Hugging Face's logo

CLARA-MeD
/
flan-t5-base

Text2Text Generation
Transformers
PyTorch
TensorBoard
t5
simplification
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Metrics Community
1
joheras commited on
Commit
d699e0f
·
1 Parent(s): 7d8e90a

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +35 -35
README.md CHANGED
@@ -17,11 +17,11 @@ should probably proofread and complete it, then remove this comment. -->
17
 
18
  This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
19
  It achieves the following results on the evaluation set:
20
- - Loss: 1.2902
21
- - Rouge1: 28.2733
22
- - Rouge2: 15.323
23
- - Rougel: 26.1421
24
- - Rougelsum: 26.1589
25
 
26
  ## Model description
27
 
@@ -52,36 +52,36 @@ The following hyperparameters were used during training:
52
 
53
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
54
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
55
- | No log | 1.0 | 380 | 1.4639 | 26.4167 | 14.2257 | 24.5659 | 24.5948 |
56
- | No log | 2.0 | 760 | 1.3946 | 26.7094 | 14.6358 | 25.0516 | 25.075 |
57
- | 1.6466 | 3.0 | 1140 | 1.3480 | 27.3758 | 14.6821 | 25.5935 | 25.6007 |
58
- | 1.6466 | 4.0 | 1520 | 1.3221 | 28.0769 | 14.9721 | 26.131 | 26.1506 |
59
- | 1.3671 | 5.0 | 1900 | 1.2988 | 27.8019 | 14.9244 | 25.8242 | 25.8322 |
60
- | 1.3671 | 6.0 | 2280 | 1.2965 | 27.9071 | 15.3235 | 26.1385 | 26.104 |
61
- | 1.3671 | 7.0 | 2660 | 1.2802 | 28.1866 | 15.4793 | 26.301 | 26.3031 |
62
- | 1.2248 | 8.0 | 3040 | 1.2733 | 27.9974 | 15.4379 | 26.1087 | 26.1159 |
63
- | 1.2248 | 9.0 | 3420 | 1.2591 | 28.2545 | 15.5006 | 26.2812 | 26.3306 |
64
- | 1.1155 | 10.0 | 3800 | 1.2609 | 27.8029 | 15.0837 | 25.7989 | 25.8486 |
65
- | 1.1155 | 11.0 | 4180 | 1.2612 | 27.676 | 15.0786 | 25.6261 | 25.6458 |
66
- | 1.1155 | 12.0 | 4560 | 1.2616 | 27.6811 | 15.0935 | 25.6905 | 25.7125 |
67
- | 1.0337 | 13.0 | 4940 | 1.2562 | 27.88 | 15.2395 | 25.8875 | 25.8988 |
68
- | 1.0337 | 14.0 | 5320 | 1.2624 | 27.9858 | 15.2151 | 25.9785 | 26.0226 |
69
- | 0.9784 | 15.0 | 5700 | 1.2674 | 28.044 | 15.1312 | 25.8866 | 25.9514 |
70
- | 0.9784 | 16.0 | 6080 | 1.2588 | 28.1022 | 15.3599 | 26.0641 | 26.0762 |
71
- | 0.9784 | 17.0 | 6460 | 1.2676 | 27.864 | 15.1432 | 25.8981 | 25.9221 |
72
- | 0.9246 | 18.0 | 6840 | 1.2620 | 27.8826 | 15.1457 | 25.8041 | 25.8971 |
73
- | 0.9246 | 19.0 | 7220 | 1.2671 | 27.965 | 15.0059 | 25.94 | 25.9831 |
74
- | 0.8891 | 20.0 | 7600 | 1.2733 | 28.3035 | 15.3041 | 26.2411 | 26.2723 |
75
- | 0.8891 | 21.0 | 7980 | 1.2748 | 28.5205 | 15.4851 | 26.4543 | 26.4725 |
76
- | 0.8891 | 22.0 | 8360 | 1.2793 | 28.3018 | 15.3251 | 26.2781 | 26.3203 |
77
- | 0.8578 | 23.0 | 8740 | 1.2788 | 28.039 | 15.238 | 25.9371 | 25.9856 |
78
- | 0.8578 | 24.0 | 9120 | 1.2901 | 28.3312 | 15.3396 | 26.1722 | 26.1993 |
79
- | 0.8299 | 25.0 | 9500 | 1.2863 | 28.0727 | 15.0182 | 25.91 | 25.9577 |
80
- | 0.8299 | 26.0 | 9880 | 1.2845 | 28.1828 | 15.1338 | 26.039 | 26.0493 |
81
- | 0.8299 | 27.0 | 10260 | 1.2819 | 28.1547 | 15.091 | 26.0256 | 26.0346 |
82
- | 0.8137 | 28.0 | 10640 | 1.2859 | 28.2203 | 15.3225 | 26.1493 | 26.1591 |
83
- | 0.8137 | 29.0 | 11020 | 1.2902 | 28.2459 | 15.3142 | 26.1283 | 26.1382 |
84
- | 0.8061 | 30.0 | 11400 | 1.2902 | 28.2733 | 15.323 | 26.1421 | 26.1589 |
85
 
