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t5-vietnamese-summarization

Summarization
Transformers
PyTorch
Vietnamese
t5
text2text-generation
text-generation-inference
Inference Endpoints
Model card Files Community
3
pengold commited on
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End of training

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@@ -1,10 +1,9 @@
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  ---
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  license: apache-2.0
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- base_model: t5-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: t5-vietnamese-summarization
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  results: []
@@ -15,14 +14,7 @@ should probably proofread and complete it, then remove this comment. -->
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  # t5-vietnamese-summarization
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- This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
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- It achieves the following results on the evaluation set:
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- - Loss: 5.2561
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- - Rouge1: 0.4601
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- - Rouge2: 0.1574
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- - Rougel: 0.2977
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- - Rougelsum: 0.2978
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- - Gen Len: 18.806
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  ## Model description
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@@ -47,99 +39,13 @@ The following hyperparameters were used during training:
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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: 100
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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- |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
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- | 6.9262 | 1.0 | 1250 | 6.6039 | 0.4025 | 0.1122 | 0.2573 | 0.2576 | 18.738 |
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- | 6.6661 | 2.0 | 2500 | 6.4575 | 0.4067 | 0.1184 | 0.2611 | 0.2617 | 18.868 |
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- | 6.5642 | 3.0 | 3750 | 6.3785 | 0.4176 | 0.1265 | 0.2722 | 0.2723 | 18.724 |
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- | 6.4586 | 4.0 | 5000 | 6.3006 | 0.4204 | 0.1286 | 0.2739 | 0.274 | 18.694 |
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- | 6.4089 | 5.0 | 6250 | 6.2724 | 0.426 | 0.1321 | 0.275 | 0.2752 | 18.784 |
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- | 6.328 | 6.0 | 7500 | 6.2053 | 0.4435 | 0.1409 | 0.2872 | 0.2873 | 18.818 |
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- | 6.2862 | 7.0 | 8750 | 6.1546 | 0.4397 | 0.1401 | 0.2845 | 0.2849 | 18.83 |
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- | 6.2394 | 8.0 | 10000 | 6.1136 | 0.443 | 0.1427 | 0.287 | 0.2874 | 18.816 |
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- | 6.2024 | 9.0 | 11250 | 6.0772 | 0.4438 | 0.1459 | 0.287 | 0.2874 | 18.858 |
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- | 6.1423 | 10.0 | 12500 | 6.0458 | 0.4455 | 0.1478 | 0.2891 | 0.2895 | 18.832 |
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- | 6.1265 | 11.0 | 13750 | 6.0011 | 0.4496 | 0.1474 | 0.2896 | 0.29 | 18.858 |
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- | 6.0642 | 12.0 | 15000 | 5.9603 | 0.4524 | 0.1488 | 0.2936 | 0.2942 | 18.884 |
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- | 6.0457 | 13.0 | 16250 | 5.9340 | 0.4479 | 0.1484 | 0.2903 | 0.2907 | 18.926 |
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- | 6.0059 | 14.0 | 17500 | 5.8934 | 0.4484 | 0.1458 | 0.2905 | 0.2908 | 18.892 |
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- | 5.9558 | 15.0 | 18750 | 5.8688 | 0.4506 | 0.1501 | 0.2917 | 0.292 | 18.896 |
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- | 5.9421 | 16.0 | 20000 | 5.8424 | 0.4502 | 0.1458 | 0.2863 | 0.2868 | 18.824 |
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- | 5.9049 | 17.0 | 21250 | 5.8208 | 0.448 | 0.1482 | 0.2889 | 0.2894 | 18.844 |
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- | 5.8713 | 18.0 | 22500 | 5.8003 | 0.449 | 0.1473 | 0.2892 | 0.2895 | 18.868 |
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- | 5.8481 | 19.0 | 23750 | 5.7704 | 0.4484 | 0.1486 | 0.2878 | 0.2883 | 18.87 |
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- | 5.8113 | 20.0 | 25000 | 5.7443 | 0.4513 | 0.152 | 0.2914 | 0.2919 | 18.822 |
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- | 5.7948 | 21.0 | 26250 | 5.7222 | 0.4485 | 0.1477 | 0.2894 | 0.2898 | 18.794 |
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- | 5.7728 | 22.0 | 27500 | 5.6990 | 0.4476 | 0.1506 | 0.289 | 0.2892 | 18.82 |
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- | 5.7476 | 23.0 | 28750 | 5.6802 | 0.4474 | 0.1493 | 0.2901 | 0.2904 | 18.846 |
