Hugging Face's logo

peldrak
/
segformer-b5-cityscapes-finetuned-coastTrain-grCoastline

Image Segmentation
Transformers
TensorBoard
Safetensors
segformer
vision
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
peldrak commited on
Commit
bad87af
•
1 Parent(s): c27c534

End of training

Browse files
Files changed (4) hide show
  1. README.md +46 -51
  2. model.safetensors +1 -1
  3. runs/Mar06_14-07-52_91a9fa093e2e/events.out.tfevents.1709734076.91a9fa093e2e.26.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -1,10 +1,10 @@
1
  ---
2
  license: other
 
3
  tags:
4
  - vision
5
  - image-segmentation
6
  - generated_from_trainer
7
- base_model: peldrak/segformer-b5-cityscapes-finetuned-coastTrain
8
  model-index:
9
  - name: segformer-b5-cityscapes-finetuned-coastTrain-grCoastline
10
  results: []
@@ -17,25 +17,25 @@ should probably proofread and complete it, then remove this comment. -->
17
 
18
  This model is a fine-tuned version of [peldrak/segformer-b5-cityscapes-finetuned-coastTrain](https://huggingface.co/peldrak/segformer-b5-cityscapes-finetuned-coastTrain) on the peldrak/grCoastline_512 dataset.
19
  It achieves the following results on the evaluation set:
20
- - Loss: 0.2841
21
- - Mean Iou: 0.7642
22
- - Mean Accuracy: 0.8370
23
- - Overall Accuracy: 0.9351
24
- - Accuracy Water: 0.9727
25
- - Accuracy Whitewater: 0.5164
26
- - Accuracy Sediment: 0.8961
27
- - Accuracy Other Natural Terrain: 0.7246
28
- - Accuracy Vegetation: 0.9546
29
- - Accuracy Development: 0.7958
30
- - Accuracy Unknown: 0.9984
31
- - Iou Water: 0.9405
32
- - Iou Whitewater: 0.3963
33
- - Iou Sediment: 0.8430
34
- - Iou Other Natural Terrain: 0.6669
35
- - Iou Vegetation: 0.8162
36
- - Iou Development: 0.6905
37
- - Iou Unknown: 0.9964
38
- - F1 Score: 0.9339
39
 
40
  ## Model description
41
 
@@ -60,42 +60,37 @@ The following hyperparameters were used during training:
60
  - seed: 42
61
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
62
  - lr_scheduler_type: linear
63
- - num_epochs: 20
64
 
