update model card README.md
Browse files
README.md
ADDED
@@ -0,0 +1,163 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: apache-2.0
|
3 |
+
base_model: t5-base
|
4 |
+
tags:
|
5 |
+
- generated_from_trainer
|
6 |
+
metrics:
|
7 |
+
- rouge
|
8 |
+
model-index:
|
9 |
+
- name: t5-base-finetuned-ehealth
|
10 |
+
results: []
|
11 |
+
---
|
12 |
+
|
13 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
14 |
+
should probably proofread and complete it, then remove this comment. -->
|
15 |
+
|
16 |
+
# t5-base-finetuned-ehealth
|
17 |
+
|
18 |
+
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
|
19 |
+
It achieves the following results on the evaluation set:
|
20 |
+
- Loss: 1.3953
|
21 |
+
- Rouge1: 16.9989
|
22 |
+
- Rouge2: 4.8395
|
23 |
+
- Rougel: 13.1702
|
24 |
+
- Rougelsum: 15.6472
|
25 |
+
- Gen Len: 19.0
|
26 |
+
|
27 |
+
## Model description
|
28 |
+
|
29 |
+
More information needed
|
30 |
+
|
31 |
+
## Intended uses & limitations
|
32 |
+
|
33 |
+
More information needed
|
34 |
+
|
35 |
+
## Training and evaluation data
|
36 |
+
|
37 |
+
More information needed
|
38 |
+
|
39 |
+
## Training procedure
|
40 |
+
|
41 |
+
### Training hyperparameters
|
42 |
+
|
43 |
+
The following hyperparameters were used during training:
|
44 |
+
- learning_rate: 2e-05
|
45 |
+
- train_batch_size: 8
|
46 |
+
- eval_batch_size: 8
|
47 |
+
- seed: 42
|
48 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
49 |
+
- lr_scheduler_type: linear
|
50 |
+
- num_epochs: 100
|
51 |
+
|
52 |
+
### Training results
|
53 |
+
|
54 |
+
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|
55 |
+
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
|
56 |
+
| No log | 1.0 | 22 | 4.2413 | 9.137 | 1.2333 | 6.9806 | 8.1957 | 18.6901 |
|
57 |
+
| No log | 2.0 | 44 | 3.5352 | 9.5584 | 1.2176 | 7.2081 | 8.5048 | 18.8187 |
|
58 |
+
| No log | 3.0 | 66 | 3.3124 | 9.9504 | 1.2105 | 7.4652 | 8.7962 | 18.8187 |
|
59 |
+
| No log | 4.0 | 88 | 3.2065 | 10.3375 | 1.1847 | 7.7904 | 9.1801 | 18.8947 |
|
60 |
+
| No log | 5.0 | 110 | 3.1208 | 10.777 | 1.326 | 8.1305 | 9.6488 | 18.8947 |
|
61 |
+
| No log | 6.0 | 132 | 3.0495 | 11.1502 | 1.4947 | 8.4386 | 9.9076 | 18.924 |
