Hugging Face's logo

gayanin
/
t5-small-mlm-pubmed-15

Text2Text Generation
Transformers
PyTorch
TensorBoard
t5
generated_from_trainer
Inference Endpoints
text-generation-inference
Model card Files Metrics Community
gayanin commited on
Commit
f070f21
•
1 Parent(s): d279038

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +112 -0
README.md ADDED
@@ -0,0 +1,112 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ model-index:
6
+ - name: t5-small-mlm-pubmed-15
7
+ results: []
8
+ ---
9
+
10
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
11
+ should probably proofread and complete it, then remove this comment. -->
12
+
13
+ # t5-small-mlm-pubmed-15
14
+
15
+ This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
16
+ It achieves the following results on the evaluation set:
17
+ - Loss: 0.5389
18
+ - Rouge2 Precision: 0.7165
19
+ - Rouge2 Recall: 0.5375
20
+ - Rouge2 Fmeasure: 0.5981
21
+
22
+ ## Model description
23
+
24
+ More information needed
25
+
26
+ ## Intended uses & limitations
27
+
28
+ More information needed
29
+
30
+ ## Training and evaluation data
31
+
32
+ More information needed
33
+
34
+ ## Training procedure
35
+
36
+ ### Training hyperparameters
37
+
38
+ The following hyperparameters were used during training:
39
+ - learning_rate: 2e-05
40
+ - train_batch_size: 16
41
+ - eval_batch_size: 16
42
+ - seed: 42
43
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
44
+ - lr_scheduler_type: linear
45
+ - num_epochs: 40
46
+ - mixed_precision_training: Native AMP
47
+
48
+ ### Training results
49
+
50
+ | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
51
+ |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
52
+ | 1.1024 | 0.75 | 500 | 0.7890 | 0.6854 | 0.4813 | 0.5502 |
53
+ | 0.8788 | 1.51 | 1000 | 0.7176 | 0.6906 | 0.4989 | 0.5638 |
54
+ | 0.8086 | 2.26 | 1500 | 0.6830 | 0.6872 | 0.5052 | 0.5663 |
55
+ | 0.7818 | 3.02 | 2000 | 0.6650 | 0.6912 | 0.5104 | 0.5711 |
56
+ | 0.7466 | 3.77 | 2500 | 0.6458 | 0.6965 | 0.5167 | 0.5774 |
57
+ | 0.731 | 4.52 | 3000 | 0.6355 | 0.6955 | 0.5161 | 0.5763 |
58
+ | 0.7126 | 5.28 | 3500 | 0.6249 | 0.6924 | 0.517 | 0.576 |
59
+ | 0.6998 | 6.03 | 4000 | 0.6166 | 0.6995 | 0.5207 | 0.5809 |
60
+ | 0.6855 | 6.79 | 4500 | 0.6076 | 0.6981 | 0.5215 | 0.5813 |
61
+ | 0.676 | 7.54 | 5000 | 0.6015 | 0.7003 | 0.5242 | 0.5836 |
62
+ | 0.6688 | 8.3 | 5500 | 0.5962 | 0.7004 | 0.5235 | 0.583 |
63
+ | 0.6569 | 9.05 | 6000 | 0.5900 | 0.6997 | 0.5234 | 0.5827 |
64
+ | 0.6503 | 9.8 | 6500 | 0.5880 | 0.703 | 0.5257 | 0.5856 |
65
+ | 0.6455 | 10.56 | 7000 | 0.5818 | 0.7008 | 0.5259 | 0.5849 |
66
+ | 0.635 | 11.31 | 7500 | 0.5796 | 0.7017 | 0.5271 | 0.5861 |
67
