Hugging Face's logo

anniew666
/
lora-roberta-large-no-anger-f4-0927

PEFT
Generated from Trainer
Model card Files Community
anniew666 commited on
Commit
8d5bf10
•
1 Parent(s): d5928fe

Model save

Browse files
Files changed (5) hide show
  1. README.md +109 -0
  2. adapter_model.bin +1 -1
  3. cf.png +0 -0
  4. cf.txt +7 -7
  5. class_report.txt +7 -7
README.md ADDED
@@ -0,0 +1,109 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ base_model: roberta-large
4
+ tags:
5
+ - generated_from_trainer
6
+ metrics:
7
+ - accuracy
8
+ - recall
9
+ - f1
10
+ model-index:
11
+ - name: lora-roberta-large-no-anger-f4-0927
12
+ results: []
13
+ ---
14
+
15
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
16
+ should probably proofread and complete it, then remove this comment. -->
17
+
18
+ # lora-roberta-large-no-anger-f4-0927
19
+
20
+ This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
21
+ It achieves the following results on the evaluation set:
22
+ - Loss: 0.7423
23
+ - Accuracy: 0.7389
24
+ - Prec: 0.7379
25
+ - Recall: 0.7389
26
+ - F1: 0.7381
27
+ - B Acc: 0.6013
28
+ - Micro F1: 0.7389
29
+ - Prec Joy: 0.7415
30
+ - Recall Joy: 0.7893
31
+ - F1 Joy: 0.7646
32
+ - Prec Anger: 0.6020
33
+ - Recall Anger: 0.5728
34
+ - F1 Anger: 0.5871
35
+ - Prec Disgust: 0.4013
36
+ - Recall Disgust: 0.3896
37
+ - F1 Disgust: 0.3953
38
+ - Prec Fear: 0.5341
39
+ - Recall Fear: 0.5296
40
+ - F1 Fear: 0.5318
41
+ - Prec Neutral: 0.8416
42
+ - Recall Neutral: 0.8279
43
+ - F1 Neutral: 0.8347
44
+ - Prec Sadness: 0.6410
45
+ - Recall Sadness: 0.6338
46
+ - F1 Sadness: 0.6374
47
+ - Prec Surprise: 0.5093
48
+ - Recall Surprise: 0.4663
49
+ - F1 Surprise: 0.4869
50
+
51
+ ## Model description
52
+
53
+ More information needed
54
+
55
+ ## Intended uses & limitations
56
+
57
+ More information needed
58
+
59
+ ## Training and evaluation data
60
+
61
+ More information needed
62
+
63
+ ## Training procedure
64
+
65
+ ### Training hyperparameters
66
+
67
+ The following hyperparameters were used during training:
68
+ - learning_rate: 0.001
69
+ - train_batch_size: 32
70
+ - eval_batch_size: 32
71
+ - seed: 42
72
+ - gradient_accumulation_steps: 4
73
+ - total_train_batch_size: 128
74
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
75
+ - lr_scheduler_type: linear
76
+ - lr_scheduler_warmup_ratio: 0.05
77
+ - num_epochs: 25.0
78
+
79
+ ### Training results
80
+
81
