.1617 | 7.42 | 6300 | 2.0606 | 0.3311 | 0.1800 | 0.2883 | 0.2862 |
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+ | 2.1557 | 7.54 | 6400 | 2.0444 | 0.3827 | 0.2318 | 0.3386 | 0.3383 |
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+ | 2.1965 | 7.65 | 6500 | 2.0391 | 0.3777 | 0.2334 | 0.3401 | 0.3414 |
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+ | 2.1599 | 7.77 | 6600 | 2.0393 | 0.3648 | 0.2121 | 0.3154 | 0.3152 |
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+ | 2.1505 | 7.89 | 6700 | 2.0483 | 0.3905 | 0.2411 | 0.3485 | 0.3496 |
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+ | 2.191 | 8.01 | 6800 | 2.0412 | 0.3815 | 0.2373 | 0.3327 | 0.3337 |
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+ | 2.1513 | 8.12 | 6900 | 2.0461 | 0.3745 | 0.2252 | 0.3363 | 0.3374 |
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+ | 2.1232 | 8.24 | 7000 | 2.0474 | 0.3672 | 0.2183 | 0.3264 | 0.3270 |
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+ | 2.1249 | 8.36 | 7100 | 2.0422 | 0.3846 | 0.2270 | 0.3379 | 0.3388 |
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+ | 2.1743 | 8.48 | 7200 | 2.0358 | 0.3707 | 0.2086 | 0.3192 | 0.3184 |
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+ | 2.1684 | 8.6 | 7300 | 2.0391 | 0.3532 | 0.2061 | 0.3048 | 0.3035 |
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+ | 2.1257 | 8.71 | 7400 | 2.0388 | 0.3773 | 0.2198 | 0.3218 | 0.3219 |
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+ | 2.1186 | 8.83 | 7500 | 2.0356 | 0.3655 | 0.2135 | 0.3091 | 0.3077 |
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+ | 2.054 | 8.95 | 7600 | 2.0308 | 0.3739 | 0.2276 | 0.3208 | 0.3208 |
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+ | 2.1534 | 9.07 | 7700 | 2.0253 | 0.3649 | 0.2150 | 0.3154 | 0.3157 |
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+ | 2.0906 | 9.18 | 7800 | 2.0327 | 0.3752 | 0.2233 | 0.3173 | 0.3174 |
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+ | 2.0703 | 9.3 | 7900 | 2.0314 | 0.3610 | 0.2100 | 0.3153 | 0.3150 |
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+ | 2.1088 | 9.42 | 8000 | 2.0289 | 0.3636 | 0.2106 | 0.3171 | 0.3174 |
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+ | 2.0719 | 9.54 | 8100 | 2.0298 | 0.3682 | 0.2145 | 0.3225 | 0.3223 |
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+ | 2.0873 | 9.66 | 8200 | 2.0294 | 0.3729 | 0.2185 | 0.3225 | 0.3228 |
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+ | 2.0953 | 9.77 | 8300 | 2.0316 | 0.3729 | 0.2185 | 0.3225 | 0.3228 |
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+ | 2.0825 | 9.89 | 8400 | 2.0312 | 0.3697 | 0.2183 | 0.3248 | 0.3253 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.16.0
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+ - Tokenizers 0.15.0
generation_config.json ADDED
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+ "decoder_start_token_id": 0,
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+ "length_penalty": 0.6,
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+ "max_length": 128,
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+ "no_repeat_ngram_size": 2,
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+ "num_beams": 15,
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+ "pad_token_id": 0,
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+ "transformers_version": "4.35.2"
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+ }
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