65
  ### Training results
66
 
67
  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sediment | Accuracy Other Natural Terrain | Accuracy Vegetation | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sediment | Iou Other Natural Terrain | Iou Vegetation | Iou Development | Iou Unknown | F1 Score |
68
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:|:--------:|
69
- | 0.4055 | 0.24 | 20 | 0.3644 | 0.6062 | 0.6921 | 0.8738 | 0.9535 | 0.0325 | 0.8544 | 0.6179 | 0.7936 | 0.5945 | 0.9983 | 0.8917 | 0.0309 | 0.7382 | 0.4229 | 0.6818 | 0.4892 | 0.9890 | 0.8730 |
70
- | 0.4038 | 0.49 | 40 | 0.3405 | 0.6314 | 0.7100 | 0.9002 | 0.9619 | 0.0178 | 0.8815 | 0.5199 | 0.9386 | 0.6521 | 0.9981 | 0.9180 | 0.0178 | 0.7634 | 0.4813 | 0.7538 | 0.4940 | 0.9913 | 0.8944 |
71
- | 0.2361 | 0.73 | 60 | 0.2577 | 0.6669 | 0.7475 | 0.9064 | 0.9783 | 0.0761 | 0.8624 | 0.7887 | 0.8193 | 0.7102 | 0.9976 | 0.9333 | 0.0750 | 0.8041 | 0.5649 | 0.7382 | 0.5600 | 0.9924 | 0.9068 |
72
- | 0.8032 | 0.98 | 80 | 0.3400 | 0.6495 | 0.7196 | 0.9109 | 0.9614 | 0.0016 | 0.9102 | 0.5659 | 0.9586 | 0.6463 | 0.9930 | 0.9218 | 0.0016 | 0.7769 | 0.5420 | 0.7783 | 0.5346 | 0.9913 | 0.9059 |
73
- | 0.1265 | 1.22 | 100 | 0.2827 | 0.6727 | 0.7407 | 0.9159 | 0.9701 | 0.0887 | 0.8815 | 0.7796 | 0.8831 | 0.5855 | 0.9965 | 0.9164 | 0.0870 | 0.7994 | 0.6280 | 0.7762 | 0.5098 | 0.9922 | 0.9143 |
74
- | 0.0787 | 1.46 | 120 | 0.2846 | 0.7171 | 0.7965 | 0.9209 | 0.9756 | 0.3483 | 0.8780 | 0.7273 | 0.9098 | 0.7411 | 0.9950 | 0.9252 | 0.3024 | 0.8107 | 0.6233 | 0.7922 | 0.5732 | 0.9925 | 0.9200 |
75
- | 0.1246 | 1.71 | 140 | 0.2294 | 0.7130 | 0.7764 | 0.9269 | 0.9712 | 0.2046 | 0.9257 | 0.7701 | 0.8999 | 0.6664 | 0.9970 | 0.9415 | 0.1917 | 0.8137 | 0.6537 | 0.8005 | 0.5966 | 0.9931 | 0.9256 |
76
- | 0.2549 | 1.95 | 160 | 0.2369 | 0.7116 | 0.7741 | 0.9274 | 0.9609 | 0.1563 | 0.9095 | 0.7690 | 0.9192 | 0.7087 | 0.9947 | 0.9297 | 0.1486 | 0.8240 | 0.6613 | 0.8003 | 0.6241 | 0.9929 | 0.9263 |
77
- | 0.4656 | 2.2 | 180 | 0.2599 | 0.7408 | 0.8118 | 0.9252 | 0.9641 | 0.4515 | 0.8497 | 0.7386 | 0.9445 | 0.7361 | 0.9979 | 0.9284 | 0.3574 | 0.8161 | 0.6515 | 0.7871 | 0.6525 | 0.9928 | 0.9243 |