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Prec | Recall | F1 | B Acc | Micro F1 | Prec Joy | Recall Joy | F1 Joy | Prec Anger | Recall Anger | F1 Anger | Prec Disgust | Recall Disgust | F1 Disgust | Prec Fear | Recall Fear | F1 Fear | Prec Neutral | Recall Neutral | F1 Neutral | Prec Sadness | Recall Sadness | F1 Sadness | Prec Surprise | Recall Surprise | F1 Surprise |
82
+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:--------:|:--------:|:----------:|:------:|:----------:|:------------:|:--------:|:------------:|:--------------:|:----------:|:---------:|:-----------:|:-------:|:------------:|:--------------:|:----------:|:------------:|:--------------:|:----------:|:-------------:|:---------------:|:-----------:|
83
+ | 0.8167 | 1.25 | 2049 | 0.7756 | 0.7130 | 0.7003 | 0.7130 | 0.7030 | 0.5272 | 0.7130 | 0.7252 | 0.7430 | 0.7340 | 0.6026 | 0.3749 | 0.4622 | 0.4187 | 0.3646 | 0.3898 | 0.5369 | 0.4170 | 0.4694 | 0.7763 | 0.8629 | 0.8173 | 0.6123 | 0.5784 | 0.5949 | 0.4797 | 0.3495 | 0.4044 |
84
+ | 0.7639 | 2.5 | 4098 | 0.7302 | 0.7293 | 0.7206 | 0.7293 | 0.7224 | 0.5662 | 0.7293 | 0.7361 | 0.7617 | 0.7487 | 0.6187 | 0.5198 | 0.5649 | 0.3881 | 0.4229 | 0.4048 | 0.5851 | 0.4247 | 0.4922 | 0.7961 | 0.8570 | 0.8254 | 0.6380 | 0.6185 | 0.6281 | 0.532 | 0.3585 | 0.4283 |
85
+ | 0.7395 | 3.75 | 6147 | 0.7348 | 0.7287 | 0.7328 | 0.7287 | 0.7271 | 0.5793 | 0.7287 | 0.6989 | 0.8136 | 0.7519 | 0.6786 | 0.4384 | 0.5327 | 0.4180 | 0.3875 | 0.4022 | 0.4632 | 0.5830 | 0.5162 | 0.8480 | 0.8134 | 0.8303 | 0.6648 | 0.5950 | 0.6280 | 0.5210 | 0.4241 | 0.4676 |
86
+ | 0.789 | 5.0 | 8196 | 0.7419 | 0.7275 | 0.7206 | 0.7275 | 0.7180 | 0.5511 | 0.7275 | 0.6888 | 0.8113 | 0.7450 | 0.6014 | 0.5183 | 0.5568 | 0.4038 | 0.4021 | 0.4029 | 0.5747 | 0.4305 | 0.4923 | 0.8063 | 0.8420 | 0.8238 | 0.6861 | 0.5838 | 0.6308 | 0.6224 | 0.2695 | 0.3762 |
87
+ | 0.7439 | 6.25 | 10245 | 0.7608 | 0.7207 | 0.7317 | 0.7207 | 0.7224 | 0.5858 | 0.7207 | 0.6882 | 0.8143 | 0.7459 | 0.6198 | 0.5004 | 0.5537 | 0.3944 | 0.3542 | 0.3732 | 0.4556 | 0.5843 | 0.5120 | 0.8599 | 0.7888 | 0.8228 | 0.7047 | 0.5590 | 0.6235 | 0.4535 | 0.4996 | 0.4754 |
88
+ | 0.712 | 7.5 | 12294 | 0.7240 | 0.7298 | 0.7270 | 0.7298 | 0.7263 | 0.5809 | 0.7298 | 0.7057 | 0.8043 | 0.7518 | 0.6313 | 0.4795 | 0.5450 | 0.4141 | 0.4271 | 0.4205 | 0.5707 | 0.4517 | 0.5043 | 0.8329 | 0.8214 | 0.8271 | 0.6126 | 0.6459 | 0.6288 | 0.5209 | 0.4367 | 0.4751 |
89
+ | 0.7032 | 8.75 | 14343 | 0.7095 | 0.7344 | 0.7328 | 0.7344 | 0.7317 | 0.5833 | 0.7344 | 0.7557 | 0.7479 | 0.7518 | 0.6391 | 0.5302 | 0.5796 | 0.4311 | 0.3521 | 0.3876 | 0.4724 | 0.6062 | 0.5310 | 0.8188 | 0.8498 | 0.8340 | 0.6472 | 0.6140 | 0.6301 | 0.5605 | 0.3827 | 0.4549 |
90