|
62 |
+
| No log | 7.0 | 154 | 2.9851 | 11.1759 | 1.5744 | 8.4744 | 9.9534 | 18.924 |
|
63 |
+
| No log | 8.0 | 176 | 2.9232 | 10.5745 | 1.5079 | 8.1888 | 9.4731 | 18.8363 |
|
64 |
+
| No log | 9.0 | 198 | 2.8663 | 10.3156 | 1.452 | 8.1662 | 9.385 | 18.8947 |
|
65 |
+
| No log | 10.0 | 220 | 2.8110 | 10.5445 | 1.6067 | 8.3821 | 9.6755 | 18.8538 |
|
66 |
+
| No log | 11.0 | 242 | 2.7625 | 11.0628 | 1.6957 | 8.7832 | 10.1425 | 18.8947 |
|
67 |
+
| No log | 12.0 | 264 | 2.7129 | 10.9152 | 1.8386 | 8.7865 | 10.0545 | 18.8538 |
|
68 |
+
| No log | 13.0 | 286 | 2.6680 | 10.8689 | 1.9024 | 8.6892 | 9.883 | 18.8889 |
|
69 |
+
| No log | 14.0 | 308 | 2.6235 | 10.4118 | 1.9101 | 8.2442 | 9.4505 | 18.8947 |
|
70 |
+
| No log | 15.0 | 330 | 2.5810 | 11.2578 | 2.0742 | 8.7641 | 10.2349 | 18.8947 |
|
71 |
+
| No log | 16.0 | 352 | 2.5412 | 11.815 | 2.1727 | 9.2403 | 10.6655 | 18.9591 |
|
72 |
+
| No log | 17.0 | 374 | 2.5056 | 11.8324 | 2.1849 | 9.2089 | 10.7361 | 18.9649 |
|
73 |
+
| No log | 18.0 | 396 | 2.4710 | 11.4611 | 2.1406 | 8.9329 | 10.4319 | 18.8246 |
|
74 |
+
| No log | 19.0 | 418 | 2.4365 | 12.0309 | 2.4387 | 9.3966 | 11.0327 | 18.8655 |
|
75 |
+
| No log | 20.0 | 440 | 2.4039 | 11.9636 | 2.4332 | 9.3448 | 11.0055 | 18.8363 |
|
76 |
+
| No log | 21.0 | 462 | 2.3734 | 12.709 | 2.6945 | 9.8722 | 11.572 | 18.7602 |
|
77 |
+
| No log | 22.0 | 484 | 2.3414 | 13.2227 | 2.6249 | 10.1069 | 11.968 | 18.7895 |
|
78 |
+
| 3.1829 | 23.0 | 506 | 2.3132 | 13.3682 | 2.6082 | 10.1546 | 12.0317 | 18.8246 |
|
79 |
+
| 3.1829 | 24.0 | 528 | 2.2861 | 14.3195 | 3.0288 | 10.8036 | 12.8973 | 18.8713 |
|
80 |
+
| 3.1829 | 25.0 | 550 | 2.2592 | 14.1227 | 2.6271 | 10.6826 | 12.7174 | 18.9064 |
|
81 |
+
| 3.1829 | 26.0 | 572 | 2.2324 | 14.3697 | 2.8314 | 10.9239 | 13.0199 | 18.9064 |
|
82 |
+
| 3.1829 | 27.0 | 594 | 2.2054 | 14.4512 | 2.9546 | 11.0853 | 13.1193 | 18.9474 |
|
83 |
+
| 3.1829 | 28.0 | 616 | 2.1810 | 15.12 | 3.3732 | 11.5842 | 13.6805 | 18.9474 |
|
84 |
+
| 3.1829 | 29.0 | 638 | 2.1563 | 14.8242 | 3.2998 | 11.2467 | 13.3076 | 18.9474 |
|
85 |
+
| 3.1829 | 30.0 | 660 | 2.1333 | 15.0384 | 3.3988 | 11.4676 | 13.6825 | 18.9123 |
|
86 |
+
