Hugging Face's logo

SungWei
/
my_awesome_billsum_model

Text2Text Generation
Transformers
PyTorch
t5
Generated from Trainer
Eval Results
text-generation-inference
Inference Endpoints
Model card Files Community
2
SungWei commited on
Commit
ca8552c
•
1 Parent(s): 3394d41

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +32 -17
README.md CHANGED
@@ -1,6 +1,6 @@
1
  ---
2
  license: apache-2.0
3
- base_model: t5-small
4
  tags:
5
  - generated_from_trainer
6
  datasets:
@@ -22,7 +22,7 @@ model-index:
22
  metrics:
23
  - name: Rouge1
24
  type: rouge
25
- value: 0.1648
26
  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -30,14 +30,14 @@ should probably proofread and complete it, then remove this comment. -->
30
 
31
  # my_awesome_billsum_model
32
 
33
- This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
34
  It achieves the following results on the evaluation set:
35
- - Loss: 2.3855
36
- - Rouge1: 0.1648
37
- - Rouge2: 0.0823
38
- - Rougel: 0.1406
39
- - Rougelsum: 0.1402
40
- - Gen Len: 16.4718
41
 
42
  ## Model description
43
 
@@ -57,22 +57,37 @@ More information needed
57
 
58
  The following hyperparameters were used during training:
59
  - learning_rate: 2e-05
60
- - train_batch_size: 8
61
- - eval_batch_size: 16
62
  - seed: 42
63
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
64
  - lr_scheduler_type: linear
65
- - num_epochs: 5
66
 
67
  ### Training results
68
 
69
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
70
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
71
- | No log | 1.0 | 124 | 2.7268 | 0.1497 | 0.0638 | 0.1259 | 0.126 | 19.0 |
72
- | No log | 2.0 | 248 | 2.5127 | 0.1502 | 0.0647 | 0.126 | 0.1261 | 18.9234 |
73
- | No log | 3.0 | 372 | 2.4331 | 0.151 | 0.0682 | 0.1274 | 0.1272 | 17.0081 |
74
- | No log | 4.0 | 496 | 2.3971 | 0.1628 | 0.0786 | 0.1388 | 0.1385 | 16.7782 |
75
- | 2.9098 | 5.0 | 620 | 2.3855 | 0.1648 | 0.0823 | 0.1406 | 0.1402 | 16.4718 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
76
 
77
 
78
  ### Framework versions
 
1
  ---
2
  license: apache-2.0
3
+ base_model: t5-base
4
  tags:
5
  - generated_from_trainer
6
  datasets:
 
22
  metrics:
23
  - name: Rouge1
24
  type: rouge
25
+ value: 0.2033
26
  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
30
 
31
  # my_awesome_billsum_model
32
 
33
+ This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the billsum dataset.
34
  It achieves the following results on the evaluation set:
35
+ - Loss: 1.6638
36
+ - Rouge1: 0.2033
37
+ - Rouge2: 0.1149
38
+ - Rougel: 0.1762
39
+ - Rougelsum: 0.1759
40
+ - Gen Len: 19.0
41
 
42
  ## Model description
43
 
 
57
 
58
  The following hyperparameters were used during training:
59
  - learning_rate: 2e-05
60
+ - train_batch_size: 4
61
+ - eval_batch_size: 8
62
  - seed: 42
63
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
64
  - lr_scheduler_type: linear
65
+ - num_epochs: 20
66
 
67
  ### Training results
68
 
69
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
70
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
71
+ | No log | 1.0 | 248 | 1.9584 | 0.1999 | 0.1073 | 0.1716 | 0.1717 | 19.0 |
72
+ | No log | 2.0 | 496 | 1.8621 | 0.195 | 0.1045 | 0.1685 | 0.1682 | 19.0 |
73
+ | 2.2512 | 3.0 | 744 | 1.8095 | 0.1973 | 0.1109 | 0.1728 | 0.1727 | 19.0 |
74
+ | 2.2512 | 4.0 | 992 | 1.7797 | 0.1989 | 0.1102 | 0.1724 | 0.1724 | 19.0 |
75
+ | 1.8144 | 5.0 | 1240 | 1.7505 | 0.1997 | 0.112 | 0.1735 | 0.1736 | 19.0 |
76
+ | 1.8144 | 6.0 | 1488 | 1.7308 | 0.2003 | 0.1134 | 0.1746 | 0.1744 | 19.0 |
77
+ | 1.6898 | 7.0 | 1736 | 1.7145 | 0.199 | 0.1114 | 0.1732 | 0.173 | 19.0 |
78
+ | 1.6898 | 8.0 | 1984 | 1.7083 | 0.1977 | 0.1106 | 0.1718 | 0.1716 | 19.0 |
79
+ | 1.5997 | 9.0 | 2232 | 1.6983 | 0.2014 | 0.1127 | 0.175 | 0.175 | 19.0 |
80
+ | 1.5997 | 10.0 | 2480 | 1.6923 | 0.2014 | 0.1153 | 0.1754 | 0.1753 | 19.0 |
81
+ | 1.5403 | 11.0 | 2728 | 1.6826 | 0.2009 | 0.1134 | 0.1752 | 0.1751 | 19.0 |
82
+ | 1.5403 | 12.0 | 2976 | 1.6768 | 0.2003 | 0.1125 | 0.1745 | 0.1744 | 19.0 |
83
+ | 1.491 | 13.0 | 3224 | 1.6722 | 0.2016 | 0.1146 | 0.1756 | 0.1755 | 19.0 |
84
+ | 1.491 | 14.0 | 3472 | 1.6750 | 0.2039 | 0.1164 | 0.1773 | 0.177 | 19.0 |
85
+ | 1.4496 | 15.0 | 3720 | 1.6679 | 0.2023 | 0.1147 | 0.1765 | 0.1763 | 19.0 |
86
+ | 1.4496 | 16.0 | 3968 | 1.6677 | 0.2032 | 0.1148 | 0.177 | 0.1768 | 19.0 |
87
+ | 1.4241 | 17.0 | 4216 | 1.6640 | 0.2021 | 0.1135 | 0.1752 | 0.175 | 19.0 |
88
+ | 1.4241 | 18.0 | 4464 | 1.6645 | 0.2027 | 0.1155 | 0.1766 | 0.1764 | 19.0 |
89
+ | 1.4025 | 19.0 | 4712 | 1.6632 | 0.2028 | 0.1149 | 0.1761 | 0.1757 | 19.0 |
90
+ | 1.4025 | 20.0 | 4960 | 1.6638 | 0.2033 | 0.1149 | 0.1762 | 0.1759 | 19.0 |
91
 
92
 
93
  ### Framework versions