+ | 0.6972 | 10.0 | 16392 | 0.7108 | 0.7343 | 0.7325 | 0.7343 | 0.7317 | 0.5923 | 0.7343 | 0.7158 | 0.8038 | 0.7572 | 0.5785 | 0.5474 | 0.5625 | 0.3615 | 0.4729 | 0.4097 | 0.5714 | 0.4865 | 0.5255 | 0.8322 | 0.8288 | 0.8305 | 0.6797 | 0.5973 | 0.6358 | 0.5403 | 0.4097 | 0.4660 |
91
+ | 0.6859 | 11.25 | 18441 | 0.7211 | 0.7376 | 0.7321 | 0.7376 | 0.7322 | 0.5792 | 0.7376 | 0.7067 | 0.8093 | 0.7545 | 0.6216 | 0.5325 | 0.5736 | 0.4119 | 0.4188 | 0.4153 | 0.5720 | 0.4755 | 0.5193 | 0.8264 | 0.8407 | 0.8335 | 0.6677 | 0.6099 | 0.6375 | 0.5876 | 0.3675 | 0.4522 |
92
+ | 0.6542 | 12.5 | 20490 | 0.7143 | 0.7347 | 0.7294 | 0.7347 | 0.7307 | 0.5817 | 0.7347 | 0.7358 | 0.7824 | 0.7584 | 0.6263 | 0.5407 | 0.5804 | 0.3931 | 0.3792 | 0.3860 | 0.5700 | 0.4665 | 0.5131 | 0.8203 | 0.8364 | 0.8283 | 0.6158 | 0.6658 | 0.6398 | 0.5400 | 0.4007 | 0.4600 |
93
+ | 0.6463 | 13.75 | 22539 | 0.7022 | 0.7369 | 0.7366 | 0.7369 | 0.7354 | 0.5947 | 0.7369 | 0.7371 | 0.7864 | 0.7610 | 0.5452 | 0.6393 | 0.5885 | 0.5170 | 0.3167 | 0.3928 | 0.5519 | 0.4858 | 0.5168 | 0.8455 | 0.8218 | 0.8335 | 0.6062 | 0.6649 | 0.6342 | 0.5320 | 0.4483 | 0.4866 |
94
+ | 0.6333 | 15.0 | 24588 | 0.7106 | 0.7405 | 0.7387 | 0.7405 | 0.7387 | 0.5982 | 0.7405 | 0.7558 | 0.7617 | 0.7587 | 0.6294 | 0.5631 | 0.5944 | 0.4637 | 0.3854 | 0.4209 | 0.4892 | 0.5817 | 0.5315 | 0.8292 | 0.8481 | 0.8385 | 0.6600 | 0.6140 | 0.6362 | 0.5320 | 0.4331 | 0.4775 |
95
+ | 0.6184 | 16.25 | 26637 | 0.7199 | 0.7338 | 0.7389 | 0.7338 | 0.7348 | 0.6077 | 0.7338 | 0.7207 | 0.8008 | 0.7586 | 0.6140 | 0.5571 | 0.5842 | 0.3692 | 0.4292 | 0.3969 | 0.5024 | 0.5972 | 0.5457 | 0.8534 | 0.8079 | 0.8301 | 0.6714 | 0.6 | 0.6337 | 0.5109 | 0.4618 | 0.4851 |
96
+ | 0.5916 | 17.5 | 28686 | 0.7220 | 0.7368 | 0.7376 | 0.7368 | 0.7363 | 0.6003 | 0.7368 | 0.7426 | 0.7859 | 0.7636 | 0.5858 | 0.5713 | 0.5784 | 0.3743 | 0.4125 | 0.3925 | 0.5766 | 0.4653 | 0.5150 | 0.8479 | 0.8258 | 0.8367 | 0.5879 | 0.6676 | 0.6252 | 0.5146 | 0.4735 | 0.4932 |
97
+ | 0.5823 | 18.75 | 30735 | 0.7228 | 0.7376 | 0.7374 | 0.7376 | 0.7364 | 0.5960 | 0.7376 | 0.7210 | 0.8058 | 0.7610 | 0.6206 | 0.5534 | 0.5851 | 0.4056 | 0.3625 | 0.3828 | 0.5199 | 0.5631 | 0.5406 | 0.8460 | 0.8200 | 0.8328 | 0.6599 | 0.6126 | 0.6354 | 0.5254 | 0.4546 | 0.4875 |
98
+ | 0.5728 | 20.0 | 32784 | 0.7313 | 0.7344 | 0.7365 | 0.7344 | 0.7349 | 0.6090 | 0.7344 | 0.7295 | 0.7934 | 0.7601 | 0.5795 | 0.5907 | 0.5851 | 0.3927 | 0.4271 | 0.4092 | 0.5434 | 0.5161 | 0.5294 | 0.8462 | 0.8115 | 0.8285 | 0.6541 | 0.6311 | 0.6424 | 0.4928 | 0.4933 | 0.4930 |
99