| 3.1829 | 31.0 | 682 | 2.1102 | 14.9877 | 3.3844 | 11.4417 | 13.5657 | 18.9591 |
|
87 |
+
| 3.1829 | 32.0 | 704 | 2.0884 | 14.9699 | 3.4128 | 11.4893 | 13.6109 | 18.9591 |
|
88 |
+
| 3.1829 | 33.0 | 726 | 2.0646 | 14.7391 | 3.0552 | 11.2351 | 13.3809 | 18.9591 |
|
89 |
+
| 3.1829 | 34.0 | 748 | 2.0419 | 14.9203 | 3.1074 | 11.2239 | 13.4966 | 18.9591 |
|
90 |
+
| 3.1829 | 35.0 | 770 | 2.0203 | 15.1875 | 3.2249 | 11.3843 | 13.8011 | 18.9591 |
|
91 |
+
| 3.1829 | 36.0 | 792 | 1.9988 | 15.1457 | 3.1865 | 11.5238 | 13.7114 | 18.9591 |
|
92 |
+
| 3.1829 | 37.0 | 814 | 1.9786 | 15.2334 | 3.3739 | 11.6124 | 13.8956 | 18.9591 |
|
93 |
+
| 3.1829 | 38.0 | 836 | 1.9580 | 15.7105 | 3.4331 | 11.8577 | 14.2217 | 18.9474 |
|
94 |
+
| 3.1829 | 39.0 | 858 | 1.9387 | 15.6612 | 3.5588 | 12.0279 | 14.2183 | 18.9474 |
|
95 |
+
| 3.1829 | 40.0 | 880 | 1.9210 | 15.8692 | 3.5665 | 12.0078 | 14.3505 | 18.9591 |
|
96 |
+
| 3.1829 | 41.0 | 902 | 1.9041 | 15.9888 | 3.6914 | 12.0342 | 14.3375 | 18.9591 |
|
97 |
+
| 3.1829 | 42.0 | 924 | 1.8834 | 15.9551 | 3.6863 | 12.0562 | 14.5444 | 18.9591 |
|
98 |
+
| 3.1829 | 43.0 | 946 | 1.8648 | 15.9107 | 3.9128 | 12.1663 | 14.5029 | 18.9591 |
|
99 |
+
| 3.1829 | 44.0 | 968 | 1.8468 | 15.9831 | 3.8588 | 12.196 | 14.5114 | 18.9591 |
|
100 |
+
| 3.1829 | 45.0 | 990 | 1.8290 | 15.9072 | 3.6844 | 12.1007 | 14.5031 | 18.9591 |
|
101 |
+
| 2.4484 | 46.0 | 1012 | 1.8127 | 15.9918 | 3.792 | 12.2569 | 14.5287 | 18.9591 |
|
102 |
+
| 2.4484 | 47.0 | 1034 | 1.7959 | 15.9685 | 3.7664 | 12.1033 | 14.473 | 18.9591 |
|
103 |
+
| 2.4484 | 48.0 | 1056 | 1.7799 | 15.7128 | 3.505 | 11.9947 | 14.216 | 18.9591 |
|
104 |
+
| 2.4484 | 49.0 | 1078 | 1.7636 | 15.8033 | 3.6874 | 12.1043 | 14.37 | 18.9591 |
|
105 |
+
| 2.4484 | 50.0 | 1100 | 1.7487 | 15.914 | 3.758 | 12.1635 | 14.4603 | 18.9591 |
|
106 |
+
| 2.4484 | 51.0 | 1122 | 1.7338 | 15.7088 | 3.7272 | 11.951 | 14.2862 | 18.9591 |
|
107 |
+
| 2.4484 | 52.0 | 1144 | 1.7202 | 15.7231 | 3.6274 | 12.0492 | 14.3036 | 18.9591 |
|
108 |
+
| 2.4484 | 53.0 | 1166 | 1.7081 | 15.6734 | 3.5837 | 11.9265 | 14.2674 | 18.9591 |
|
109 |
+
| 2.4484 | 54.0 | 1188 | 1.6935 | 15.6501 | 3.5574 | 11.8579 | 14.2387 | 18.9591 |
|
110 |
