Hugging Face's logo

vichyt
/
codet5p-770m-py-sanitized-codebleu-1-True-0.0001-0.1-lora-infant

Generated from Trainer
Model card Files Community
vichyt commited on
Commit
78f01cc
•
1 Parent(s): a2ef390

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +108 -3
README.md CHANGED
@@ -1,9 +1,114 @@
1
  ---
2
- library_name: peft
 
 
 
 
 
 
 
 
3
  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4
  ## Training procedure
5
 
6
- ### Framework versions
 
 
 
 
 
 
 
 
 
 
7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8
 
9
- - PEFT 0.4.0
 
 
 
 
1
  ---
2
+ license: bsd-3-clause
3
+ base_model: Salesforce/codet5p-770m-py
4
+ tags:
5
+ - generated_from_trainer
6
+ datasets:
7
+ - mbpp
8
+ model-index:
9
+ - name: codet5p-770m-py-sanitized-codebleu-1-True-0.0001-0.1-lora-infant
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
+ # codet5p-770m-py-sanitized-codebleu-1-True-0.0001-0.1-lora-infant
17
+
18
+ This model is a fine-tuned version of [Salesforce/codet5p-770m-py](https://huggingface.co/Salesforce/codet5p-770m-py) on the mbpp dataset.
19
+ It achieves the following results on the evaluation set:
20
+ - Loss: 0.6873
21
+ - Codebleu: 0.1244
22
+ - Ngram Match Score: 0.0258
23
+ - Weighted Ngram Match Score: 0.0520
24
+ - Syntax Match Score: 0.1310
25
+ - Dataflow Match Score: 0.1606
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: 0.0001
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
+ - lr_scheduler_warmup_steps: 100
51
+ - num_epochs: 50
52
 
