Hugging Face's logo

Kobi-01
/
tamil-qa-distilled

PEFT
Safetensors
Generated from Trainer
Model card Files
xet
Community
Kobi-01 commited on
Commit
b1efd0a
·
verified ·
1 Parent(s): e5445c3

Kobi-01/distilled-tamil-qa-v2

Browse files
Files changed (4) hide show
  1. README.md +78 -45
  2. adapter_config.json +2 -2
  3. adapter_model.safetensors +1 -1
  4. training_args.bin +1 -1
README.md CHANGED
@@ -18,16 +18,16 @@ should probably proofread and complete it, then remove this comment. -->
18
 
19
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
20
  It achieves the following results on the evaluation set:
21
- - Loss: 2.4959
22
- - Exact: 22.3153
23
- - F1: 37.4456
24
  - Total: 5848
25
- - Hasans Exact: 22.3153
26
- - Hasans F1: 37.4456
27
  - Hasans Total: 5848
28
- - Best Exact: 22.3153
29
  - Best Exact Thresh: 0.0
30
- - Best F1: 37.4456
31
  - Best F1 Thresh: 0.0
32
 
33
  ## Model description
@@ -53,53 +53,86 @@ The following hyperparameters were used during training:
53
  - seed: 42
54
  - gradient_accumulation_steps: 2
55
  - total_train_batch_size: 8
56
- - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
57
  - lr_scheduler_type: linear
58
- - num_epochs: 1
59
 
