factual-consistency-multilabel-classification-ja
This model is a fine-tuned version of line-corporation/line-distilbert-base-japanese on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3230
- Accuracy: 0.8701
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
Training results
| Training Loss |
Epoch |
Step |
Validation Loss |
Accuracy |
| No log |
1.0 |
306 |
0.4850 |
0.7861 |
| 0.5279 |
2.0 |
612 |
0.4657 |
0.7881 |
| 0.5279 |
3.0 |
918 |
0.4495 |
0.7979 |
| 0.4707 |
4.0 |
1224 |
0.4361 |
0.8115 |
| 0.448 |
5.0 |
1530 |
0.4252 |
0.8242 |
| 0.448 |
6.0 |
1836 |
0.4169 |
0.8271 |
| 0.4321 |
7.0 |
2142 |
0.4098 |
0.8369 |
| 0.4321 |
8.0 |
2448 |
0.4037 |
0.8389 |
| 0.4208 |
9.0 |
2754 |
0.3989 |
0.8428 |
| 0.4129 |
10.0 |
3060 |
0.3942 |
0.8447 |
| 0.4129 |
11.0 |
3366 |
0.3905 |
0.8457 |
| 0.4058 |
12.0 |
3672 |
0.3865 |
0.8467 |
| 0.4058 |
13.0 |
3978 |
0.3833 |
0.8496 |
| 0.4041 |
14.0 |
4284 |
0.3802 |
0.8496 |
| 0.3992 |
15.0 |
4590 |
0.3774 |
0.8506 |
| 0.3992 |
16.0 |
4896 |
0.3749 |
0.8525 |
| 0.3922 |
17.0 |
5202 |
0.3726 |
0.8525 |
| 0.3936 |
18.0 |
5508 |
0.3701 |
0.8535 |
| 0.3936 |
19.0 |
5814 |
0.3679 |
0.8535 |
| 0.3893 |
20.0 |
6120 |
0.3666 |
0.8535 |
| 0.3893 |
21.0 |
6426 |
0.3640 |
0.8555 |
| 0.3871 |
22.0 |
6732 |
0.3626 |
0.8545 |
| 0.3856 |
23.0 |
7038 |
0.3607 |
0.8555 |
| 0.3856 |
24.0 |
7344 |
0.3591 |
0.8564 |
| 0.3836 |
25.0 |
7650 |
0.3572 |
0.8584 |
| 0.3836 |
26.0 |
7956 |
0.3561 |
0.8604 |
| 0.3801 |
27.0 |
8262 |
0.3543 |
0.8604 |
| 0.3794 |
28.0 |
8568 |
0.3530 |
0.8613 |
| 0.3794 |
29.0 |
8874 |
0.3517 |
0.8633 |
| 0.379 |
30.0 |
9180 |
0.3505 |
0.8633 |
| 0.379 |
31.0 |
9486 |
0.3494 |
0.8633 |
| 0.377 |
32.0 |
9792 |
0.3482 |
0.8623 |
| 0.3765 |
33.0 |
10098 |
0.3471 |
0.8662 |
| 0.3765 |
34.0 |
10404 |
0.3465 |
0.8652 |
| 0.3739 |
35.0 |
10710 |
0.3456 |
0.8613 |
| 0.3737 |
36.0 |
11016 |
0.3441 |
0.8662 |
| 0.3737 |
37.0 |
11322 |
0.3435 |
0.8662 |
| 0.3723 |
38.0 |
11628 |
0.3426 |
0.8662 |
| 0.3723 |
39.0 |
11934 |
0.3418 |
0.8652 |
| 0.3728 |
40.0 |
12240 |
0.3410 |
0.8652 |
| 0.3713 |
41.0 |
12546 |
0.3401 |
0.8633 |
| 0.3713 |
42.0 |
12852 |
0.3395 |
0.8662 |
| 0.3686 |
43.0 |
13158 |
0.3392 |
0.8662 |
| 0.3686 |
44.0 |
13464 |
0.3378 |
0.8643 |
| 0.3693 |
45.0 |
13770 |
0.3375 |
0.8662 |
| 0.3685 |
46.0 |
14076 |
0.3365 |
0.8643 |
| 0.3685 |
47.0 |
14382 |
0.3362 |
0.8643 |
| 0.3675 |