+ | 0.5562 | 21.25 | 34833 | 0.7414 | 0.7376 | 0.7372 | 0.7376 | 0.7366 | 0.5995 | 0.7376 | 0.7372 | 0.7934 | 0.7643 | 0.6308 | 0.5258 | 0.5735 | 0.3946 | 0.425 | 0.4092 | 0.5324 | 0.5341 | 0.5332 | 0.8433 | 0.8267 | 0.8349 | 0.6139 | 0.6374 | 0.6254 | 0.5249 | 0.4537 | 0.4867 |
100
+ | 0.5348 | 22.5 | 36882 | 0.7398 | 0.7370 | 0.7374 | 0.7370 | 0.7365 | 0.6017 | 0.7370 | 0.7268 | 0.8039 | 0.7634 | 0.5844 | 0.5892 | 0.5868 | 0.4013 | 0.3937 | 0.3975 | 0.5331 | 0.5238 | 0.5284 | 0.8488 | 0.8163 | 0.8322 | 0.6473 | 0.6275 | 0.6372 | 0.5194 | 0.4573 | 0.4864 |
101
+ | 0.5202 | 23.75 | 38931 | 0.7423 | 0.7389 | 0.7379 | 0.7389 | 0.7381 | 0.6013 | 0.7389 | 0.7415 | 0.7893 | 0.7646 | 0.6020 | 0.5728 | 0.5871 | 0.4013 | 0.3896 | 0.3953 | 0.5341 | 0.5296 | 0.5318 | 0.8416 | 0.8279 | 0.8347 | 0.6410 | 0.6338 | 0.6374 | 0.5093 | 0.4663 | 0.4869 |
102
+
103
+
104
+ ### Framework versions
105
+
106
+ - Transformers 4.33.1
107
+ - Pytorch 2.0.1
108
+ - Datasets 2.12.0
109
+ - Tokenizers 0.13.3
adapter_model.bin CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:78704a261088050a5c4a61f064e89462706b3aaf17824d556f2ae89462573028
3
  size 7409629
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1f8ff222407d2a14b396aaa083c085406fe77c5e091b30eac3904fa90c765662
3
  size 7409629
cf.png CHANGED
cf.txt CHANGED
@@ -1,7 +1,7 @@
1
- 5.892457057505601226e-01 7.169529499626586810e-02 1.941747572815533812e-02 8.289768483943241173e-02 1.202389843166542238e-01 7.916355489171023052e-02 3.734129947722180515e-02
2
- 1.979166666666666574e-01 3.937499999999999889e-01 1.208333333333333343e-01 7.291666666666667129e-02 6.041666666666666713e-02 1.416666666666666630e-01 1.250000000000000069e-02
3
- 3.153153153153152866e-02 3.024453024453024538e-02 5.238095238095238360e-01 1.274131274131274083e-01 1.203346203346203347e-01 1.312741312741312616e-01 3.539253539253539582e-02
4
- 1.218087769063907859e-02 3.837810779242449739e-03 1.868846988152845015e-02 8.039379275821791904e-01 1.206407475387952649e-01 2.369431002836642694e-02 1.701985649924912455e-02
5
- 1.936570305921975904e-02 4.490597810833567641e-03 2.086256899616428101e-02 1.009448966226962341e-01 8.162597062400598347e-01 1.637197118533071510e-02 2.170455608569557548e-02
6
- 4.009009009009009000e-02 2.702702702702702853e-02 1.022522522522522487e-01 7.747747747747747715e-02 1.139639639639639657e-01 6.274774774774775077e-01 1.171171171171171178e-02
7
- 4.312668463611860070e-02 7.187780772686433450e-03 6.019766397124887841e-02 1.940700808625336893e-01 1.805929919137466422e-01 5.750224618149146760e-02 4.573225516621742814e-01
 