60
  ### Training results
61
 
62
- | Training Loss | Epoch | Step | Validation Loss | Exact | F1 | Total | Hasans Exact | Hasans F1 | Hasans Total | Best Exact | Best Exact Thresh | Best F1 | Best F1 Thresh |
63
- |:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:----------:|:-----------------:|:-------:|:--------------:|
64
- | 5.4467 | 0.0302 | 250 | 4.6445 | 0.4275 | 6.2970 | 5848 | 0.4275 | 6.2970 | 5848 | 0.4275 | 0.0 | 6.2970 | 0.0 |
65
- | 4.3568 | 0.0604 | 500 | 4.0214 | 4.8906 | 12.5359 | 5848 | 4.8906 | 12.5359 | 5848 | 4.8906 | 0.0 | 12.5359 | 0.0 |
66
- | 4.0656 | 0.0905 | 750 | 3.7904 | 12.5342 | 24.2632 | 5848 | 12.5342 | 24.2632 | 5848 | 12.5342 | 0.0 | 24.2632 | 0.0 |
67
- | 3.8889 | 0.1207 | 1000 | 3.3827 | 15.5438 | 27.3077 | 5848 | 15.5438 | 27.3077 | 5848 | 15.5438 | 0.0 | 27.3077 | 0.0 |
68
- | 3.4289 | 0.1509 | 1250 | 3.1672 | 13.8338 | 24.8643 | 5848 | 13.8338 | 24.8643 | 5848 | 13.8338 | 0.0 | 24.8643 | 0.0 |
69
- | 3.5355 | 0.1811 | 1500 | 3.0776 | 16.9802 | 30.2997 | 5848 | 16.9802 | 30.2997 | 5848 | 16.9802 | 0.0 | 30.2997 | 0.0 |
70
- | 3.1612 | 0.2112 | 1750 | 2.9511 | 19.4425 | 33.6814 | 5848 | 19.4425 | 33.6814 | 5848 | 19.4425 | 0.0 | 33.6814 | 0.0 |
71
- | 3.2976 | 0.2414 | 2000 | 3.0930 | 20.0752 | 34.6007 | 5848 | 20.0752 | 34.6007 | 5848 | 20.0752 | 0.0 | 34.6007 | 0.0 |
72
- | 3.2589 | 0.2716 | 2250 | 2.8639 | 18.9808 | 32.7633 | 5848 | 18.9808 | 32.7633 | 5848 | 18.9808 | 0.0 | 32.7633 | 0.0 |
73
- | 2.9672 | 0.3018 | 2500 | 2.9398 | 20.4001 | 35.6524 | 5848 | 20.4001 | 35.6524 | 5848 | 20.4001 | 0.0 | 35.6524 | 0.0 |
74
- | 3.7545 | 0.3319 | 2750 | 2.9454 | 19.2886 | 33.4999 | 5848 | 19.2886 | 33.4999 | 5848 | 19.2886 | 0.0 | 33.4999 | 0.0 |
75
- | 2.8654 | 0.3621 | 3000 | 2.7416 | 18.7244 | 31.0609 | 5848 | 18.7244 | 31.0609 | 5848 | 18.7244 | 0.0 | 31.0609 | 0.0 |
76
- | 3.0861 | 0.3923 | 3250 | 2.7491 | 21.0499 | 35.7036 | 5848 | 21.0499 | 35.7036 | 5848 | 21.0499 | 0.0 | 35.7036 | 0.0 |
77
- | 2.7695 | 0.4225 | 3500 | 2.8284 | 21.5287 | 36.4741 | 5848 | 21.5287 | 36.4741 | 5848 | 21.5287 | 0.0 | 36.4741 | 0.0 |
78
- | 3.0057 | 0.4526 | 3750 | 2.7043 | 20.1436 | 34.2750 | 5848 | 20.1436 | 34.2750 | 5848 | 20.1436 | 0.0 | 34.2750 | 0.0 |
79
- | 2.9906 | 0.4828 | 4000 | 2.6032 | 20.1607 | 33.6974 | 5848 | 20.1607 | 33.6974 | 5848 | 20.1607 | 0.0 | 33.6974 | 0.0 |
80
- | 2.7957 | 0.5130 | 4250 | 2.7008 | 20.9815 | 36.0642 | 5848 | 20.9815 | 36.0642 | 5848 | 20.9815 | 0.0 | 36.0642 | 0.0 |
81
- | 2.8885 | 0.5432 | 4500 | 2.6539 | 21.5116 | 37.0821 | 5848 | 21.5116 | 37.0821 | 5848 | 21.5116 | 0.0 | 37.0821 | 0.0 |
82
- | 2.7088 | 0.5733 | 4750 | 2.6216 | 22.1956 | 37.0885 | 5848 | 22.1956 | 37.0885 | 5848 | 22.1956 | 0.0 | 37.0885 | 0.0 |
83
- | 2.5385 | 0.6035 | 5000 | 2.7195 | 22.1614 | 37.4103 | 5848 | 22.1614 | 37.4103 | 5848 | 22.1614 | 0.0 | 37.4103 | 0.0 |
84
- | 2.7104 | 0.6337 | 5250 | 2.5939 | 21.0841 | 36.2480 | 5848 | 21.0841 | 36.2480 | 5848 | 21.0841 | 0.0 | 36.2480 | 0.0 |
85
- | 2.9083 | 0.6639 | 5500 | 2.5586 | 21.8707 | 37.0920 | 5848 | 21.8707 | 37.0920 | 5848 | 21.8707 | 0.0 | 37.0920 | 0.0 |
86
- | 2.7255 | 0.6940 | 5750 | 2.5521 | 21.3406 | 36.6577 | 5848 | 21.3406 | 36.6577 | 5848 | 21.3406 | 0.0 | 36.6577 | 0.0 |
87
- | 2.6606 | 0.7242 | 6000 | 2.6845 | 22.7770 | 38.6065 | 5848 | 22.7770 | 38.6065 | 5848 | 22.7770 | 0.0 | 38.6065 | 0.0 |
88
- | 2.7477 | 0.7544 | 6250 | 2.5134 | 21.3919 | 36.2724 | 5848 | 21.3919 | 36.2724 | 5848 | 21.3919 | 0.0 | 36.2724 | 0.0 |
89
- | 2.6319 | 0.7846 | 6500 | 2.5086 | 21.2722 | 35.6261 | 5848 | 21.2722 | 35.6261 | 5848 | 21.2722 | 0.0 | 35.6261 | 0.0 |
90
- | 2.687 | 0.8147 | 6750 | 2.6354 | 23.4097 | 39.3830 | 5848 | 23.4097 | 39.3830 | 5848 | 23.4097 | 0.0 | 39.3830 | 0.0 |
91
- | 2.6415 | 0.8449 | 7000 | 2.4868 | 21.7339 | 36.0235 | 5848 | 21.7339 | 36.0235 | 5848 | 21.7339 | 0.0 | 36.0235 | 0.0 |
92
- | 2.6671 | 0.8751 | 7250 | 2.5276 | 22.5718 | 38.0853 | 5848 | 22.5718 | 38.0853 | 5848 | 22.5718 | 0.0 | 38.0853 | 0.0 |
93
- | 2.489 | 0.9053 | 7500 | 2.4679 | 21.7852 | 36.3686 | 5848 | 21.7852 | 36.3686 | 5848 | 21.7852 | 0.0 | 36.3686 | 0.0 |
94
- | 2.6783 | 0.9354 | 7750 | 2.4992 | 22.1101 | 37.3694 | 5848 | 22.1101 | 37.3694 | 5848 | 22.1101 | 0.0 | 37.3694 | 0.0 |
95
- | 2.75 | 0.9656 | 8000 | 2.5187 | 22.4692 | 37.8004 | 5848 | 22.4692 | 37.8004 | 5848 | 22.4692 | 0.0 | 37.8004 | 0.0 |
96
- | 2.7079 | 0.9958 | 8250 | 2.4959 | 22.3153 | 37.4456 | 5848 | 22.3153 | 37.4456 | 5848 | 22.3153 | 0.0 | 37.4456 | 0.0 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
97
 