78
- | 0.1394 | 2.44 | 200 | 0.3436 | 0.6930 | 0.7554 | 0.9235 | 0.9796 | 0.1591 | 0.9181 | 0.6312 | 0.9574 | 0.6459 | 0.9969 | 0.9410 | 0.1532 | 0.7963 | 0.6009 | 0.8068 | 0.5581 | 0.9944 | 0.9199 |
79
- | 0.2121 | 2.68 | 220 | 0.2425 | 0.7257 | 0.8019 | 0.9191 | 0.9746 | 0.3472 | 0.8738 | 0.8903 | 0.8212 | 0.7092 | 0.9970 | 0.9392 | 0.3090 | 0.8316 | 0.6278 | 0.7601 | 0.6173 | 0.9948 | 0.9203 |
80
- | 0.1015 | 2.93 | 240 | 0.2522 | 0.7431 | 0.8077 | 0.9283 | 0.9575 | 0.4025 | 0.8943 | 0.7359 | 0.9420 | 0.7236 | 0.9978 | 0.9316 | 0.3486 | 0.8345 | 0.6501 | 0.7961 | 0.6456 | 0.9950 | 0.9273 |
81
- | 0.1055 | 3.17 | 260 | 0.2163 | 0.7312 | 0.7954 | 0.9312 | 0.9710 | 0.3969 | 0.9059 | 0.8201 | 0.9172 | 0.5583 | 0.9982 | 0.9423 | 0.3302 | 0.8224 | 0.6974 | 0.8106 | 0.5213 | 0.9942 | 0.9298 |
82
- | 0.1588 | 3.41 | 280 | 0.2765 | 0.7204 | 0.7826 | 0.9275 | 0.9679 | 0.3053 | 0.9067 | 0.7336 | 0.9373 | 0.6292 | 0.9984 | 0.9323 | 0.2748 | 0.8228 | 0.6596 | 0.8017 | 0.5566 | 0.9949 | 0.9257 |
83
- | 0.1333 | 3.66 | 300 | 0.2697 | 0.7388 | 0.8223 | 0.9246 | 0.9651 | 0.4931 | 0.8618 | 0.8183 | 0.8949 | 0.7257 | 0.9971 | 0.9307 | 0.3490 | 0.8275 | 0.6511 | 0.7837 | 0.6348 | 0.9953 | 0.9249 |
84
- | 0.0724 | 3.9 | 320 | 0.2825 | 0.7360 | 0.8010 | 0.9296 | 0.9618 | 0.2669 | 0.8915 | 0.7653 | 0.9208 | 0.8020 | 0.9986 | 0.9284 | 0.2392 | 0.8390 | 0.6638 | 0.7943 | 0.6923 | 0.9953 | 0.9289 |
85
- | 0.2464 | 4.15 | 340 | 0.2823 | 0.7383 | 0.7966 | 0.9312 | 0.9748 | 0.3850 | 0.9020 | 0.7365 | 0.9506 | 0.6296 | 0.9978 | 0.9392 | 0.3277 | 0.8391 | 0.6600 | 0.8066 | 0.5999 | 0.9955 | 0.9295 |
86
- | 0.1542 | 4.39 | 360 | 0.2225 | 0.7639 | 0.8445 | 0.9338 | 0.9720 | 0.5500 | 0.9067 | 0.8583 | 0.8811 | 0.7456 | 0.9979 | 0.9426 | 0.4146 | 0.8484 | 0.6836 | 0.8091 | 0.6535 | 0.9959 | 0.9341 |
87
- | 0.3511 | 4.63 | 380 | 0.2159 | 0.7587 | 0.8330 | 0.9375 | 0.9806 | 0.4261 | 0.8955 | 0.8024 | 0.9205 | 0.8079 | 0.9977 | 0.9369 | 0.3425 | 0.8413 | 0.7060 | 0.8271 | 0.6612 | 0.9958 | 0.9370 |
88
