Hugging Face's logo

Zekunli
/
flan-t5-large-extraction-cnndm_4000-all

Text2Text Generation
Transformers
PyTorch
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Community
1
Zekunli commited on
Commit
fce1348
•
1 Parent(s): 9ea73ec

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +22 -18
README.md CHANGED
@@ -15,12 +15,12 @@ should probably proofread and complete it, then remove this comment. -->
15
 
16
  This model was trained from scratch on the None dataset.
17
  It achieves the following results on the evaluation set:
18
- - Loss: 1.7531
19
- - Rouge1: 35.0348
20
- - Rouge2: 15.5615
21
- - Rougel: 30.2628
22
- - Rougelsum: 30.218
23
- - Gen Len: 18.994
24
 
25
  ## Model description
26
 
@@ -45,23 +45,27 @@ The following hyperparameters were used during training:
45
  - seed: 1799
46
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
47
  - lr_scheduler_type: linear
48
- - num_epochs: 20
49
 
50
  ### Training results
51
 
52
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
53
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
54
- | 1.6013 | 0.4 | 200 | 1.7818 | 34.9655 | 15.0698 | 29.8381 | 29.7984 | 19.0 |
55
- | 1.5864 | 0.8 | 400 | 1.7662 | 33.9055 | 14.3357 | 29.3382 | 29.3206 | 19.0 |
56
- | 1.5214 | 1.2 | 600 | 1.7988 | 34.856 | 15.4214 | 29.9006 | 29.8346 | 19.0 |
57
- | 1.4759 | 1.6 | 800 | 1.8195 | 33.7856 | 14.6215 | 29.2806 | 29.2144 | 19.0 |
58
- | 1.5068 | 2.0 | 1000 | 1.7686 | 34.828 | 14.8614 | 29.761 | 29.7316 | 18.998 |
59
- | 1.5696 | 2.4 | 1200 | 1.7531 | 35.0348 | 15.5615 | 30.2628 | 30.218 | 18.994 |
60
- | 1.5671 | 2.8 | 1400 | 1.7651 | 34.0963 | 15.2973 | 29.8032 | 29.7499 | 19.0 |
61
- | 1.5385 | 3.2 | 1600 | 1.7834 | 33.9286 | 14.8702 | 29.2844 | 29.2384 | 19.0 |
62
- | 1.4972 | 3.6 | 1800 | 1.7808 | 34.6569 | 15.1071 | 29.8532 | 29.8168 | 19.0 |
63
- | 1.4991 | 4.0 | 2000 | 1.7640 | 34.7095 | 15.0358 | 29.6992 | 29.672 | 19.0 |
64
- | 1.4504 | 4.4 | 2200 | 1.7944 | 34.9119 | 15.3942 | 29.9696 | 29.9097 | 19.0 |
 
 
 
 
65
 
66
 
67
  ### Framework versions
 
15
 
16
  This model was trained from scratch on the None dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 1.8084
19
+ - Rouge1: 35.2389
20
+ - Rouge2: 15.2731
21
+ - Rougel: 29.9899
22
+ - Rougelsum: 30.0262
23
+ - Gen Len: 19.0
24
 
25
  ## Model description
26
 
 
45
  - seed: 1799
46
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
47
  - lr_scheduler_type: linear
48
+ - num_epochs: 10
49
 
50
  ### Training results
51
 
52
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
53
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
54
+ | 1.2214 | 0.4 | 200 | 1.9330 | 34.7186 | 15.2527 | 29.7852 | 29.8623 | 19.0 |
55
+ | 1.2119 | 0.8 | 400 | 1.9119 | 34.718 | 15.3471 | 29.4347 | 29.4709 | 19.0 |
56
+ | 1.1482 | 1.2 | 600 | 2.0060 | 34.1536 | 15.0233 | 29.503 | 29.518 | 18.99 |
57
+ | 1.1102 | 1.6 | 800 | 2.0276 | 34.8004 | 15.1277 | 29.5782 | 29.6371 | 18.998 |
58
+ | 1.1295 | 2.0 | 1000 | 1.9375 | 35.1942 | 15.2087 | 30.156 | 30.0925 | 18.996 |
59
+ | 1.2045 | 2.4 | 1200 | 1.9016 | 35.5121 | 15.8033 | 30.515 | 30.5451 | 18.984 |
60
+ | 1.492 | 2.8 | 1400 | 1.8119 | 35.0575 | 15.2373 | 29.8621 | 29.9106 | 19.0 |
61
+ | 1.4535 | 3.2 | 1600 | 1.8160 | 35.0796 | 15.6135 | 30.1449 | 30.189 | 19.0 |
62
+ | 1.4087 | 3.6 | 1800 | 1.8223 | 34.9121 | 15.3203 | 29.7578 | 29.8006 | 18.998 |
63
+ | 1.4098 | 4.0 | 2000 | 1.8084 | 35.2389 | 15.2731 | 29.9899 | 30.0262 | 19.0 |
64
+ | 1.3759 | 4.4 | 2200 | 1.8357 | 35.4492 | 15.8883 | 30.1135 | 30.151 | 19.0 |
65
+ | 1.3565 | 4.8 | 2400 | 1.8347 | 34.6559 | 15.2567 | 29.5659 | 29.5704 | 19.0 |
66
+ | 1.3268 | 5.2 | 2600 | 1.8416 | 35.326 | 15.5918 | 29.841 | 29.8391 | 19.0 |
67
+ | 1.3204 | 5.6 | 2800 | 1.8445 | 35.4671 | 15.5422 | 30.169 | 30.1985 | 19.0 |
68
+ | 1.3271 | 6.0 | 3000 | 1.8374 | 35.4057 | 15.6566 | 30.2378 | 30.2328 | 18.998 |
69
 
70
 
71
  ### Framework versions