98
 
99
  ### Framework versions
100
 
101
  - PEFT 0.14.0
102
  - Transformers 4.48.2
103
- - Pytorch 2.5.1+cu124
104
  - Datasets 3.2.0
105
  - Tokenizers 0.21.0
 
18
 
19
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
20
  It achieves the following results on the evaluation set:
21
+ - Loss: 2.3458
22
+ - Exact: 23.9056
23
+ - F1: 40.0870
24
  - Total: 5848
25
+ - Hasans Exact: 23.9056
26
+ - Hasans F1: 40.0870
27
  - Hasans Total: 5848
28
+ - Best Exact: 23.9056
29
  - Best Exact Thresh: 0.0
30
+ - Best F1: 40.0870
31
  - Best F1 Thresh: 0.0
32
 
33
  ## Model description
 
53
  - seed: 42
54
  - gradient_accumulation_steps: 2
55
  - total_train_batch_size: 8
56
+ - optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
57
  - lr_scheduler_type: linear
58
+ - num_epochs: 2
59
 
60
  ### Training results
61
 
62
+ | Training Loss | Epoch | Step | Validation Loss | Exact | F1 | Total | Hasans Exact | Hasans F1 | Hasans Total | Best Exact | Best Exact Thresh | Best F1 | Best F1 Thresh |
63
+ |:-------------:|:------:|:-----:|:---------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:----------:|:-----------------:|:-------:|:--------------:|
64
+ | 5.9372 | 0.0302 | 250 | 5.5830 | 0.0342 | 5.2752 | 5848 | 0.0342 | 5.2752 | 5848 | 0.0342 | 0.0 | 5.2752 | 0.0 |
65
+ | 4.4435 | 0.0604 | 500 | 4.1190 | 5.5404 | 14.4289 | 5848 | 5.5404 | 14.4289 | 5848 | 5.5404 | 0.0 | 14.4289 | 0.0 |
66
+ | 4.1362 | 0.0905 | 750 | 3.8871 | 11.4227 | 22.5581 | 5848 | 11.4227 | 22.5581 | 5848 | 11.4227 | 0.0 | 22.5581 | 0.0 |
67
+ | 4.071 | 0.1207 | 1000 | 3.6548 | 12.6197 | 23.8673 | 5848 | 12.6197 | 23.8673 | 5848 | 12.6197 | 0.0 | 23.8673 | 0.0 |
68
+ | 3.4979 | 0.1509 | 1250 | 3.3795 | 12.7052 | 23.1488 | 5848 | 12.7052 | 23.1488 | 5848 | 12.7052 | 0.0 | 23.1488 | 0.0 |
69
+ | 3.5602 | 0.1811 | 1500 | 3.1890 | 16.9631 | 28.6981 | 5848 | 16.9631 | 28.6981 | 5848 | 16.9631 | 0.0 | 28.6981 | 0.0 |
70
+ | 3.1678 | 0.2112 | 1750 | 3.0806 | 17.1512 | 31.1741 | 5848 | 17.1512 | 31.1741 | 5848 | 17.1512 | 0.0 | 31.1741 | 0.0 |
71
+ | 3.3402 | 0.2414 | 2000 | 3.0524 | 20.2975 | 34.5701 | 5848 | 20.2975 | 34.5701 | 5848 | 20.2975 | 0.0 | 34.5701 | 0.0 |
72
+ | 3.2099 | 0.2716 | 2250 | 2.8973 | 17.4761 | 30.0263 | 5848 | 17.4761 | 30.0263 | 5848 | 17.4761 | 0.0 | 30.0263 | 0.0 |
73
+ | 2.9404 | 0.3018 | 2500 | 2.9263 | 20.3830 | 35.2615 | 5848 | 20.3830 | 35.2615 | 5848 | 20.3830 | 0.0 | 35.2615 | 0.0 |
