Hugging Face's logo

raquelclemente
/
mt5-teste-full-length

Text2Text Generation
Transformers
PyTorch
TensorBoard
mt5
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
raquelclemente commited on
Commit
92a1739
•
1 Parent(s): f75fd46

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +29 -29
README.md CHANGED
@@ -16,11 +16,11 @@ should probably proofread and complete it, then remove this comment. -->
16
 
17
  This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
18
  It achieves the following results on the evaluation set:
19
- - Loss: 0.5996
20
- - Rouge1: 0.5083
21
- - Rouge2: 0.2820
22
- - Rougel: 0.4095
23
- - Rougelsum: 0.4108
24
 
25
  ## Model description
26
 
@@ -54,30 +54,30 @@ The following hyperparameters were used during training:
54
 
55
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
56
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
57
- | 9.1442 | 0.16 | 100 | 9.7852 | 0.0531 | 0.0 | 0.0524 | 0.0 |
58
- | 1.0643 | 0.33 | 200 | 0.9089 | 0.3623 | 0.1853 | 0.3252 | 0.3261 |
59
- | 0.8283 | 0.49 | 300 | 0.8361 | 0.4184 | 0.2112 | 0.3535 | 0.3548 |
60
- | 0.7754 | 0.65 | 400 | 0.7522 | 0.4407 | 0.2575 | 0.3802 | 0.3828 |
61
- | 0.8012 | 0.82 | 500 | 0.7226 | 0.4643 | 0.2638 | 0.3866 | 0.3866 |
62
- | 0.7758 | 0.98 | 600 | 0.7265 | 0.4624 | 0.2458 | 0.3840 | 0.3847 |
63
- | 0.6744 | 1.15 | 700 | 0.7018 | 0.4477 | 0.2469 | 0.3732 | 0.3741 |
64
- | 0.6636 | 1.31 | 800 | 0.6955 | 0.4786 | 0.2632 | 0.4027 | 0.4038 |
65
- | 0.6839 | 1.47 | 900 | 0.6737 | 0.4773 | 0.2689 | 0.3909 | 0.3898 |
66
- | 0.6264 | 1.64 | 1000 | 0.6504 | 0.4457 | 0.2533 | 0.3747 | 0.3767 |
67
- | 0.6641 | 1.8 | 1100 | 0.6442 | 0.4582 | 0.2428 | 0.3661 | 0.3659 |
68
- | 0.6492 | 1.96 | 1200 | 0.6500 | 0.5004 | 0.2751 | 0.3984 | 0.3993 |
69
- | 0.5823 | 2.13 | 1300 | 0.6344 | 0.4917 | 0.2743 | 0.4000 | 0.4016 |
70
- | 0.5585 | 2.29 | 1400 | 0.6373 | 0.4749 | 0.2490 | 0.3834 | 0.3849 |
71
- | 0.5748 | 2.45 | 1500 | 0.6168 | 0.5036 | 0.2915 | 0.4128 | 0.4145 |
72
- | 0.5452 | 2.62 | 1600 | 0.6135 | 0.5004 | 0.2864 | 0.4038 | 0.4044 |
73
- | 0.5735 | 2.78 | 1700 | 0.6164 | 0.4904 | 0.2689 | 0.4001 | 0.3993 |
74
- | 0.5394 | 2.95 | 1800 | 0.6153 | 0.4864 | 0.2884 | 0.4091 | 0.4089 |
75
- | 0.4816 | 3.11 | 1900 | 0.6070 | 0.5027 | 0.2765 | 0.4042 | 0.4031 |
76
- | 0.5328 | 3.27 | 2000 | 0.6095 | 0.4896 | 0.2783 | 0.4026 | 0.4031 |
77
- | 0.5157 | 3.44 | 2100 | 0.6021 | 0.5165 | 0.2853 | 0.4137 | 0.4145 |
78
- | 0.5295 | 3.6 | 2200 | 0.6063 | 0.4926 | 0.2721 | 0.3965 | 0.3980 |
79
- | 0.5027 | 3.76 | 2300 | 0.6004 | 0.5120 | 0.2885 | 0.4092 | 0.4103 |
80
- | 0.4943 | 3.93 | 2400 | 0.5996 | 0.5083 | 0.2820 | 0.4095 | 0.4108 |
81
 