53
+ ### Training results
54
+
55
+ | Training Loss | Epoch | Step | Validation Loss | Codebleu | Ngram Match Score | Weighted Ngram Match Score | Syntax Match Score | Dataflow Match Score |
56
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:--------------------------:|:------------------:|:--------------------:|
57
+ | 0.9871 | 1.0 | 15 | 0.9197 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 |
58
+ | 0.9599 | 2.0 | 30 | 0.9009 | 0.0200 | 0.0000 | 0.0053 | 0.0185 | 0.0301 |
59
+ | 0.9303 | 3.0 | 45 | 0.8654 | 0.0833 | 0.0193 | 0.0474 | 0.1032 | 0.0884 |
60
+ | 0.8725 | 4.0 | 60 | 0.8467 | 0.1005 | 0.0209 | 0.0503 | 0.1270 | 0.1064 |
61
+ | 0.8613 | 5.0 | 75 | 0.8336 | 0.1007 | 0.0238 | 0.0521 | 0.1243 | 0.1084 |
62
+ | 0.836 | 6.0 | 90 | 0.8153 | 0.1005 | 0.0232 | 0.0512 | 0.1323 | 0.1004 |
63
+ | 0.8286 | 7.0 | 105 | 0.7953 | 0.0984 | 0.0236 | 0.0514 | 0.1270 | 0.1004 |
64
+ | 0.8074 | 8.0 | 120 | 0.7667 | 0.1036 | 0.0249 | 0.0563 | 0.1283 | 0.1104 |
65
+ | 0.7782 | 9.0 | 135 | 0.7399 | 0.0960 | 0.0136 | 0.0416 | 0.1217 | 0.1044 |
66
+ | 0.7638 | 10.0 | 150 | 0.7282 | 0.1033 | 0.0174 | 0.0503 | 0.1270 | 0.1145 |
67
+ | 0.7551 | 11.0 | 165 | 0.7236 | 0.1038 | 0.0192 | 0.0502 | 0.1257 | 0.1165 |
68
+ | 0.7538 | 12.0 | 180 | 0.7178 | 0.1084 | 0.0185 | 0.0518 | 0.1349 | 0.1185 |
69
+ | 0.7446 | 13.0 | 195 | 0.7139 | 0.1081 | 0.0198 | 0.0528 | 0.1376 | 0.1145 |
70
+ | 0.7465 | 14.0 | 210 | 0.7103 | 0.1065 | 0.0172 | 0.0444 | 0.1323 | 0.1185 |
71
+ | 0.7728 | 15.0 | 225 | 0.7065 | 0.1117 | 0.0233 | 0.0541 | 0.1415 | 0.1185 |
72
+ | 0.7455 | 16.0 | 240 | 0.7033 | 0.1107 | 0.0247 | 0.0525 | 0.1429 | 0.1145 |
73
+ | 0.7309 | 17.0 | 255 | 0.7017 | 0.1199 | 0.0270 | 0.0544 | 0.1508 | 0.1285 |
74
+ | 0.7172 | 18.0 | 270 | 0.6992 | 0.1161 | 0.0238 | 0.0514 | 0.1429 | 0.1285 |
75
+ | 0.7241 | 19.0 | 285 | 0.6979 | 0.1183 | 0.0259 | 0.0530 | 0.1495 | 0.1265 |
76
+ | 0.7132 | 20.0 | 300 | 0.6966 | 0.1126 | 0.0245 | 0.0512 | 0.1442 | 0.1185 |
77
+ | 0.6995 | 21.0 | 315 | 0.6936 | 0.1193 | 0.0262 | 0.0550 | 0.1534 | 0.1245 |
78
+ | 0.6916 | 22.0 | 330 | 0.6926 | 0.1127 | 0.0230 | 0.0482 | 0.1415 | 0.1225 |
79
+ | 0.6873 | 23.0 | 345 | 0.6913 | 0.1152 | 0.0255 | 0.0513 | 0.1442 | 0.1245 |
80
+ | 0.6884 | 24.0 | 360 | 0.6908 | 0.1157 | 0.0238 | 0.0471 | 0.1389 | 0.1325 |
81
+ | 0.7025 | 25.0 | 375 | 0.6895 | 0.1133 | 0.0241 | 0.0471 | 0.1389 | 0.1265 |
82
+ | 0.6857 | 26.0 | 390 | 0.6885 | 0.1093 | 0.0217 | 0.0468 | 0.1296 | 0.1265 |
83
+ | 0.6669 | 27.0 | 405 | 0.6894 | 0.1107 | 0.0201 | 0.0463 | 0.1296 | 0.1305 |
84
+ | 0.6842 | 28.0 | 420 | 0.6866 | 0.1053 | 0.0220 | 0.0489 | 0.1230 | 0.1225 |
85
+ | 0.6606 | 29.0 | 435 | 0.6866 | 0.1112 | 0.0227 | 0.0486 | 0.1217 | 0.1386 |
86
+ | 0.6648 | 30.0 | 450 | 0.6868 | 0.1070 | 0.0209 | 0.0478 | 0.1177 | 0.1325 |
87
+ | 0.6615 | 31.0 | 465 | 0.6856 | 0.1091 | 0.0223 | 0.0493 | 0.1283 | 0.1265 |
88
+ | 0.6616 | 32.0 | 480 | 0.6871 | 0.1104 | 0.0226 | 0.0484 | 0.1217 | 0.1365 |
89
+ | 0.663 | 33.0 | 495 | 0.6876 | 0.1076 | 0.0240 | 0.0511 | 0.1257 | 0.1245 |
90
+ | 0.6632 | 34.0 | 510 | 0.6876 | 0.1042 | 0.0237 | 0.0497 | 0.1257 | 0.1165 |
91
+ | 0.6548 | 35.0 | 525 | 0.6882 | 0.1148 | 0.0246 | 0.0501 | 0.1217 | 0.1466 |
92
+ | 0.6778 | 36.0 | 540 | 0.6848 | 0.1114 | 0.0224 | 0.0502 | 0.1217 | 0.1386 |
93
+ | 0.6517 | 37.0 | 555 | 0.6866 | 0.1180 | 0.0234 | 0.0490 | 0.1283 | 0.1486 |
94
+ | 0.6576 | 38.0 | 570 | 0.6876 | 0.1219 | 0.0248 | 0.0519 | 0.1349 | 0.1506 |
95
+ | 0.6504 | 39.0 | 585 | 0.6862 | 0.1224 | 0.0237 | 0.0501 | 0.1310 | 0.1566 |
96
+ | 0.6558 | 40.0 | 600 | 0.6871 | 0.1242 | 0.0249 | 0.0507 | 0.1349 | 0.1566 |
97
+ | 0.6426 | 41.0 | 615 | 0.6876 | 0.1290 | 0.0288 | 0.0546 | 0.1389 | 0.1627 |
98
+ | 0.6533 | 42.0 | 630 | 0.6868 | 0.1246 | 0.0244 | 0.0501 | 0.1323 | 0.1606 |
99
+ | 0.6396 | 43.0 | 645 | 0.6876 | 0.1226 | 0.0270 | 0.0539 | 0.1336 | 0.1526 |
100
+ | 0.6488 | 44.0 | 660 | 0.6873 | 0.1226 | 0.0270 | 0.0538 | 0.1336 | 0.1526 |
101
+ | 0.6419 | 45.0 | 675 | 0.6876 | 0.1282 | 0.0264 | 0.0525 | 0.1402 | 0.1606 |
102
+ | 0.6443 | 46.0 | 690 | 0.6874 | 0.1221 | 0.0264 | 0.0523 | 0.1310 | 0.1546 |
103
+ | 0.6499 | 47.0 | 705 | 0.6875 | 0.1200 | 0.0264 | 0.0523 | 0.1257 | 0.1546 |
104
+ | 0.6402 | 48.0 | 720 | 0.6874 | 0.1244 | 0.0258 | 0.0520 | 0.1310 | 0.1606 |
105
+ | 0.6552 | 49.0 | 735 | 0.6872 | 0.1244 | 0.0258 | 0.0520 | 0.1310 | 0.1606 |
106
+ | 0.6296 | 50.0 | 750 | 0.6873 | 0.1244 | 0.0258 | 0.0520 | 0.1310 | 0.1606 |
107
+
108
+
109
+ ### Framework versions
110
 
111
+ - Transformers 4.31.0
112
+ - Pytorch 2.0.1
113
+ - Datasets 2.14.4
114
+ - Tokenizers 0.13.3