74
+ | 3.5376 | 0.3319 | 2750 | 2.8066 | 19.1518 | 33.0098 | 5848 | 19.1518 | 33.0098 | 5848 | 19.1518 | 0.0 | 33.0098 | 0.0 |
75
+ | 2.8949 | 0.3621 | 3000 | 2.7580 | 18.1601 | 30.1449 | 5848 | 18.1601 | 30.1449 | 5848 | 18.1601 | 0.0 | 30.1449 | 0.0 |
76
+ | 3.1584 | 0.3923 | 3250 | 2.8535 | 21.9904 | 37.2070 | 5848 | 21.9904 | 37.2070 | 5848 | 21.9904 | 0.0 | 37.2070 | 0.0 |
77
+ | 2.7833 | 0.4225 | 3500 | 2.8549 | 21.7339 | 36.0716 | 5848 | 21.7339 | 36.0716 | 5848 | 21.7339 | 0.0 | 36.0716 | 0.0 |
78
+ | 2.9223 | 0.4526 | 3750 | 2.7775 | 19.8529 | 33.9325 | 5848 | 19.8529 | 33.9325 | 5848 | 19.8529 | 0.0 | 33.9325 | 0.0 |
79
+ | 3.0183 | 0.4828 | 4000 | 2.6326 | 17.8865 | 29.0245 | 5848 | 17.8865 | 29.0245 | 5848 | 17.8865 | 0.0 | 29.0245 | 0.0 |
80
+ | 2.8363 | 0.5130 | 4250 | 2.7080 | 22.2298 | 37.7239 | 5848 | 22.2298 | 37.7239 | 5848 | 22.2298 | 0.0 | 37.7239 | 0.0 |
81
+ | 2.7222 | 0.5432 | 4500 | 2.8187 | 22.1785 | 38.5146 | 5848 | 22.1785 | 38.5146 | 5848 | 22.1785 | 0.0 | 38.5146 | 0.0 |
82
+ | 2.6763 | 0.5733 | 4750 | 2.6953 | 22.5547 | 37.7873 | 5848 | 22.5547 | 37.7873 | 5848 | 22.5547 | 0.0 | 37.7873 | 0.0 |
83
+ | 2.5925 | 0.6035 | 5000 | 2.8178 | 22.6573 | 38.2837 | 5848 | 22.6573 | 38.2837 | 5848 | 22.6573 | 0.0 | 38.2837 | 0.0 |
84
+ | 2.6485 | 0.6337 | 5250 | 2.6554 | 22.1101 | 37.5204 | 5848 | 22.1101 | 37.5204 | 5848 | 22.1101 | 0.0 | 37.5204 | 0.0 |
85
+ | 3.0327 | 0.6639 | 5500 | 2.6043 | 21.9733 | 37.8703 | 5848 | 21.9733 | 37.8703 | 5848 | 21.9733 | 0.0 | 37.8703 | 0.0 |
86
+ | 2.7016 | 0.6940 | 5750 | 2.5752 | 22.1272 | 38.2156 | 5848 | 22.1272 | 38.2156 | 5848 | 22.1272 | 0.0 | 38.2156 | 0.0 |
87
+ | 2.6785 | 0.7242 | 6000 | 2.6829 | 22.5889 | 38.4805 | 5848 | 22.5889 | 38.4805 | 5848 | 22.5889 | 0.0 | 38.4805 | 0.0 |
88
+ | 2.7414 | 0.7544 | 6250 | 2.5243 | 21.8023 | 36.6811 | 5848 | 21.8023 | 36.6811 | 5848 | 21.8023 | 0.0 | 36.6811 | 0.0 |
89
+ | 2.6025 | 0.7846 | 6500 | 2.4759 | 22.2811 | 36.9185 | 5848 | 22.2811 | 36.9185 | 5848 | 22.2811 | 0.0 | 36.9185 | 0.0 |
90
+ | 2.6305 | 0.8147 | 6750 | 2.6849 | 23.3926 | 39.6983 | 5848 | 23.3926 | 39.6983 | 5848 | 23.3926 | 0.0 | 39.6983 | 0.0 |
91
+ | 2.6485 | 0.8449 | 7000 | 2.5377 | 23.2900 | 38.9523 | 5848 | 23.2900 | 38.9523 | 5848 | 23.2900 | 0.0 | 38.9523 | 0.0 |
92
+ | 2.6644 | 0.8751 | 7250 | 2.5613 | 22.6915 | 37.9573 | 5848 | 22.6915 | 37.9573 | 5848 | 22.6915 | 0.0 | 37.9573 | 0.0 |
93
+ | 2.4504 | 0.9053 | 7500 | 2.5179 | 23.1019 | 39.2826 | 5848 | 23.1019 | 39.2826 | 5848 | 23.1019 | 0.0 | 39.2826 | 0.0 |
94
+ | 2.7274 | 0.9354 | 7750 | 2.4839 | 23.3242 | 39.6564 | 5848 | 23.3242 | 39.6564 | 5848 | 23.3242 | 0.0 | 39.6564 | 0.0 |
95
+ | 2.732 | 0.9656 | 8000 | 2.4473 | 22.6915 | 38.1896 | 5848 | 22.6915 | 38.1896 | 5848 | 22.6915 | 0.0 | 38.1896 | 0.0 |
96
+ | 2.5412 | 0.9958 | 8250 | 2.4536 | 22.8112 | 38.1155 | 5848 | 22.8112 | 38.1155 | 5848 | 22.8112 | 0.0 | 38.1155 | 0.0 |
97
+ | 2.4763 | 1.0260 | 8500 | 2.4868 | 23.6491 | 39.5944 | 5848 | 23.6491 | 39.5944 | 5848 | 23.6491 | 0.0 | 39.5944 | 0.0 |
98
+ | 2.6547 | 1.0561 | 8750 | 2.5451 | 23.8030 | 40.6998 | 5848 | 23.8030 | 40.6998 | 5848 | 23.8030 | 0.0 | 40.6998 | 0.0 |
99
+ | 2.5899 | 1.0863 | 9000 | 2.4332 | 22.6915 | 37.8246 | 5848 | 22.6915 | 37.8246 | 5848 | 22.6915 | 0.0 | 37.8246 | 0.0 |
100
+ | 2.5589 | 1.1165 | 9250 | 2.3779 | 22.0417 | 36.8825 | 5848 | 22.0417 | 36.8825 | 5848 | 22.0417 | 0.0 | 36.8825 | 0.0 |
101
+ | 2.3319 | 1.1467 | 9500 | 2.3715 | 22.7086 | 38.7405 | 5848 | 22.7086 | 38.7405 | 5848 | 22.7086 | 0.0 | 38.7405 | 0.0 |
102
+ | 2.2501 | 1.1768 | 9750 | 2.4244 | 23.4781 | 39.4466 | 5848 | 23.4781 | 39.4466 | 5848 | 23.4781 | 0.0 | 39.4466 | 0.0 |
103
+ | 2.337 | 1.2070 | 10000 | 2.3661 | 22.5718 | 36.8759 | 5848 | 22.5718 | 36.8759 | 5848 | 22.5718 | 0.0 | 36.8759 | 0.0 |
104
+ | 2.5074 | 1.2372 | 10250 | 2.6080 | 25.1368 | 42.1434 | 5848 | 25.1368 | 42.1434 | 5848 | 25.1368 | 0.0 | 42.1434 | 0.0 |
105
+ | 2.531 | 1.2674 | 10500 | 2.3560 | 21.6484 | 36.3479 | 5848 | 21.6484 | 36.3479 | 5848 | 21.6484 | 0.0 | 36.3479 | 0.0 |
106
+ | 2.513 | 1.2975 | 10750 | 2.4430 | 23.2387 | 39.3702 | 5848 | 23.2387 | 39.3702 | 5848 | 23.2387 | 0.0 | 39.3702 | 0.0 |
107
+ | 2.2461 | 1.3277 | 11000 | 2.5602 | 23.6149 | 40.3775 | 5848 | 23.6149 | 40.3775 | 5848 | 23.6149 | 0.0 | 40.3775 | 0.0 |
108
+ | 2.5934 | 1.3579 | 11250 | 2.4806 | 22.8625 | 39.5232 | 5848 | 22.8625 | 39.5232 | 5848 | 22.8625 | 0.0 | 39.5232 | 0.0 |
109
+ | 2.2671 | 1.3881 | 11500 | 2.4027 | 22.7599 | 37.6504 | 5848 | 22.7599 | 37.6504 | 5848 | 22.7599 | 0.0 | 37.6504 | 0.0 |
110
+ | 2.3451 | 1.4182 | 11750 | 2.4227 | 23.1703 | 39.2540 | 5848 | 23.1703 | 39.2540 | 5848 | 23.1703 | 0.0 | 39.2540 | 0.0 |
111