1
+ 5.728155339805824919e-01 7.766990291262135249e-02 2.165795369678864823e-02 8.439133681852128976e-02 1.202389843166542238e-01 7.692307692307692735e-02 4.630321135175503866e-02
2
+ 1.729166666666666630e-01 3.895833333333333370e-01 1.270833333333333259e-01 6.875000000000000555e-02 6.666666666666666574e-02 1.583333333333333259e-01 1.666666666666666644e-02
3
+ 2.509652509652509494e-02 2.445302445302445157e-02 5.296010296010296159e-01 1.126126126126126142e-01 1.370656370656370693e-01 1.351351351351351426e-01 3.603603603603603572e-02
4
+ 1.001168029367595531e-02 4.505256132154179978e-03 2.052394460203570831e-02 7.892541298181211529e-01 1.331553479058902034e-01 2.469547805773402230e-02 1.785416319038878735e-02
5
+ 1.871082421180653155e-02 4.397043689774534150e-03 1.861727009074749806e-02 9.027972682196651333e-02 8.278604172513799320e-01 1.777528300121620358e-02 2.235943493310880298e-02
6
+ 3.738738738738738715e-02 2.477477477477477499e-02 1.031531531531531543e-01 7.297297297297297702e-02 1.153153153153153171e-01 6.337837837837837496e-01 1.261261261261261216e-02
7
+ 3.773584905660377214e-02 7.187780772686433450e-03 6.918238993710691676e-02 1.805929919137466422e-01 1.841868823000898381e-01 5.480682839173404985e-02 4.663072776280323684e-01
class_report.txt CHANGED
@@ -1,13 +1,13 @@
1
  precision recall f1-score support
2
 
3
- 0anger 0.58 0.59 0.59 1339
4
  1disgust 0.40 0.39 0.40 480
5
- 2fear 0.53 0.52 0.53 1554
6
- 3joy 0.73 0.80 0.76 5993
7
- 4neutral 0.85 0.82 0.83 10689
8
- 5sadness 0.65 0.63 0.64 2220
9
- 6surprise 0.52 0.46 0.49 1113
10
 
11
  accuracy 0.74 23388
12
- macro avg 0.61 0.60 0.60 23388
13
  weighted avg 0.74 0.74 0.74 23388
 
1
  precision recall f1-score support
2
 
3
+ 0anger 0.60 0.57 0.59 1339
4
  1disgust 0.40 0.39 0.40 480
5
+ 2fear 0.53 0.53 0.53 1554
6
+ 3joy 0.74 0.79 0.76 5993
7
+ 4neutral 0.84 0.83 0.83 10689
8
+ 5sadness 0.64 0.63 0.64 2220
9
+ 6surprise 0.51 0.47 0.49 1113
10
 
11
  accuracy 0.74 23388
12
+ macro avg 0.61 0.60 0.61 23388
13
  weighted avg 0.74 0.74 0.74 23388