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- | 5.7299 | 24.0 | 30000 | 5.6608 | 0.4514 | 0.1549 | 0.2924 | 0.2928 | 18.872 |
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- | 5.7021 | 25.0 | 31250 | 5.6533 | 0.451 | 0.1537 | 0.2921 | 0.2925 | 18.842 |
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- | 5.6861 | 26.0 | 32500 | 5.6371 | 0.4502 | 0.1534 | 0.291 | 0.2915 | 18.826 |
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- | 5.6833 | 27.0 | 33750 | 5.6241 | 0.4541 | 0.1542 | 0.2938 | 0.2941 | 18.876 |
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- | 5.6473 | 28.0 | 35000 | 5.6113 | 0.4509 | 0.1535 | 0.2932 | 0.2935 | 18.83 |
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- | 5.6248 | 29.0 | 36250 | 5.5896 | 0.454 | 0.1562 | 0.2934 | 0.2938 | 18.878 |
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- | 5.6126 | 30.0 | 37500 | 5.5768 | 0.4555 | 0.1563 | 0.2952 | 0.2954 | 18.924 |
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- | 5.6044 | 31.0 | 38750 | 5.5627 | 0.4526 | 0.1547 | 0.2929 | 0.2932 | 18.856 |
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- | 5.58 | 32.0 | 40000 | 5.5459 | 0.4482 | 0.1523 | 0.291 | 0.2914 | 18.898 |
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- | 5.5621 | 33.0 | 41250 | 5.5345 | 0.4524 | 0.1546 | 0.2936 | 0.294 | 18.88 |
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- | 5.5399 | 34.0 | 42500 | 5.5209 | 0.4554 | 0.1554 | 0.2939 | 0.2942 | 18.868 |
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- | 5.5272 | 35.0 | 43750 | 5.5011 | 0.4512 | 0.1562 | 0.2928 | 0.2931 | 18.858 |
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- | 5.5276 | 36.0 | 45000 | 5.5009 | 0.4504 | 0.1548 | 0.2926 | 0.2931 | 18.846 |
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- | 5.5355 | 37.0 | 46250 | 5.4912 | 0.4538 | 0.1552 | 0.2932 | 0.2936 | 18.874 |
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- | 5.4894 | 38.0 | 47500 | 5.4792 | 0.455 | 0.1591 | 0.2932 | 0.2937 | 18.872 |
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- | 5.4872 | 39.0 | 48750 | 5.4692 | 0.4558 | 0.1556 | 0.2918 | 0.2923 | 18.864 |
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- | 5.4716 | 40.0 | 50000 | 5.4585 | 0.4564 | 0.159 | 0.2964 | 0.2966 | 18.844 |
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- | 5.4461 | 41.0 | 51250 | 5.4532 | 0.4591 | 0.1604 | 0.2961 | 0.2964 | 18.85 |
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- | 5.4423 | 42.0 | 52500 | 5.4420 | 0.4557 | 0.1577 | 0.295 | 0.2952 | 18.862 |
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- | 5.4259 | 43.0 | 53750 | 5.4341 | 0.4534 | 0.1565 | 0.2929 | 0.2929 | 18.83 |
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- | 5.4125 | 44.0 | 55000 | 5.4303 | 0.4543 | 0.1579 | 0.2935 | 0.2936 | 18.854 |
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- | 5.4101 | 45.0 | 56250 | 5.4062 | 0.457 | 0.1594 | 0.2945 | 0.2948 | 18.836 |
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- | 5.4027 | 46.0 | 57500 | 5.4094 | 0.4539 | 0.1553 | 0.2934 | 0.2937 | 18.822 |
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- | 5.3947 | 47.0 | 58750 | 5.4018 | 0.4567 | 0.1555 | 0.2944 | 0.2949 | 18.79 |
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- | 5.3905 | 48.0 | 60000 | 5.4001 | 0.4557 | 0.1554 | 0.295 | 0.2952 | 18.802 |
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- | 5.3798 | 49.0 | 61250 | 5.3843 | 0.4549 | 0.156 | 0.2949 | 0.2952 | 18.818 |
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- | 5.3556 | 50.0 | 62500 | 5.3866 | 0.4578 | 0.1581 | 0.2948 | 0.2951 | 18.828 |
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- | 5.3796 | 51.0 | 63750 | 5.3794 | 0.4564 | 0.1577 | 0.2963 | 0.2967 | 18.81 |
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- | 5.341 | 52.0 | 65000 | 5.3720 | 0.4573 | 0.1578 | 0.2959 | 0.2964 | 18.796 |
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- | 5.3461 | 53.0 | 66250 | 5.3592 | 0.4579 | 0.1571 | 0.2955 | 0.2956 | 18.812 |
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- | 5.3385 | 54.0 | 67500 | 5.3622 | 0.4567 | 0.1562 | 0.2954 | 0.2957 | 18.756 |
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- | 5.3163 | 55.0 | 68750 | 5.3548 | 0.4591 | 0.155 | 0.2956 | 0.2959 | 18.824 |
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- | 5.3222 | 56.0 | 70000 | 5.3542 | 0.4585 | 0.1564 | 0.2955 | 0.2959 | 18.836 |
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- | 5.3232 | 57.0 | 71250 | 5.3478 | 0.4577 | 0.1567 | 0.2959 | 0.2961 | 18.82 |
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- | 5.2974 | 58.0 | 72500 | 5.3366 | 0.4538 | 0.1545 | 0.2932 | 0.2934 | 18.81 |
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- | 5.284 | 59.0 | 73750 | 5.3386 | 0.4578 | 0.1557 | 0.2955 | 0.2959 | 18.79 |
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- | 5.3004 | 60.0 | 75000 | 5.3349 | 0.4569 | 0.1568 | 0.2957 | 0.2959 | 18.794 |