48.0 |
14688 |
0.3352 |
0.8633 |
| 0.3675 |
49.0 |
14994 |
0.3349 |
0.8643 |
| 0.3654 |
50.0 |
15300 |
0.3341 |
0.8652 |
| 0.3672 |
51.0 |
15606 |
0.3339 |
0.8672 |
| 0.3672 |
52.0 |
15912 |
0.3335 |
0.8682 |
| 0.3659 |
53.0 |
16218 |
0.3325 |
0.8643 |
| 0.3648 |
54.0 |
16524 |
0.3323 |
0.8662 |
| 0.3648 |
55.0 |
16830 |
0.3315 |
0.8643 |
| 0.3638 |
56.0 |
17136 |
0.3314 |
0.8643 |
| 0.3638 |
57.0 |
17442 |
0.3307 |
0.8662 |
| 0.3657 |
58.0 |
17748 |
0.3304 |
0.8662 |
| 0.3641 |
59.0 |
18054 |
0.3299 |
0.8662 |
| 0.3641 |
60.0 |
18360 |
0.3297 |
0.8672 |
| 0.3624 |
61.0 |
18666 |
0.3294 |
0.8672 |
| 0.3624 |
62.0 |
18972 |
0.3290 |
0.8662 |
| 0.3625 |
63.0 |
19278 |
0.3285 |
0.8662 |
| 0.3639 |
64.0 |
19584 |
0.3281 |
0.8662 |
| 0.3639 |
65.0 |
19890 |
0.3282 |
0.8682 |
| 0.3632 |
66.0 |
20196 |
0.3274 |
0.8652 |
| 0.3618 |
67.0 |
20502 |
0.3273 |
0.8682 |
| 0.3618 |
68.0 |
20808 |
0.3270 |
0.8672 |
| 0.3636 |
69.0 |
21114 |
0.3267 |
0.8672 |
| 0.3636 |
70.0 |
21420 |
0.3265 |
0.8662 |
| 0.3577 |
71.0 |
21726 |
0.3262 |
0.8682 |
| 0.3607 |
72.0 |
22032 |
0.3262 |
0.8682 |
| 0.3607 |
73.0 |
22338 |
0.3258 |
0.8682 |
| 0.3591 |
74.0 |
22644 |
0.3255 |
0.8662 |
| 0.3591 |
75.0 |
22950 |
0.3255 |
0.8691 |
| 0.3597 |
76.0 |
23256 |
0.3252 |
0.8691 |
| 0.3593 |
77.0 |
23562 |
0.3250 |
0.8691 |
| 0.3593 |
78.0 |
23868 |
0.3248 |
0.8682 |
| 0.3597 |
79.0 |
24174 |
0.3246 |
0.8682 |
| 0.3597 |
80.0 |
24480 |
0.3244 |
0.8672 |
| 0.3593 |
81.0 |
24786 |
0.3243 |
0.8672 |
| 0.3602 |
82.0 |
25092 |
0.3242 |
0.8682 |
| 0.3602 |
83.0 |
25398 |
0.3242 |
0.8672 |
| 0.3579 |
84.0 |
25704 |
0.3242 |
0.8711 |
| 0.3614 |
85.0 |
26010 |
0.3238 |
0.8672 |
| 0.3614 |
86.0 |
26316 |
0.3238 |
0.8711 |
| 0.359 |
87.0 |
26622 |
0.3237 |
0.8701 |
| 0.359 |
88.0 |
26928 |
0.3236 |
0.8682 |
| 0.3583 |
89.0 |
27234 |
0.3235 |
0.8682 |
| 0.3591 |
90.0 |
27540 |
0.3234 |
0.8682 |
| 0.3591 |
91.0 |
27846 |
0.3233 |
0.8691 |
| 0.3581 |
92.0 |
28152 |
0.3231 |
0.8672 |
| 0.3581 |
93.0 |
28458 |
0.3232 |
0.8701 |
| 0.3579 |
94.0 |
28764 |
0.3231 |
0.8691 |
| 0.3584 |
95.0 |
29070 |
0.3231 |
0.8701 |
| 0.3584 |
96.0 |
29376 |
0.3230 |
0.8701 |
| 0.356 |
97.0 |
29682 |
0.3230 |
0.8691 |
| 0.356 |
98.0 |
29988 |
0.3230 |
0.8691 |
| 0.3604 |
99.0 |
30294 |
0.3230 |
0.8701 |
| 0.3607 |
100.0 |
30600 |
0.3230 |
0.8701 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
