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mt5-summarize-tr

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metadata
license: apache-2.0
base_model: google/mt5-small
tags:
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: mt5-summarize-tr
    results: []

mt5-summarize-tr

This model is a fine-tuned version of google/mt5-small on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 2.0312
  • Rouge1: 0.3697
  • Rouge2: 0.2183
  • Rougel: 0.3248
  • Rougelsum: 0.3253

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0005
  • train_batch_size: 2
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 90
  • num_epochs: 10

Training results

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

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.0
  • Tokenizers 0.15.0