86
 
87
  ### Framework versions
 
17
 
18
  This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
19
  It achieves the following results on the evaluation set:
20
+ - Loss: 1.2699
21
+ - Rouge1: 30.1376
22
+ - Rouge2: 16.8424
23
+ - Rougel: 27.9649
24
+ - Rougelsum: 27.9946
25
 
26
  ## Model description
27
 
 
52
 
53
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
54
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
55
+ | No log | 1.0 | 380 | 1.4710 | 27.6278 | 15.5057 | 25.9917 | 26.0601 |
56
+ | No log | 2.0 | 760 | 1.3863 | 28.4324 | 15.8032 | 26.8023 | 26.8387 |
57
+ | 1.6476 | 3.0 | 1140 | 1.3494 | 28.6807 | 16.0854 | 26.9253 | 26.9743 |
58
+ | 1.6476 | 4.0 | 1520 | 1.3170 | 28.3434 | 15.6852 | 26.58 | 26.5937 |
59
+ | 1.3695 | 5.0 | 1900 | 1.3009 | 28.8006 | 15.819 | 26.8122 | 26.8756 |
60
+ | 1.3695 | 6.0 | 2280 | 1.2797 | 29.0521 | 16.4032 | 27.1802 | 27.1988 |
61
+ | 1.3695 | 7.0 | 2660 | 1.2744 | 29.2339 | 16.4583 | 27.3799 | 27.4091 |
62
+ | 1.2162 | 8.0 | 3040 | 1.2557 | 28.8177 | 16.2513 | 26.9967 | 27.028 |
63
+ | 1.2162 | 9.0 | 3420 | 1.2553 | 29.0411 | 16.4606 | 27.2912 | 27.3004 |
64
+ | 1.1232 | 10.0 | 3800 | 1.2540 | 29.0367 | 16.3896 | 27.2911 | 27.324 |
65
+ | 1.1232 | 11.0 | 4180 | 1.2500 | 29.3928 | 16.6718 | 27.4638 | 27.4877 |
66
+ | 1.1232 | 12.0 | 4560 | 1.2487 | 29.6046 | 16.7906 | 27.6814 | 27.6977 |
67
+ | 1.0389 | 13.0 | 4940 | 1.2542 | 29.4922 | 16.5255 | 27.5363 | 27.5904 |
68
+ | 1.0389 | 14.0 | 5320 | 1.2384 | 29.6472 | 16.707 | 27.6808 | 27.6988 |
69
+ | 0.9794 | 15.0 | 5700 | 1.2476 | 29.3771 | 16.2381 | 27.3751 | 27.3876 |
70
+ | 0.9794 | 16.0 | 6080 | 1.2437 | 29.4158 | 16.4003 | 27.3116 | 27.3409 |
71
+ | 0.9794 | 17.0 | 6460 | 1.2466 | 29.2787 | 16.4136 | 27.3256 | 27.3622 |
72
+ | 0.9276 | 18.0 | 6840 | 1.2530 | 29.4183 | 16.4244 | 27.325 | 27.3583 |
73
+ | 0.9276 | 19.0 | 7220 | 1.2582 | 29.743 | 16.7631 | 27.6997 | 27.7752 |
74
+ | 0.8851 | 20.0 | 7600 | 1.2560 | 29.5645 | 16.5834 | 27.5395 | 27.5622 |
75
+ | 0.8851 | 21.0 | 7980 | 1.2544 | 29.4893 | 16.4478 | 27.3961 | 27.4465 |
76
+ | 0.8851 | 22.0 | 8360 | 1.2593 | 29.785 | 16.6023 | 27.6214 | 27.6394 |
77
+ | 0.8578 | 23.0 | 8740 | 1.2588 | 30.008 | 16.8796 | 27.882 | 27.8989 |
78
+ | 0.8578 | 24.0 | 9120 | 1.2672 | 30.0112 | 16.6782 | 27.8556 | 27.8934 |
79
+ | 0.8347 | 25.0 | 9500 | 1.2668 | 29.6945 | 16.431 | 27.4398 | 27.4956 |
80
+ | 0.8347 | 26.0 | 9880 | 1.2642 | 29.9327 | 16.6105 | 27.798 | 27.8497 |
81
+ | 0.8347 | 27.0 | 10260 | 1.2674 | 30.0747 | 16.7768 | 27.9137 | 27.9609 |
82
+ | 0.8156 | 28.0 | 10640 | 1.2712 | 29.9504 | 16.6466 | 27.8371 | 27.8742 |
83
+ | 0.8156 | 29.0 | 11020 | 1.2692 | 30.2209 | 16.9038 | 28.0454 | 28.0982 |
84
+ | 0.8055 | 30.0 | 11400 | 1.2699 | 30.1376 | 16.8424 | 27.9649 | 27.9946 |
85
 
86
 
87
  ### Framework versions