- | 0.1767 | 4.88 | 400 | 0.2066 | 0.7587 | 0.8277 | 0.9334 | 0.9716 | 0.4338 | 0.9202 | 0.7967 | 0.9005 | 0.7731 | 0.9982 | 0.9418 | 0.3629 | 0.8418 | 0.6733 | 0.8062 | 0.6889 | 0.9957 | 0.9330 |
89
- | 0.0618 | 5.12 | 420 | 0.2675 | 0.7444 | 0.8125 | 0.9295 | 0.9838 | 0.4204 | 0.8715 | 0.7619 | 0.9257 | 0.7260 | 0.9983 | 0.9358 | 0.3400 | 0.8286 | 0.6538 | 0.8017 | 0.6546 | 0.9960 | 0.9286 |
90
- | 0.488 | 5.37 | 440 | 0.2639 | 0.7674 | 0.8591 | 0.9327 | 0.9745 | 0.6324 | 0.8978 | 0.7776 | 0.9100 | 0.8239 | 0.9974 | 0.9448 | 0.4515 | 0.8449 | 0.6622 | 0.8084 | 0.6641 | 0.9959 | 0.9326 |
91
- | 0.0341 | 5.61 | 460 | 0.2951 | 0.7583 | 0.8328 | 0.9302 | 0.9617 | 0.5107 | 0.8751 | 0.7487 | 0.9410 | 0.7944 | 0.9980 | 0.9318 | 0.3980 | 0.8319 | 0.6572 | 0.8005 | 0.6923 | 0.9965 | 0.9296 |
92
- | 0.082 | 5.85 | 480 | 0.3024 | 0.7520 | 0.8248 | 0.9297 | 0.9707 | 0.5152 | 0.8894 | 0.8072 | 0.9083 | 0.6851 | 0.9979 | 0.9406 | 0.4066 | 0.8389 | 0.6592 | 0.8001 | 0.6220 | 0.9963 | 0.9295 |
93
- | 0.0629 | 6.1 | 500 | 0.2703 | 0.7729 | 0.8528 | 0.9350 | 0.9667 | 0.6058 | 0.8878 | 0.7572 | 0.9439 | 0.8098 | 0.9986 | 0.9411 | 0.4454 | 0.8442 | 0.6673 | 0.8168 | 0.6993 | 0.9962 | 0.9345 |
94
- | 0.104 | 6.34 | 520 | 0.3095 | 0.7393 | 0.8109 | 0.9304 | 0.9778 | 0.4977 | 0.9379 | 0.7164 | 0.9346 | 0.6137 | 0.9981 | 0.9456 | 0.3854 | 0.8052 | 0.6550 | 0.8202 | 0.5670 | 0.9964 | 0.9284 |
95
- | 0.0595 | 6.59 | 540 | 0.2985 | 0.7461 | 0.8179 | 0.9285 | 0.9606 | 0.5192 | 0.9102 | 0.7545 | 0.9297 | 0.6527 | 0.9982 | 0.9316 | 0.4061 | 0.8275 | 0.6596 | 0.8002 | 0.6013 | 0.9963 | 0.9274 |
96
- | 0.0759 | 6.83 | 560 | 0.3163 | 0.7469 | 0.8283 | 0.9263 | 0.9698 | 0.6233 | 0.9033 | 0.7687 | 0.9121 | 0.6227 | 0.9982 | 0.9427 | 0.4458 | 0.8319 | 0.6389 | 0.7919 | 0.5809 | 0.9960 | 0.9255 |
97
- | 0.0894 | 7.07 | 580 | 0.2689 | 0.7676 | 0.8450 | 0.9345 | 0.9779 | 0.5720 | 0.9028 | 0.7744 | 0.9213 | 0.7679 | 0.9983 | 0.9429 | 0.4199 | 0.8496 | 0.6640 | 0.8124 | 0.6881 | 0.9965 | 0.9339 |
98
- | 0.2075 | 7.32 | 600 | 0.2841 | 0.7642 | 0.8370 | 0.9351 | 0.9727 | 0.5164 | 0.8961 | 0.7246 | 0.9546 | 0.7958 | 0.9984 | 0.9405 | 0.3963 | 0.8430 | 0.6669 | 0.8162 | 0.6905 | 0.9964 | 0.9339 |
99
 
100
 
101
  ### Framework versions
 
1
  ---
2
  license: other
3
+ base_model: peldrak/segformer-b5-cityscapes-finetuned-coastTrain
4
  tags:
5
  - vision
6
  - image-segmentation
7
  - generated_from_trainer
 
8
  model-index:
9
  - name: segformer-b5-cityscapes-finetuned-coastTrain-grCoastline
10
  results: []
 
17
 
18
  This model is a fine-tuned version of [peldrak/segformer-b5-cityscapes-finetuned-coastTrain](https://huggingface.co/peldrak/segformer-b5-cityscapes-finetuned-coastTrain) on the peldrak/grCoastline_512 dataset.
19
  It achieves the following results on the evaluation set:
20
+ - Loss: 0.2369
21
+ - Mean Iou: 0.7581
22
+ - Mean Accuracy: 0.8319
23
+ - Overall Accuracy: 0.9386
24
+ - Accuracy Water: 0.9821
25
+ - Accuracy Whitewater: 0.4902
26
+ - Accuracy Sediment: 0.9136
27
+ - Accuracy Other Natural Terrain: 0.8026
28
+ - Accuracy Vegetation: 0.9297
29
+ - Accuracy Development: 0.7063
30
+ - Accuracy Unknown: 0.9989
31
+ - Iou Water: 0.9520
32
+ - Iou Whitewater: 0.3507
33
+ - Iou Sediment: 0.8649
34
+ - Iou Other Natural Terrain: 0.6679
35
+ - Iou Vegetation: 0.8298
36
+ - Iou Development: 0.6457
37
+ - Iou Unknown: 0.9956
38
+ - F1 Score: 0.9379
39
 
40
  ## Model description
41
 
 
60
  - seed: 42
61
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
62
  - lr_scheduler_type: linear
63
+ - num_epochs: 30
64
 
65
  ### Training results
66
 
67
  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sediment | Accuracy Other Natural Terrain | Accuracy Vegetation | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sediment | Iou Other Natural Terrain | Iou Vegetation | Iou Development | Iou Unknown | F1 Score |
68
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:|:--------:|
69
+ | 0.4436 | 0.24 | 20 | 0.3559 | 0.6026 | 0.6838 | 0.8880 | 0.9773 | 0.0337 | 0.9374 | 0.5772 | 0.8436 | 0.4204 | 0.9972 | 0.8777 | 0.0293 | 0.7012 | 0.4913 | 0.7446 | 0.3805 | 0.9937 | 0.8816 |
70
+ | 0.3897 | 0.49 | 40 | 0.2428 | 0.6524 | 0.7222 | 0.9165 | 0.9825 | 0.0 | 0.9378 | 0.7357 | 0.8910 | 0.5144 | 0.9944 | 0.9418 | 0.0 | 0.7760 | 0.5956 | 0.8037 | 0.4564 | 0.9931 | 0.9134 |
71
+ | 0.5041 | 0.73 | 60 | 0.2645 | 0.6562 | 0.7196 | 0.9172 | 0.9864 | 0.0587 | 0.9317 | 0.6634 | 0.9456 | 0.4577 | 0.9934 | 0.9407 | 0.0581 | 0.7808 | 0.5880 | 0.8037 | 0.4299 | 0.9922 | 0.9118 |
72
+ | 0.1355 | 0.98 | 80 | 0.1992 | 0.7084 | 0.7729 | 0.9324 | 0.9855 | 0.1154 | 0.9117 | 0.8215 | 0.8980 | 0.6808 | 0.9974 | 0.9470 | 0.1086 | 0.8482 | 0.6539 | 0.8245 | 0.5821 | 0.9943 | 0.9316 |
73
+ | 0.1142 | 1.22 | 100 | 0.2597 | 0.6630 | 0.7299 | 0.9220 | 0.9852 | 0.0279 | 0.9498 | 0.8028 | 0.8860 | 0.4611 | 0.9965 | 0.9489 | 0.0278 | 0.7668 | 0.6533 | 0.8202 | 0.4298 | 0.9943 | 0.9184 |
74
+ | 0.2346 | 1.46 | 120 | 0.2670 | 0.6708 | 0.7331 | 0.9239 | 0.9823 | 0.0530 | 0.9331 | 0.7716 | 0.9238 | 0.4729 | 0.9953 | 0.9497 | 0.0524 | 0.8044 | 0.6352 | 0.8128 | 0.4478 | 0.9936 | 0.9201 |
75
+ | 0.4209 | 1.71 | 140 | 0.1952 | 0.7291 | 0.7955 | 0.9336 | 0.9688 | 0.2746 | 0.9536 | 0.8644 | 0.8830 | 0.6252 | 0.9989 | 0.9485 | 0.2476 | 0.8375 | 0.6754 | 0.8291 | 0.5719 | 0.9939 | 0.9328 |
76
+ | 0.0803 | 1.95 | 160 | 0.2460 | 0.6772 | 0.7440 | 0.9228 | 0.9824 | 0.0952 | 0.9514 | 0.7835 | 0.8898 | 0.5101 | 0.9958 | 0.9458 | 0.0937 | 0.7832 | 0.6355 | 0.8190 | 0.4692 | 0.9943 | 0.9200 |
77
+ | 0.3305 | 2.2 | 180 | 0.2127 | 0.7372 | 0.7992 | 0.9381 | 0.9869 | 0.1967 | 0.9330 | 0.7088 | 0.9337 | 0.8417 | 0.9935 | 0.9463 | 0.1844 | 0.8578 | 0.6514 | 0.8284 | 0.6996 | 0.9928 | 0.9366 |
78
+ | 0.1355 | 2.44 | 200 | 0.1968 | 0.7219 | 0.7768 | 0.9387 | 0.9877 | 0.0983 | 0.9309 | 0.7413 | 0.9411 | 0.7414 | 0.9971 | 0.9480 | 0.0963 | 0.8611 | 0.6629 | 0.8333 | 0.6575 | 0.9941 | 0.9370 |
79
+ | 0.0807 | 2.68 | 220 | 0.2531 | 0.6939 | 0.7569 | 0.9273 | 0.9826 | 0.1709 | 0.9534 | 0.7590 | 0.9194 | 0.5160 | 0.9969 | 0.9520 | 0.1628 | 0.8076 | 0.6326 | 0.8290 | 0.4785 | 0.9950 | 0.9242 |
80
+ | 0.1226 | 2.93 | 240 | 0.2434 | 0.7382 | 0.8081 | 0.9315 | 0.9832 | 0.3932 | 0.9090 | 0.7441 | 0.9272 | 0.7026 | 0.9976 | 0.9340 | 0.3119 | 0.8604 | 0.6199 | 0.8192 | 0.6276 | 0.9947 | 0.9303 |
81
+ | 0.0681 | 3.17 | 260 | 0.2265 | 0.7529 | 0.8220 | 0.9387 | 0.9802 | 0.3786 | 0.9371 | 0.7742 | 0.9194 | 0.7668 | 0.9974 | 0.9506 | 0.3042 | 0.8661 | 0.6614 | 0.8315 | 0.6617 | 0.9950 | 0.9380 |
82