+
| 2.4484 | 55.0 | 1210 | 1.6793 | 15.8984 | 3.8029 | 12.0981 | 14.3888 | 18.9591 |
|
111 |
+
| 2.4484 | 56.0 | 1232 | 1.6666 | 15.7263 | 3.6691 | 12.0325 | 14.3152 | 18.9591 |
|
112 |
+
| 2.4484 | 57.0 | 1254 | 1.6516 | 15.8016 | 3.6151 | 12.0349 | 14.3556 | 18.9591 |
|
113 |
+
| 2.4484 | 58.0 | 1276 | 1.6385 | 15.8773 | 3.7501 | 12.1887 | 14.456 | 18.9591 |
|
114 |
+
| 2.4484 | 59.0 | 1298 | 1.6266 | 16.0252 | 3.8027 | 12.3099 | 14.5017 | 18.9591 |
|
115 |
+
| 2.4484 | 60.0 | 1320 | 1.6151 | 16.29 | 3.9544 | 12.5391 | 14.7691 | 18.9591 |
|
116 |
+
| 2.4484 | 61.0 | 1342 | 1.6034 | 16.2891 | 4.0512 | 12.5053 | 14.8155 | 18.9591 |
|
117 |
+
| 2.4484 | 62.0 | 1364 | 1.5925 | 16.1871 | 4.0482 | 12.4821 | 14.6986 | 18.9591 |
|
118 |
+
| 2.4484 | 63.0 | 1386 | 1.5812 | 16.1774 | 3.9903 | 12.4861 | 14.7798 | 18.9591 |
|
119 |
+
| 2.4484 | 64.0 | 1408 | 1.5716 | 16.1663 | 3.9399 | 12.4316 | 14.7449 | 18.9591 |
|
120 |
+
| 2.4484 | 65.0 | 1430 | 1.5623 | 16.4455 | 4.2777 | 12.7206 | 14.9193 | 18.9591 |
|
121 |
+
| 2.4484 | 66.0 | 1452 | 1.5517 | 16.466 | 4.2148 | 12.7613 | 15.052 | 18.9591 |
|
122 |
+
| 2.4484 | 67.0 | 1474 | 1.5414 | 16.5696 | 4.193 | 12.6949 | 15.1064 | 18.9591 |
|
123 |
+
| 2.4484 | 68.0 | 1496 | 1.5347 | 16.7602 | 4.4803 | 12.938 | 15.3339 | 18.9649 |
|
124 |
+
| 2.1379 | 69.0 | 1518 | 1.5278 | 16.6684 | 4.3943 | 12.9152 | 15.2626 | 18.9649 |
|
125 |
+
| 2.1379 | 70.0 | 1540 | 1.5193 | 16.7462 | 4.4151 | 12.9251 | 15.3619 | 18.9649 |
|
126 |
+
| 2.1379 | 71.0 | 1562 | 1.5104 | 16.658 | 4.4187 | 12.8792 | 15.2538 | 18.9591 |
|
127 |
+
| 2.1379 | 72.0 | 1584 | 1.5026 | 16.8475 | 4.481 | 13.0381 | 15.4041 | 18.9591 |
|
128 |
+
| 2.1379 | 73.0 | 1606 | 1.4944 | 16.9066 | 4.6433 | 13.1838 | 15.489 | 18.9591 |
|
129 |
+
| 2.1379 | 74.0 | 1628 | 1.4864 | 16.9434 | 4.6401 | 13.0527 | 15.4966 | 18.9591 |
|
130 |
+
| 2.1379 | 75.0 | 1650 | 1.4801 | 16.9744 | 4.694 | 13.1585 | 15.5739 | 19.0 |
|
131 |
+
| 2.1379 | 76.0 | 1672 | 1.4733 | 17.0546 | 4.6971 | 13.0968 | 15.633 | 19.0 |
|
132 |
+
| 2.1379 | 77.0 | 1694 | 1.4668 | 17.1603 | 4.7771 | 13.2896 | 15.7112 | 19.0 |
|
133 |
+
| 2.1379 | 78.0 | 1716 | 1.4607 | 17.086 | 4.7411 | 13.2587 | 15.6842 | 19.0 |