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- | 5.259 | 61.0 | 76250 | 5.3238 | 0.4607 | 0.1566 | 0.2987 | 0.2991 | 18.822 |
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- | 5.2885 | 62.0 | 77500 | 5.3232 | 0.4607 | 0.1591 | 0.2981 | 0.2986 | 18.81 |
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- | 5.2857 | 63.0 | 78750 | 5.3139 | 0.4594 | 0.1574 | 0.2957 | 0.2959 | 18.77 |
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- | 5.274 | 64.0 | 80000 | 5.3202 | 0.4601 | 0.1558 | 0.2971 | 0.2972 | 18.824 |
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- | 5.2665 | 65.0 | 81250 | 5.3123 | 0.4599 | 0.1575 | 0.2967 | 0.2969 | 18.884 |
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- | 5.2482 | 66.0 | 82500 | 5.3004 | 0.4601 | 0.1572 | 0.2985 | 0.2985 | 18.812 |
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- | 5.2429 | 67.0 | 83750 | 5.2976 | 0.4572 | 0.1535 | 0.2957 | 0.2958 | 18.792 |
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- | 5.2407 | 68.0 | 85000 | 5.2985 | 0.4591 | 0.1581 | 0.2966 | 0.2966 | 18.828 |
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- | 5.2371 | 69.0 | 86250 | 5.2896 | 0.4604 | 0.1584 | 0.2982 | 0.2984 | 18.828 |
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- | 5.2341 | 70.0 | 87500 | 5.2917 | 0.4612 | 0.1605 | 0.2988 | 0.2991 | 18.83 |
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- | 5.2311 | 71.0 | 88750 | 5.2882 | 0.4594 | 0.1574 | 0.2977 | 0.298 | 18.778 |
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- | 5.2395 | 72.0 | 90000 | 5.2811 | 0.4609 | 0.1587 | 0.2974 | 0.2975 | 18.854 |
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- | 5.2187 | 73.0 | 91250 | 5.2836 | 0.4606 | 0.1599 | 0.2986 | 0.2986 | 18.804 |
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- | 5.2158 | 74.0 | 92500 | 5.2781 | 0.4603 | 0.1584 | 0.2974 | 0.2976 | 18.8 |
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- | 5.2153 | 75.0 | 93750 | 5.2802 | 0.4603 | 0.1577 | 0.2973 | 0.2975 | 18.806 |
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- | 5.2153 | 76.0 | 95000 | 5.2771 | 0.4596 | 0.1563 | 0.2954 | 0.2957 | 18.816 |
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- | 5.1939 | 77.0 | 96250 | 5.2771 | 0.4594 | 0.1566 | 0.2966 | 0.2968 | 18.828 |
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- | 5.2215 | 78.0 | 97500 | 5.2725 | 0.4578 | 0.1576 | 0.2973 | 0.2975 | 18.78 |
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- | 5.1974 | 79.0 | 98750 | 5.2704 | 0.458 | 0.1578 | 0.2974 | 0.2976 | 18.77 |
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- | 5.2068 | 80.0 | 100000 | 5.2657 | 0.4612 | 0.1575 | 0.2991 | 0.2994 | 18.786 |
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- | 5.2018 | 81.0 | 101250 | 5.2643 | 0.4592 | 0.157 | 0.2971 | 0.2971 | 18.812 |
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- | 5.21 | 82.0 | 102500 | 5.2608 | 0.459 | 0.1577 | 0.2979 | 0.2979 | 18.792 |
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- | 5.2032 | 83.0 | 103750 | 5.2612 | 0.4604 | 0.1574 | 0.2978 | 0.298 | 18.798 |
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- | 5.1908 | 84.0 | 105000 | 5.2594 | 0.4593 | 0.1587 | 0.2983 | 0.2985 | 18.822 |
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- | 5.194 | 85.0 | 106250 | 5.2548 | 0.4581 | 0.1576 | 0.2981 | 0.2981 | 18.792 |
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- | 5.1821 | 86.0 | 107500 | 5.2580 | 0.459 | 0.1568 | 0.2971 | 0.2971 | 18.802 |
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- | 5.1901 | 87.0 | 108750 | 5.2561 | 0.4601 | 0.1574 | 0.2977 | 0.2978 | 18.806 |
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  ### Framework versions
 
1
  ---
2
  license: apache-2.0
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+ base_model: pengold/t5-vietnamese-summarization
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  tags:
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+ - v1.0.0
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  - generated_from_trainer
 
 
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  model-index:
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  - name: t5-vietnamese-summarization
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  results: []
 
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15
  # t5-vietnamese-summarization
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+ This model is a fine-tuned version of [pengold/t5-vietnamese-summarization](https://huggingface.co/pengold/t5-vietnamese-summarization) on an unknown dataset.
 
 
 
 
 
 
 
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19
  ## Model description
20
 
 
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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: 1
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44
  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
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+ | No log | 1.0 | 63 | 5.2372 | 0.4583 | 0.1595 | 0.2959 | 0.2957 | 18.814 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions