Model save
Browse files- README.md +61 -68
- model.safetensors +1 -1
- training_args.bin +1 -1
README.md
CHANGED
|
@@ -3,8 +3,6 @@ library_name: transformers
|
|
| 3 |
license: other
|
| 4 |
base_model: nvidia/segformer-b2-finetuned-cityscapes-1024-1024
|
| 5 |
tags:
|
| 6 |
-
- vision
|
| 7 |
-
- image-segmentation
|
| 8 |
- generated_from_trainer
|
| 9 |
model-index:
|
| 10 |
- name: SegFormer_b2_
|
|
@@ -16,50 +14,50 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 16 |
|
| 17 |
# SegFormer_b2_
|
| 18 |
|
| 19 |
-
This model is a fine-tuned version of [nvidia/segformer-b2-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b2-finetuned-cityscapes-1024-1024) on
|
| 20 |
It achieves the following results on the evaluation set:
|
| 21 |
-
- Loss: 1.
|
| 22 |
-
- Mean Iou: 0.
|
| 23 |
-
- Mean Accuracy: 0.
|
| 24 |
-
- Overall Accuracy: 0.
|
| 25 |
-
- Accuracy Road: 0.
|
| 26 |
-
- Accuracy Sidewalk: 0.
|
| 27 |
-
- Accuracy Building: 0.
|
| 28 |
-
- Accuracy Wall: 0.
|
| 29 |
-
- Accuracy Fence: 0.
|
| 30 |
-
- Accuracy Pole: 0.
|
| 31 |
-
- Accuracy Traffic light: 0.
|
| 32 |
-
- Accuracy Traffic sign: 0.
|
| 33 |
-
- Accuracy Vegetation: 0.
|
| 34 |
- Accuracy Terrain: 0.6285
|
| 35 |
-
- Accuracy Sky: 0.
|
| 36 |
-
- Accuracy Person: 0.
|
| 37 |
-
- Accuracy Rider: 0.
|
| 38 |
-
- Accuracy Car: 0.
|
| 39 |
-
- Accuracy Truck: 0.
|
| 40 |
-
- Accuracy Bus: 0.
|
| 41 |
-
- Accuracy Train: 0.
|
| 42 |
-
- Accuracy Motorcycle: 0.
|
| 43 |
-
- Accuracy Bicycle: 0.
|
| 44 |
-
- Iou Road: 0.
|
| 45 |
-
- Iou Sidewalk: 0.
|
| 46 |
-
- Iou Building: 0.
|
| 47 |
-
- Iou Wall: 0.
|
| 48 |
-
- Iou Fence: 0.
|
| 49 |
-
- Iou Pole: 0.
|
| 50 |
-
- Iou Traffic light: 0.
|
| 51 |
-
- Iou Traffic sign: 0.
|
| 52 |
-
- Iou Vegetation: 0.
|
| 53 |
-
- Iou Terrain: 0.
|
| 54 |
-
- Iou Sky: 0.
|
| 55 |
-
- Iou Person: 0.
|
| 56 |
-
- Iou Rider: 0.
|
| 57 |
-
- Iou Car: 0.
|
| 58 |
-
- Iou Truck: 0.
|
| 59 |
-
- Iou Bus: 0.
|
| 60 |
-
- Iou Train: 0.
|
| 61 |
-
- Iou Motorcycle: 0.
|
| 62 |
-
- Iou Bicycle: 0.
|
| 63 |
|
| 64 |
## Model description
|
| 65 |
|
|
@@ -78,7 +76,7 @@ More information needed
|
|
| 78 |
### Training hyperparameters
|
| 79 |
|
| 80 |
The following hyperparameters were used during training:
|
| 81 |
-
- learning_rate: 0.
|
| 82 |
- train_batch_size: 16
|
| 83 |
- eval_batch_size: 16
|
| 84 |
- seed: 42
|
|
@@ -94,29 +92,24 @@ The following hyperparameters were used during training:
|
|
| 94 |
|
| 95 |
| Training Loss | Epoch | Step | Accuracy Bicycle | Accuracy Building | Accuracy Bus | Accuracy Car | Accuracy Fence | Accuracy Motorcycle | Accuracy Person | Accuracy Pole | Accuracy Rider | Accuracy Road | Accuracy Sidewalk | Accuracy Sky | Accuracy Terrain | Accuracy Traffic light | Accuracy Traffic sign | Accuracy Train | Accuracy Truck | Accuracy Vegetation | Accuracy Wall | Iou Bicycle | Iou Building | Iou Bus | Iou Car | Iou Fence | Iou Motorcycle | Iou Person | Iou Pole | Iou Rider | Iou Road | Iou Sidewalk | Iou Sky | Iou Terrain | Iou Traffic light | Iou Traffic sign | Iou Train | Iou Truck | Iou Vegetation | Iou Wall | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy |
|
| 96 |
|:-------------:|:-------:|:----:|:----------------:|:-----------------:|:------------:|:------------:|:--------------:|:-------------------:|:---------------:|:-------------:|:--------------:|:-------------:|:-----------------:|:------------:|:----------------:|:----------------------:|:---------------------:|:--------------:|:--------------:|:-------------------:|:-------------:|:-----------:|:------------:|:-------:|:-------:|:---------:|:--------------:|:----------:|:--------:|:---------:|:--------:|:------------:|:-------:|:-----------:|:-----------------:|:----------------:|:---------:|:---------:|:--------------:|:--------:|:---------------:|:-------------:|:--------:|:----------------:|
|
| 97 |
-
|
|
| 98 |
-
|
|
| 99 |
-
|
|
| 100 |
-
|
|
| 101 |
-
|
|
| 102 |
-
|
|
| 103 |
-
|
|
| 104 |
-
| 6.
|
| 105 |
-
|
|
| 106 |
-
| 7.
|
| 107 |
-
|
|
| 108 |
-
| 7.
|
| 109 |
-
|
|
| 110 |
-
|
|
| 111 |
-
|
|
| 112 |
-
|
|
| 113 |
-
| 6.
|
| 114 |
-
|
|
| 115 |
-
| 6.5878 | 40.8172 | 1900 | 0.7381 | 0.9350 | 0.7873 | 0.9238 | 0.5780 | 0.5450 | 0.7328 | 0.5148 | 0.5550 | 0.9835 | 0.8388 | 0.9479 | 0.6593 | 0.6625 | 0.6837 | 0.6830 | 0.6526 | 0.9247 | 0.5516 | 0.5734 | 0.8657 | 0.6889 | 0.8721 | 0.4468 | 0.4075 | 0.5674 | 0.3451 | 0.3605 | 0.9563 | 0.7123 | 0.9014 | 0.5452 | 0.4680 | 0.5641 | 0.6428 | 0.5564 | 0.8702 | 0.4884 | 1.9603 | 0.7315 | 0.6228 | 0.9227 |
|
| 116 |
-
| 6.2246 | 42.9462 | 2000 | 0.6965 | 0.9435 | 0.7914 | 0.9296 | 0.5240 | 0.5602 | 0.7195 | 0.5298 | 0.5844 | 0.9834 | 0.8383 | 0.9389 | 0.6232 | 0.6611 | 0.6819 | 0.6379 | 0.6310 | 0.9229 | 0.5725 | 0.5719 | 0.8653 | 0.6732 | 0.8720 | 0.4420 | 0.4200 | 0.5698 | 0.3492 | 0.3700 | 0.9570 | 0.7176 | 0.9014 | 0.5463 | 0.4744 | 0.5614 | 0.6100 | 0.5576 | 0.8706 | 0.4980 | 1.9342 | 0.7247 | 0.6225 | 0.9234 |
|
| 117 |
-
| 7.4045 | 45.0645 | 2100 | 0.7076 | 0.9380 | 0.8054 | 0.9286 | 0.5505 | 0.4858 | 0.7431 | 0.5202 | 0.5815 | 0.9837 | 0.8338 | 0.9423 | 0.6323 | 0.6712 | 0.6895 | 0.6615 | 0.6309 | 0.9318 | 0.5456 | 0.5740 | 0.8682 | 0.6932 | 0.8736 | 0.4476 | 0.3952 | 0.5701 | 0.3504 | 0.3631 | 0.9568 | 0.7172 | 0.9038 | 0.5471 | 0.4733 | 0.5649 | 0.6260 | 0.5743 | 0.8724 | 0.4843 | 1.9209 | 0.7254 | 0.6240 | 0.9240 |
|
| 118 |
-
| 6.6521 | 47.1935 | 2200 | 0.7241 | 0.9349 | 0.7896 | 0.9258 | 0.5411 | 0.5575 | 0.7431 | 0.5364 | 0.5836 | 0.9841 | 0.8297 | 0.9467 | 0.6298 | 0.6757 | 0.6985 | 0.6633 | 0.6485 | 0.9278 | 0.5435 | 0.5685 | 0.8664 | 0.6826 | 0.8722 | 0.4472 | 0.4163 | 0.5700 | 0.3488 | 0.3622 | 0.9558 | 0.7126 | 0.9040 | 0.5401 | 0.4692 | 0.5633 | 0.6280 | 0.5708 | 0.8716 | 0.4826 | 1.9400 | 0.7307 | 0.6228 | 0.9229 |
|
| 119 |
-
| 6.383 | 49.9677 | 2300 | 1.9394 | 0.6247 | 0.7299 | 0.9233 | 0.9836 | 0.8334 | 0.9387 | 0.5535 | 0.5674 | 0.5316 | 0.6698 | 0.6901 | 0.9239 | 0.6285 | 0.9506 | 0.7416 | 0.5474 | 0.9271 | 0.6458 | 0.7972 | 0.6846 | 0.5459 | 0.7077 | 0.9563 | 0.7156 | 0.8667 | 0.4851 | 0.4486 | 0.3497 | 0.4737 | 0.5639 | 0.8710 | 0.5384 | 0.9042 | 0.5739 | 0.3597 | 0.8723 | 0.5765 | 0.6862 | 0.6377 | 0.4203 | 0.5696 |
|
| 120 |
|
| 121 |
|
| 122 |
### Framework versions
|
|
|
|
| 3 |
license: other
|
| 4 |
base_model: nvidia/segformer-b2-finetuned-cityscapes-1024-1024
|
| 5 |
tags:
|
|
|
|
|
|
|
| 6 |
- generated_from_trainer
|
| 7 |
model-index:
|
| 8 |
- name: SegFormer_b2_
|
|
|
|
| 14 |
|
| 15 |
# SegFormer_b2_
|
| 16 |
|
| 17 |
+
This model is a fine-tuned version of [nvidia/segformer-b2-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b2-finetuned-cityscapes-1024-1024) on an unknown dataset.
|
| 18 |
It achieves the following results on the evaluation set:
|
| 19 |
+
- Loss: 1.9619
|
| 20 |
+
- Mean Iou: 0.6350
|
| 21 |
+
- Mean Accuracy: 0.7337
|
| 22 |
+
- Overall Accuracy: 0.9277
|
| 23 |
+
- Accuracy Road: 0.9852
|
| 24 |
+
- Accuracy Sidewalk: 0.8497
|
| 25 |
+
- Accuracy Building: 0.9358
|
| 26 |
+
- Accuracy Wall: 0.5532
|
| 27 |
+
- Accuracy Fence: 0.5198
|
| 28 |
+
- Accuracy Pole: 0.5455
|
| 29 |
+
- Accuracy Traffic light: 0.6870
|
| 30 |
+
- Accuracy Traffic sign: 0.7070
|
| 31 |
+
- Accuracy Vegetation: 0.9414
|
| 32 |
- Accuracy Terrain: 0.6285
|
| 33 |
+
- Accuracy Sky: 0.9488
|
| 34 |
+
- Accuracy Person: 0.7732
|
| 35 |
+
- Accuracy Rider: 0.5306
|
| 36 |
+
- Accuracy Car: 0.9299
|
| 37 |
+
- Accuracy Truck: 0.6569
|
| 38 |
+
- Accuracy Bus: 0.7685
|
| 39 |
+
- Accuracy Train: 0.6389
|
| 40 |
+
- Accuracy Motorcycle: 0.6015
|
| 41 |
+
- Accuracy Bicycle: 0.7396
|
| 42 |
+
- Iou Road: 0.9605
|
| 43 |
+
- Iou Sidewalk: 0.7409
|
| 44 |
+
- Iou Building: 0.8722
|
| 45 |
+
- Iou Wall: 0.4867
|
| 46 |
+
- Iou Fence: 0.4444
|
| 47 |
+
- Iou Pole: 0.3637
|
| 48 |
+
- Iou Traffic light: 0.4816
|
| 49 |
+
- Iou Traffic sign: 0.5829
|
| 50 |
+
- Iou Vegetation: 0.8786
|
| 51 |
+
- Iou Terrain: 0.5591
|
| 52 |
+
- Iou Sky: 0.9110
|
| 53 |
+
- Iou Person: 0.6011
|
| 54 |
+
- Iou Rider: 0.3690
|
| 55 |
+
- Iou Car: 0.8721
|
| 56 |
+
- Iou Truck: 0.5861
|
| 57 |
+
- Iou Bus: 0.7113
|
| 58 |
+
- Iou Train: 0.6222
|
| 59 |
+
- Iou Motorcycle: 0.4337
|
| 60 |
+
- Iou Bicycle: 0.5882
|
| 61 |
|
| 62 |
## Model description
|
| 63 |
|
|
|
|
| 76 |
### Training hyperparameters
|
| 77 |
|
| 78 |
The following hyperparameters were used during training:
|
| 79 |
+
- learning_rate: 0.0002
|
| 80 |
- train_batch_size: 16
|
| 81 |
- eval_batch_size: 16
|
| 82 |
- seed: 42
|
|
|
|
| 92 |
|
| 93 |
| Training Loss | Epoch | Step | Accuracy Bicycle | Accuracy Building | Accuracy Bus | Accuracy Car | Accuracy Fence | Accuracy Motorcycle | Accuracy Person | Accuracy Pole | Accuracy Rider | Accuracy Road | Accuracy Sidewalk | Accuracy Sky | Accuracy Terrain | Accuracy Traffic light | Accuracy Traffic sign | Accuracy Train | Accuracy Truck | Accuracy Vegetation | Accuracy Wall | Iou Bicycle | Iou Building | Iou Bus | Iou Car | Iou Fence | Iou Motorcycle | Iou Person | Iou Pole | Iou Rider | Iou Road | Iou Sidewalk | Iou Sky | Iou Terrain | Iou Traffic light | Iou Traffic sign | Iou Train | Iou Truck | Iou Vegetation | Iou Wall | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy |
|
| 94 |
|:-------------:|:-------:|:----:|:----------------:|:-----------------:|:------------:|:------------:|:--------------:|:-------------------:|:---------------:|:-------------:|:--------------:|:-------------:|:-----------------:|:------------:|:----------------:|:----------------------:|:---------------------:|:--------------:|:--------------:|:-------------------:|:-------------:|:-----------:|:------------:|:-------:|:-------:|:---------:|:--------------:|:----------:|:--------:|:---------:|:--------:|:------------:|:-------:|:-----------:|:-----------------:|:----------------:|:---------:|:---------:|:--------------:|:--------:|:---------------:|:-------------:|:--------:|:----------------:|
|
| 95 |
+
| 6.8123 | 2.1290 | 100 | 0.6984 | 0.9338 | 0.8122 | 0.9277 | 0.5514 | 0.5692 | 0.7411 | 0.5353 | 0.5676 | 0.9832 | 0.8382 | 0.9456 | 0.6402 | 0.6671 | 0.6933 | 0.6673 | 0.6179 | 0.9322 | 0.5422 | 0.5707 | 0.8673 | 0.7055 | 0.8721 | 0.4429 | 0.4264 | 0.5663 | 0.3490 | 0.3608 | 0.9568 | 0.7169 | 0.9046 | 0.5479 | 0.4801 | 0.5658 | 0.6310 | 0.5609 | 0.8722 | 0.4836 | 1.9557 | 0.7297 | 0.6253 | 0.9235 |
|
| 96 |
+
| 5.5587 | 4.2581 | 200 | 0.7256 | 0.9379 | 0.7677 | 0.9250 | 0.5431 | 0.5447 | 0.7266 | 0.5255 | 0.5780 | 0.9827 | 0.8446 | 0.9559 | 0.6846 | 0.6513 | 0.6983 | 0.7100 | 0.6409 | 0.9234 | 0.5933 | 0.5811 | 0.8659 | 0.6798 | 0.8731 | 0.4480 | 0.4214 | 0.5739 | 0.3477 | 0.3697 | 0.9569 | 0.7187 | 0.9050 | 0.5508 | 0.4799 | 0.5715 | 0.6571 | 0.5627 | 0.8720 | 0.5035 | 1.9616 | 0.7347 | 0.6284 | 0.9237 |
|
| 97 |
+
| 6.8729 | 6.3871 | 300 | 0.7543 | 0.9334 | 0.7977 | 0.9311 | 0.5201 | 0.4788 | 0.7494 | 0.5370 | 0.5464 | 0.9821 | 0.8502 | 0.9495 | 0.6429 | 0.6814 | 0.6879 | 0.6860 | 0.6341 | 0.9325 | 0.5513 | 0.5783 | 0.8654 | 0.7097 | 0.8738 | 0.4383 | 0.3938 | 0.5814 | 0.3495 | 0.3657 | 0.9587 | 0.7267 | 0.9026 | 0.5385 | 0.4817 | 0.5703 | 0.6481 | 0.5721 | 0.8733 | 0.4796 | 2.0128 | 0.7287 | 0.6267 | 0.9243 |
|
| 98 |
+
| 6.3252 | 8.5161 | 400 | 0.6642 | 0.9384 | 0.7729 | 0.9231 | 0.5929 | 0.4225 | 0.7956 | 0.5462 | 0.5785 | 0.9811 | 0.8564 | 0.9407 | 0.6866 | 0.6477 | 0.7079 | 0.7334 | 0.6215 | 0.9150 | 0.5680 | 0.5609 | 0.8657 | 0.6914 | 0.8719 | 0.4471 | 0.3463 | 0.5605 | 0.3529 | 0.3504 | 0.9575 | 0.7210 | 0.9026 | 0.5582 | 0.4800 | 0.5663 | 0.6632 | 0.5712 | 0.8702 | 0.4953 | 2.0218 | 0.7312 | 0.6228 | 0.9227 |
|
| 99 |
+
| 6.3303 | 10.6452 | 500 | 0.7177 | 0.9441 | 0.8494 | 0.9181 | 0.5622 | 0.5395 | 0.7004 | 0.5211 | 0.5410 | 0.9835 | 0.8381 | 0.9392 | 0.6200 | 0.6394 | 0.6761 | 0.6616 | 0.6112 | 0.9303 | 0.4396 | 0.5813 | 0.8632 | 0.6945 | 0.8714 | 0.4555 | 0.4227 | 0.5589 | 0.3463 | 0.3686 | 0.9574 | 0.7176 | 0.9037 | 0.5494 | 0.4400 | 0.5567 | 0.6219 | 0.5686 | 0.8730 | 0.4040 | 2.0294 | 0.7175 | 0.6187 | 0.9231 |
|
| 100 |
+
| 6.1549 | 12.7742 | 600 | 0.7508 | 0.9573 | 0.7949 | 0.9217 | 0.5561 | 0.6094 | 0.7175 | 0.4992 | 0.3451 | 0.9822 | 0.8461 | 0.9377 | 0.6346 | 0.6769 | 0.6496 | 0.6472 | 0.5465 | 0.8897 | 0.5458 | 0.5683 | 0.8503 | 0.6773 | 0.8694 | 0.4253 | 0.4378 | 0.5759 | 0.3383 | 0.2860 | 0.9581 | 0.7156 | 0.8943 | 0.5439 | 0.4632 | 0.5551 | 0.6143 | 0.5262 | 0.8543 | 0.4745 | 2.1125 | 0.7110 | 0.6120 | 0.9192 |
|
| 101 |
+
| 7.3083 | 14.9032 | 700 | 0.7100 | 0.9398 | 0.8242 | 0.9160 | 0.5903 | 0.6395 | 0.7402 | 0.5186 | 0.5756 | 0.9850 | 0.8476 | 0.9476 | 0.6635 | 0.6793 | 0.7236 | 0.6976 | 0.7515 | 0.9247 | 0.5481 | 0.5762 | 0.8665 | 0.7249 | 0.8740 | 0.4511 | 0.4341 | 0.5749 | 0.3518 | 0.3727 | 0.9589 | 0.7281 | 0.9005 | 0.5730 | 0.4763 | 0.5694 | 0.6395 | 0.6627 | 0.8741 | 0.4982 | 2.0793 | 0.7486 | 0.6372 | 0.9252 |
|
| 102 |
+
| 6.576 | 17.0215 | 800 | 0.7703 | 0.9409 | 0.7932 | 0.9190 | 0.5249 | 0.5203 | 0.7513 | 0.5143 | 0.5919 | 0.9821 | 0.8585 | 0.9530 | 0.6539 | 0.6359 | 0.6807 | 0.6271 | 0.6034 | 0.9322 | 0.4577 | 0.5755 | 0.8684 | 0.6832 | 0.8710 | 0.4431 | 0.3965 | 0.5864 | 0.3538 | 0.3662 | 0.9589 | 0.7242 | 0.9068 | 0.5658 | 0.4663 | 0.5700 | 0.5985 | 0.5316 | 0.8743 | 0.4129 | 2.0442 | 0.7216 | 0.6186 | 0.9247 |
|
| 103 |
+
| 6.1906 | 19.1505 | 900 | 0.6906 | 0.9467 | 0.8470 | 0.9224 | 0.5170 | 0.4961 | 0.7813 | 0.5332 | 0.5720 | 0.9847 | 0.8472 | 0.9447 | 0.6608 | 0.7185 | 0.7102 | 0.4138 | 0.5740 | 0.9189 | 0.4473 | 0.5695 | 0.8643 | 0.6888 | 0.8751 | 0.4150 | 0.3911 | 0.5883 | 0.3517 | 0.3777 | 0.9604 | 0.7371 | 0.9050 | 0.5748 | 0.4555 | 0.5705 | 0.4080 | 0.5304 | 0.8708 | 0.4035 | 2.0855 | 0.7119 | 0.6072 | 0.9242 |
|
| 104 |
+
| 7.41 | 21.2796 | 1000 | 0.7659 | 0.9404 | 0.7758 | 0.9210 | 0.5056 | 0.5361 | 0.7416 | 0.5777 | 0.6048 | 0.9830 | 0.8693 | 0.9475 | 0.6439 | 0.6821 | 0.7203 | 0.6634 | 0.6090 | 0.9171 | 0.4731 | 0.5747 | 0.8631 | 0.7228 | 0.8708 | 0.4197 | 0.4150 | 0.5772 | 0.3588 | 0.3654 | 0.9599 | 0.7358 | 0.9058 | 0.5547 | 0.4700 | 0.5752 | 0.6282 | 0.5567 | 0.8737 | 0.4317 | 2.0099 | 0.7304 | 0.6242 | 0.9239 |
|
| 105 |
+
| 5.5115 | 23.4086 | 1100 | 0.7564 | 0.9446 | 0.7604 | 0.9148 | 0.6497 | 0.5345 | 0.7515 | 0.5375 | 0.5970 | 0.9812 | 0.8744 | 0.9407 | 0.6428 | 0.6566 | 0.6718 | 0.7071 | 0.6244 | 0.9197 | 0.4471 | 0.5694 | 0.8659 | 0.7115 | 0.8685 | 0.4391 | 0.4190 | 0.5891 | 0.3549 | 0.3717 | 0.9597 | 0.7354 | 0.9072 | 0.5640 | 0.4857 | 0.5729 | 0.6665 | 0.5744 | 0.8741 | 0.4060 | 2.0197 | 0.7322 | 0.6282 | 0.9244 |
|
| 106 |
+
| 7.0305 | 25.5376 | 1200 | 0.7267 | 0.9504 | 0.7640 | 0.9260 | 0.5518 | 0.5643 | 0.7378 | 0.5290 | 0.5458 | 0.9839 | 0.8701 | 0.9395 | 0.6378 | 0.6620 | 0.6745 | 0.7368 | 0.6248 | 0.9167 | 0.5230 | 0.5713 | 0.8643 | 0.7156 | 0.8710 | 0.4571 | 0.4256 | 0.5882 | 0.3598 | 0.3668 | 0.9612 | 0.7406 | 0.9042 | 0.5554 | 0.4727 | 0.5726 | 0.6853 | 0.5750 | 0.8728 | 0.4675 | 1.9816 | 0.7297 | 0.6330 | 0.9258 |
|
| 107 |
+
| 5.2368 | 27.6667 | 1300 | 0.7217 | 0.9374 | 0.7948 | 0.9332 | 0.5699 | 0.5587 | 0.7443 | 0.5263 | 0.5490 | 0.9843 | 0.8599 | 0.9396 | 0.6362 | 0.6413 | 0.7024 | 0.7470 | 0.6401 | 0.9401 | 0.5039 | 0.5817 | 0.8697 | 0.7120 | 0.8732 | 0.4659 | 0.4127 | 0.5929 | 0.3582 | 0.3795 | 0.9610 | 0.7402 | 0.9039 | 0.5671 | 0.4764 | 0.5836 | 0.6913 | 0.5853 | 0.8782 | 0.4550 | 1.9868 | 0.7332 | 0.6362 | 0.9274 |
|
| 108 |
+
| 6.2172 | 29.7957 | 1400 | 0.7696 | 0.9371 | 0.7514 | 0.9329 | 0.5485 | 0.5655 | 0.7389 | 0.5560 | 0.5959 | 0.9858 | 0.8414 | 0.9414 | 0.6290 | 0.7111 | 0.6842 | 0.7097 | 0.5908 | 0.9342 | 0.5966 | 0.5896 | 0.8689 | 0.7058 | 0.8719 | 0.4575 | 0.4462 | 0.5838 | 0.3593 | 0.3561 | 0.9610 | 0.7383 | 0.9079 | 0.5570 | 0.4571 | 0.5676 | 0.6666 | 0.5487 | 0.8797 | 0.5196 | 2.0414 | 0.7379 | 0.6338 | 0.9268 |
|
| 109 |
+
| 5.8238 | 31.9247 | 1500 | 0.7155 | 0.9423 | 0.7631 | 0.9290 | 0.6080 | 0.5156 | 0.7468 | 0.5452 | 0.6095 | 0.9852 | 0.8504 | 0.9454 | 0.6533 | 0.6628 | 0.6916 | 0.6605 | 0.6024 | 0.9245 | 0.5715 | 0.5826 | 0.8678 | 0.7121 | 0.8697 | 0.4594 | 0.4105 | 0.5992 | 0.3578 | 0.3635 | 0.9609 | 0.7403 | 0.9070 | 0.5617 | 0.4771 | 0.5806 | 0.6362 | 0.5521 | 0.8765 | 0.5023 | 2.0376 | 0.7328 | 0.6325 | 0.9264 |
|
| 110 |
+
| 5.5947 | 34.0430 | 1600 | 0.7463 | 0.9465 | 0.8115 | 0.9198 | 0.5313 | 0.5075 | 0.7525 | 0.5320 | 0.5378 | 0.9834 | 0.8629 | 0.9352 | 0.6095 | 0.6516 | 0.6914 | 0.6838 | 0.6637 | 0.9242 | 0.5233 | 0.5755 | 0.8663 | 0.6922 | 0.8715 | 0.4515 | 0.4040 | 0.5862 | 0.3583 | 0.3677 | 0.9593 | 0.7355 | 0.9035 | 0.5453 | 0.4746 | 0.5805 | 0.6466 | 0.5881 | 0.8729 | 0.4686 | 1.9786 | 0.7271 | 0.6288 | 0.9253 |
|
| 111 |
+
| 6.8136 | 36.1720 | 1700 | 0.7539 | 0.9418 | 0.7692 | 0.9278 | 0.5493 | 0.5517 | 0.7664 | 0.5399 | 0.5394 | 0.9841 | 0.8605 | 0.9423 | 0.6269 | 0.6547 | 0.6952 | 0.6466 | 0.6442 | 0.9287 | 0.5673 | 0.5902 | 0.8702 | 0.7151 | 0.8725 | 0.4427 | 0.4200 | 0.5972 | 0.3619 | 0.3700 | 0.9602 | 0.7376 | 0.9088 | 0.5491 | 0.4791 | 0.5807 | 0.6186 | 0.5809 | 0.8764 | 0.4945 | 2.0119 | 0.7311 | 0.6329 | 0.9268 |
|
| 112 |
+
| 4.8895 | 39.0860 | 1800 | 1.9619 | 0.6350 | 0.7337 | 0.9277 | 0.9852 | 0.8497 | 0.9358 | 0.5532 | 0.5198 | 0.5455 | 0.6870 | 0.7070 | 0.9414 | 0.6285 | 0.9488 | 0.7732 | 0.5306 | 0.9299 | 0.6569 | 0.7685 | 0.6389 | 0.6015 | 0.7396 | 0.9605 | 0.7409 | 0.8722 | 0.4867 | 0.4444 | 0.3637 | 0.4816 | 0.5829 | 0.8786 | 0.5591 | 0.9110 | 0.6011 | 0.3690 | 0.8721 | 0.5861 | 0.7113 | 0.6222 | 0.4337 | 0.5882 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 113 |
|
| 114 |
|
| 115 |
### Framework versions
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 109496316
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:05d7270696edd88097201489d7edd2f661a6bacbb8c235128cf9b1281816ee7b
|
| 3 |
size 109496316
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 5368
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8e4daa28f0d33f952f29a6f4ea4df0f1de95f195e5fdbdd6ff60ebf7eed59904
|
| 3 |
size 5368
|