82
 
83
  ### Framework versions
 
16
 
17
  This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
18
  It achieves the following results on the evaluation set:
19
+ - Loss: 0.5750
20
+ - Rouge1: 0.4784
21
+ - Rouge2: 0.3008
22
+ - Rougel: 0.4185
23
+ - Rougelsum: 0.4212
24
 
25
  ## Model description
26
 
 
54
 
55
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
56
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
57
+ | 2.9357 | 0.16 | 100 | 2.5583 | 0.2654 | 0.0431 | 0.1946 | 0.1951 |
58
+ | 1.9974 | 0.33 | 200 | 1.7104 | 0.1803 | 0.0817 | 0.1712 | 0.1726 |
59
+ | 1.4803 | 0.49 | 300 | 1.4404 | 0.1770 | 0.0695 | 0.1707 | 0.1727 |
60
+ | 1.2432 | 0.65 | 400 | 1.0519 | 0.2809 | 0.1314 | 0.2509 | 0.2511 |
61
+ | 0.8186 | 0.82 | 500 | 0.7386 | 0.3487 | 0.1767 | 0.2894 | 0.2903 |
62
+ | 0.791 | 0.98 | 600 | 0.7135 | 0.3634 | 0.1912 | 0.3108 | 0.3108 |
63
+ | 0.6697 | 1.15 | 700 | 0.6835 | 0.3874 | 0.1900 | 0.3123 | 0.3131 |
64
+ | 0.7146 | 1.31 | 800 | 0.6657 | 0.3816 | 0.2209 | 0.3414 | 0.3428 |
65
+ | 0.6957 | 1.47 | 900 | 0.6498 | 0.3878 | 0.2045 | 0.3336 | 0.3339 |
66
+ | 0.6737 | 1.64 | 1000 | 0.6332 | 0.4094 | 0.2219 | 0.3524 | 0.3535 |
67
+ | 0.6537 | 1.8 | 1100 | 0.6369 | 0.4401 | 0.2621 | 0.3629 | 0.3630 |
68
+ | 0.6746 | 1.96 | 1200 | 0.6169 | 0.4369 | 0.2326 | 0.3566 | 0.3574 |
69
+ | 0.5961 | 2.13 | 1300 | 0.6171 | 0.4364 | 0.2464 | 0.3666 | 0.3670 |
70
+ | 0.5829 | 2.29 | 1400 | 0.6122 | 0.4539 | 0.2683 | 0.3813 | 0.3825 |
71
+ | 0.6336 | 2.45 | 1500 | 0.5993 | 0.4347 | 0.2548 | 0.3660 | 0.3689 |
72
+ | 0.5754 | 2.62 | 1600 | 0.5905 | 0.4575 | 0.2789 | 0.3856 | 0.3857 |
73
+ | 0.5984 | 2.78 | 1700 | 0.5872 | 0.4630 | 0.2768 | 0.3915 | 0.3929 |
74
+ | 0.5966 | 2.95 | 1800 | 0.5944 | 0.4605 | 0.2753 | 0.3822 | 0.3828 |
75
+ | 0.5288 | 3.11 | 1900 | 0.5955 | 0.4520 | 0.2651 | 0.3874 | 0.3887 |
76
+ | 0.5316 | 3.27 | 2000 | 0.5841 | 0.4649 | 0.2820 | 0.4052 | 0.4056 |
77
+ | 0.5332 | 3.44 | 2100 | 0.5765 | 0.4861 | 0.3046 | 0.4021 | 0.4050 |
78
+ | 0.5296 | 3.6 | 2200 | 0.5812 | 0.4610 | 0.2815 | 0.3976 | 0.4021 |
79
+ | 0.5215 | 3.76 | 2300 | 0.5757 | 0.4724 | 0.2947 | 0.4122 | 0.4164 |
80
+ | 0.5399 | 3.93 | 2400 | 0.5750 | 0.4784 | 0.3008 | 0.4185 | 0.4212 |
81
 
82
 
83
  ### Framework versions