+ | 0.1304 | 3.41 | 280 | 0.2342 | 0.7360 | 0.8009 | 0.9348 | 0.9772 | 0.3403 | 0.9507 | 0.7835 | 0.9220 | 0.6358 | 0.9971 | 0.9486 | 0.2864 | 0.8471 | 0.6572 | 0.8302 | 0.5880 | 0.9947 | 0.9333 |
83
+ | 0.1353 | 3.66 | 300 | 0.1970 | 0.7426 | 0.8071 | 0.9400 | 0.9803 | 0.2770 | 0.9285 | 0.8866 | 0.8962 | 0.6822 | 0.9987 | 0.9508 | 0.2259 | 0.8732 | 0.6883 | 0.8370 | 0.6278 | 0.9954 | 0.9397 |
84
+ | 0.3968 | 3.9 | 320 | 0.2181 | 0.7551 | 0.8214 | 0.9389 | 0.9868 | 0.3918 | 0.9280 | 0.7945 | 0.9160 | 0.7343 | 0.9982 | 0.9512 | 0.3204 | 0.8645 | 0.6646 | 0.8297 | 0.6594 | 0.9962 | 0.9381 |
85
+ | 0.0548 | 4.15 | 340 | 0.2025 | 0.7645 | 0.8313 | 0.9411 | 0.9804 | 0.4091 | 0.9287 | 0.7892 | 0.9208 | 0.7920 | 0.9987 | 0.9525 | 0.3393 | 0.8686 | 0.6729 | 0.8347 | 0.6878 | 0.9956 | 0.9406 |
86
+ | 0.0653 | 4.39 | 360 | 0.2549 | 0.7399 | 0.8105 | 0.9327 | 0.9851 | 0.4396 | 0.9222 | 0.8119 | 0.9144 | 0.6029 | 0.9976 | 0.9528 | 0.3534 | 0.8455 | 0.6479 | 0.8239 | 0.5602 | 0.9957 | 0.9314 |
87
+ | 0.1244 | 4.63 | 380 | 0.2580 | 0.7216 | 0.7821 | 0.9336 | 0.9925 | 0.2673 | 0.9136 | 0.7674 | 0.9405 | 0.5951 | 0.9984 | 0.9465 | 0.2327 | 0.8327 | 0.6598 | 0.8338 | 0.5501 | 0.9957 | 0.9312 |
88
+ | 0.2298 | 4.88 | 400 | 0.2345 | 0.7441 | 0.8112 | 0.9368 | 0.9760 | 0.2900 | 0.9511 | 0.8680 | 0.8716 | 0.7250 | 0.9965 | 0.9520 | 0.2585 | 0.8639 | 0.6659 | 0.8229 | 0.6497 | 0.9956 | 0.9369 |
89
+ | 0.0469 | 5.12 | 420 | 0.2614 | 0.7131 | 0.7813 | 0.9279 | 0.9854 | 0.3576 | 0.9420 | 0.8114 | 0.9099 | 0.4639 | 0.9986 | 0.9534 | 0.3075 | 0.8081 | 0.6597 | 0.8237 | 0.4432 | 0.9957 | 0.9244 |
90
+ | 0.1398 | 5.37 | 440 | 0.2542 | 0.7344 | 0.8077 | 0.9318 | 0.9851 | 0.4151 | 0.9388 | 0.8538 | 0.8842 | 0.5789 | 0.9979 | 0.9561 | 0.3381 | 0.8258 | 0.6665 | 0.8241 | 0.5345 | 0.9957 | 0.9305 |
91
+ | 0.0683 | 5.61 | 460 | 0.2496 | 0.7472 | 0.8213 | 0.9365 | 0.9856 | 0.5096 | 0.9419 | 0.8014 | 0.9301 | 0.5823 | 0.9980 | 0.9569 | 0.3788 | 0.8228 | 0.6847 | 0.8445 | 0.5467 | 0.9961 | 0.9346 |
92
+ | 0.141 | 5.85 | 480 | 0.2251 | 0.7514 | 0.8174 | 0.9393 | 0.9873 | 0.3664 | 0.9389 | 0.8465 | 0.8977 | 0.6867 | 0.9982 | 0.9559 | 0.3151 | 0.8544 | 0.6856 | 0.8387 | 0.6140 | 0.9962 | 0.9387 |
93
+ | 0.2161 | 6.1 | 500 | 0.2369 | 0.7581 | 0.8319 | 0.9386 | 0.9821 | 0.4902 | 0.9136 | 0.8026 | 0.9297 | 0.7063 | 0.9989 | 0.9520 | 0.3507 | 0.8649 | 0.6679 | 0.8298 | 0.6457 | 0.9956 | 0.9379 |
 
 
 
 
 
94
 
95
 
96
  ### Framework versions
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:64072fd789111dc71587149ce05ad44ed0458c9a44effb1140208015d9c41af7
3
  size 338543820
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:54b7b76d993fc0eac0ec0bed5a1c2bdb7d6f02e9aee8fd7e9676b1a83169b169
3
  size 338543820
runs/Mar06_14-07-52_91a9fa093e2e/events.out.tfevents.1709734076.91a9fa093e2e.26.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ed2500317ee6d6769f71c83105f7b871f5b28931593ed0a32f0cb7d4c1ef2652
3
+ size 144254
training_args.bin CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:eac6dcbeefc9c4b291dbbe9e80b94cb342ab991841c012283bdc4e3c909bc701
3
  size 5048
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:00af548bad642224323ef68791b975e74988501c980b68bec819d78d57f3e4fb
3
  size 5048