+ | 2.6997 | 1.4484 | 12000 | 2.3992 | 23.2216 | 39.6402 | 5848 | 23.2216 | 39.6402 | 5848 | 23.2216 | 0.0 | 39.6402 | 0.0 |
112
+ | 2.5253 | 1.4786 | 12250 | 2.4076 | 24.1621 | 40.3112 | 5848 | 24.1621 | 40.3112 | 5848 | 24.1621 | 0.0 | 40.3112 | 0.0 |
113
+ | 2.6091 | 1.5088 | 12500 | 2.3743 | 22.4179 | 38.6880 | 5848 | 22.4179 | 38.6880 | 5848 | 22.4179 | 0.0 | 38.6880 | 0.0 |
114
+ | 2.3445 | 1.5389 | 12750 | 2.3862 | 23.9056 | 39.8296 | 5848 | 23.9056 | 39.8296 | 5848 | 23.9056 | 0.0 | 39.8296 | 0.0 |
115
+ | 2.4083 | 1.5691 | 13000 | 2.5380 | 24.8461 | 41.8272 | 5848 | 24.8461 | 41.8272 | 5848 | 24.8461 | 0.0 | 41.8272 | 0.0 |
116
+ | 2.2722 | 1.5993 | 13250 | 2.3600 | 23.5123 | 39.5265 | 5848 | 23.5123 | 39.5265 | 5848 | 23.5123 | 0.0 | 39.5265 | 0.0 |
117
+ | 2.4945 | 1.6295 | 13500 | 2.3862 | 23.9056 | 39.8474 | 5848 | 23.9056 | 39.8474 | 5848 | 23.9056 | 0.0 | 39.8474 | 0.0 |
118
+ | 2.5895 | 1.6596 | 13750 | 2.3518 | 23.4952 | 39.1746 | 5848 | 23.4952 | 39.1746 | 5848 | 23.4952 | 0.0 | 39.1746 | 0.0 |
119
+ | 2.3354 | 1.6898 | 14000 | 2.3872 | 23.9398 | 39.7158 | 5848 | 23.9398 | 39.7158 | 5848 | 23.9398 | 0.0 | 39.7158 | 0.0 |
120
+ | 2.4015 | 1.7200 | 14250 | 2.3248 | 23.4439 | 39.5145 | 5848 | 23.4439 | 39.5145 | 5848 | 23.4439 | 0.0 | 39.5145 | 0.0 |
121
+ | 2.4879 | 1.7502 | 14500 | 2.3493 | 24.1279 | 40.3504 | 5848 | 24.1279 | 40.3504 | 5848 | 24.1279 | 0.0 | 40.3504 | 0.0 |
122
+ | 2.1585 | 1.7803 | 14750 | 2.3772 | 24.0424 | 40.2189 | 5848 | 24.0424 | 40.2189 | 5848 | 24.0424 | 0.0 | 40.2189 | 0.0 |
123
+ | 2.307 | 1.8105 | 15000 | 2.3749 | 23.8543 | 40.0715 | 5848 | 23.8543 | 40.0715 | 5848 | 23.8543 | 0.0 | 40.0715 | 0.0 |
124
+ | 2.2712 | 1.8407 | 15250 | 2.3785 | 24.1279 | 40.3913 | 5848 | 24.1279 | 40.3913 | 5848 | 24.1279 | 0.0 | 40.3913 | 0.0 |
125
+ | 2.2997 | 1.8709 | 15500 | 2.3398 | 23.6320 | 39.4552 | 5848 | 23.6320 | 39.4552 | 5848 | 23.6320 | 0.0 | 39.4552 | 0.0 |
126
+ | 2.7463 | 1.9010 | 15750 | 2.3623 | 23.9227 | 39.9884 | 5848 | 23.9227 | 39.9884 | 5848 | 23.9227 | 0.0 | 39.9884 | 0.0 |
127
+ | 2.6714 | 1.9312 | 16000 | 2.3396 | 24.0424 | 40.0280 | 5848 | 24.0424 | 40.0280 | 5848 | 24.0424 | 0.0 | 40.0280 | 0.0 |
128
+ | 2.4979 | 1.9614 | 16250 | 2.3225 | 23.7004 | 39.5895 | 5848 | 23.7004 | 39.5895 | 5848 | 23.7004 | 0.0 | 39.5895 | 0.0 |
129
+ | 2.4623 | 1.9916 | 16500 | 2.3458 | 23.9056 | 40.0870 | 5848 | 23.9056 | 40.0870 | 5848 | 23.9056 | 0.0 | 40.0870 | 0.0 |
130
 