+ | 0.6323 | 12.07 | 8000 | 0.5769 | 0.7053 | 0.5276 | 0.5877 |
68
+ | 0.6241 | 12.82 | 8500 | 0.5730 | 0.7011 | 0.5243 | 0.5838 |
69
+ | 0.6224 | 13.57 | 9000 | 0.5696 | 0.7046 | 0.5286 | 0.5879 |
70
+ | 0.6139 | 14.33 | 9500 | 0.5685 | 0.7047 | 0.5295 | 0.5886 |
71
+ | 0.6118 | 15.08 | 10000 | 0.5653 | 0.704 | 0.5297 | 0.5886 |
72
+ | 0.6089 | 15.84 | 10500 | 0.5633 | 0.703 | 0.5272 | 0.5865 |
73
+ | 0.598 | 16.59 | 11000 | 0.5613 | 0.7059 | 0.5293 | 0.5889 |
74
+ | 0.6003 | 17.35 | 11500 | 0.5602 | 0.7085 | 0.532 | 0.5918 |
75
+ | 0.5981 | 18.1 | 12000 | 0.5587 | 0.7106 | 0.5339 | 0.5938 |
76
+ | 0.5919 | 18.85 | 12500 | 0.5556 | 0.708 | 0.5319 | 0.5914 |
77
+ | 0.5897 | 19.61 | 13000 | 0.5556 | 0.7106 | 0.5327 | 0.5931 |
78
+ | 0.5899 | 20.36 | 13500 | 0.5526 | 0.7114 | 0.534 | 0.5939 |
79
+ | 0.5804 | 21.12 | 14000 | 0.5521 | 0.7105 | 0.5328 | 0.5928 |
80
+ | 0.5764 | 21.87 | 14500 | 0.5520 | 0.715 | 0.537 | 0.5976 |
81
+ | 0.5793 | 22.62 | 15000 | 0.5506 | 0.713 | 0.5346 | 0.5951 |
82
+ | 0.5796 | 23.38 | 15500 | 0.5492 | 0.7124 | 0.5352 | 0.5952 |
83
+ | 0.5672 | 24.13 | 16000 | 0.5482 | 0.7124 | 0.5346 | 0.5948 |
84
+ | 0.5737 | 24.89 | 16500 | 0.5470 | 0.7134 | 0.5352 | 0.5956 |
85
+ | 0.5685 | 25.64 | 17000 | 0.5463 | 0.7117 | 0.5346 | 0.5946 |
86
+ | 0.5658 | 26.4 | 17500 | 0.5457 | 0.7145 | 0.5359 | 0.5965 |
87
+ | 0.5657 | 27.15 | 18000 | 0.5447 | 0.7145 | 0.5367 | 0.597 |
88
+ | 0.5645 | 27.9 | 18500 | 0.5441 | 0.7141 | 0.5362 | 0.5964 |
89
+ | 0.565 | 28.66 | 19000 | 0.5436 | 0.7151 | 0.5367 | 0.5972 |
90
+ | 0.5579 | 29.41 | 19500 | 0.5426 | 0.7162 | 0.5378 | 0.5982 |
91
+ | 0.563 | 30.17 | 20000 | 0.5424 | 0.7155 | 0.5373 | 0.5977 |
92
+ | 0.556 | 30.92 | 20500 | 0.5418 | 0.7148 | 0.536 | 0.5966 |
93
+ | 0.5576 | 31.67 | 21000 | 0.5411 | 0.7141 | 0.5356 | 0.5961 |
94
+ | 0.5546 | 32.43 | 21500 | 0.5409 | 0.7149 | 0.5364 | 0.5967 |
95
+ | 0.556 | 33.18 | 22000 | 0.5405 | 0.7143 | 0.5356 | 0.596 |
96
+ | 0.5536 | 33.94 | 22500 | 0.5401 | 0.7165 | 0.5377 | 0.5982 |
97
+ | 0.5527 | 34.69 | 23000 | 0.5397 | 0.7188 | 0.5389 | 0.5999 |
98
+ | 0.5531 | 35.44 | 23500 | 0.5395 | 0.7172 | 0.538 | 0.5989 |
99
+ | 0.5508 | 36.2 | 24000 | 0.5392 | 0.7166 | 0.538 | 0.5985 |
100
+ | 0.5495 | 36.95 | 24500 | 0.5391 | 0.7176 | 0.5387 | 0.5993 |
101
+ | 0.5539 | 37.71 | 25000 | 0.5391 | 0.7169 | 0.5372 | 0.598 |
102
+ | 0.5452 | 38.46 | 25500 | 0.5390 | 0.7179 | 0.5384 | 0.5991 |
103
+ | 0.5513 | 39.22 | 26000 | 0.5390 | 0.717 | 0.5377 | 0.5984 |
104
+ | 0.5506 | 39.97 | 26500 | 0.5389 | 0.7165 | 0.5375 | 0.5981 |
105
+
106
+
107
+ ### Framework versions
108
+
109
+ - Transformers 4.12.5
110
+ - Pytorch 1.10.0+cu111
111
+ - Datasets 1.15.1
112
+ - Tokenizers 0.10.3