|
134 |
+
| 2.1379 | 79.0 | 1738 | 1.4552 | 17.0322 | 4.7652 | 13.2693 | 15.711 | 19.0 |
|
135 |
+
| 2.1379 | 80.0 | 1760 | 1.4493 | 17.1045 | 4.8492 | 13.2752 | 15.7876 | 19.0 |
|
136 |
+
| 2.1379 | 81.0 | 1782 | 1.4445 | 17.0275 | 4.8688 | 13.2621 | 15.7825 | 19.0 |
|
137 |
+
| 2.1379 | 82.0 | 1804 | 1.4392 | 17.0985 | 4.8148 | 13.2498 | 15.7718 | 19.0 |
|
138 |
+
| 2.1379 | 83.0 | 1826 | 1.4337 | 17.1395 | 4.8482 | 13.357 | 15.8122 | 19.0 |
|
139 |
+
| 2.1379 | 84.0 | 1848 | 1.4294 | 17.0411 | 4.8237 | 13.3126 | 15.7736 | 19.0 |
|
140 |
+
| 2.1379 | 85.0 | 1870 | 1.4254 | 17.1265 | 4.8691 | 13.3033 | 15.81 | 19.0 |
|
141 |
+
| 2.1379 | 86.0 | 1892 | 1.4212 | 16.9899 | 4.7712 | 13.1785 | 15.6416 | 19.0 |
|
142 |
+
| 2.1379 | 87.0 | 1914 | 1.4176 | 17.0389 | 4.7936 | 13.219 | 15.7048 | 19.0 |
|
143 |
+
| 2.1379 | 88.0 | 1936 | 1.4141 | 17.2266 | 4.9339 | 13.3935 | 15.8629 | 19.0 |
|
144 |
+
| 2.1379 | 89.0 | 1958 | 1.4108 | 17.0176 | 4.8752 | 13.2829 | 15.7145 | 19.0 |
|
145 |
+
| 2.1379 | 90.0 | 1980 | 1.4084 | 17.154 | 4.9912 | 13.3718 | 15.8255 | 19.0 |
|
146 |
+
| 1.9718 | 91.0 | 2002 | 1.4061 | 17.0783 | 4.9171 | 13.2617 | 15.7864 | 19.0 |
|
147 |
+
| 1.9718 | 92.0 | 2024 | 1.4037 | 17.0967 | 4.9393 | 13.2608 | 15.8054 | 19.0 |
|
148 |
+
| 1.9718 | 93.0 | 2046 | 1.4020 | 17.1524 | 4.995 | 13.332 | 15.8315 | 19.0 |
|
149 |
+
| 1.9718 | 94.0 | 2068 | 1.4001 | 17.1357 | 4.9699 | 13.3064 | 15.7932 | 19.0 |
|
150 |
+
| 1.9718 | 95.0 | 2090 | 1.3988 | 17.0758 | 4.8899 | 13.2231 | 15.7124 | 19.0 |
|
151 |
+
| 1.9718 | 96.0 | 2112 | 1.3976 | 16.9842 | 4.8395 | 13.173 | 15.653 | 19.0 |
|
152 |
+
| 1.9718 | 97.0 | 2134 | 1.3967 | 17.0425 | 4.8395 | 13.2243 | 15.6976 | 19.0 |
|
153 |
+
| 1.9718 | 98.0 | 2156 | 1.3960 | 16.9842 | 4.8395 | 13.173 | 15.653 | 19.0 |
|
154 |
+
| 1.9718 | 99.0 | 2178 | 1.3955 | 16.9842 | 4.8395 | 13.173 | 15.653 | 19.0 |
|
155 |
+
| 1.9718 | 100.0 | 2200 | 1.3953 | 16.9989 | 4.8395 | 13.1702 | 15.6472 | 19.0 |
|
156 |
+
|
157 |
+
|
158 |
+
### Framework versions
|
159 |
+
|
160 |
+
- Transformers 4.31.0
|
161 |
+
- Pytorch 2.0.1+cu118
|
162 |
+
- Datasets 2.14.1
|
163 |
+
- Tokenizers 0.13.3
|