131
 
132
  ### Framework versions
133
 
134
  - PEFT 0.14.0
135
  - Transformers 4.48.2
136
+ - Pytorch 2.5.1+cu121
137
  - Datasets 3.2.0
138
  - Tokenizers 0.21.0
adapter_config.json CHANGED
@@ -25,9 +25,9 @@
25
  "rank_pattern": {},
26
  "revision": null,
27
  "target_modules": [
28
- "query",
29
  "value",
30
- "key"
 
31
  ],
32
  "task_type": "QUESTION_ANS",
33
  "use_dora": false,
 
25
  "rank_pattern": {},
26
  "revision": null,
27
  "target_modules": [
 
28
  "value",
29
+ "key",
30
+ "query"
31
  ],
32
  "task_type": "QUESTION_ANS",
33
  "use_dora": false,
adapter_model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:d9c6ba3a85a32e32221f2dca795d190d8d872c79a5225855e30f798a003dfa62
3
  size 1782956
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:407ec4870ef0c90e8b01900b67a1122e221d65d23b37a89202da11fedb1ed0ab
3
  size 1782956
training_args.bin CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:5f87101fd38e2eda11193a40cac4c0b424922fc592447cdbf67cf5eb75c49945
3
  size 5304
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:359e09b86dc962b98a4da1d8e6fd0474a9e02ad98d0ef77715e95eb52f3199e1
3
  size 5304