Mozart-coder
commited on
Commit
•
dd76509
1
Parent(s):
3180f4d
update model card README.md
Browse files
README.md
ADDED
@@ -0,0 +1,155 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
tags:
|
3 |
+
- generated_from_trainer
|
4 |
+
model-index:
|
5 |
+
- name: dna_bert_3_1000seq-finetuned
|
6 |
+
results: []
|
7 |
+
---
|
8 |
+
|
9 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
10 |
+
should probably proofread and complete it, then remove this comment. -->
|
11 |
+
|
12 |
+
# dna_bert_3_1000seq-finetuned
|
13 |
+
|
14 |
+
This model is a fine-tuned version of [armheb/DNA_bert_3](https://huggingface.co/armheb/DNA_bert_3) on an unknown dataset.
|
15 |
+
It achieves the following results on the evaluation set:
|
16 |
+
- Loss: 0.4684
|
17 |
+
|
18 |
+
## Model description
|
19 |
+
|
20 |
+
More information needed
|
21 |
+
|
22 |
+
## Intended uses & limitations
|
23 |
+
|
24 |
+
More information needed
|
25 |
+
|
26 |
+
## Training and evaluation data
|
27 |
+
|
28 |
+
More information needed
|
29 |
+
|
30 |
+
## Training procedure
|
31 |
+
|
32 |
+
### Training hyperparameters
|
33 |
+
|
34 |
+
The following hyperparameters were used during training:
|
35 |
+
- learning_rate: 2e-05
|
36 |
+
- train_batch_size: 16
|
37 |
+
- eval_batch_size: 16
|
38 |
+
- seed: 42
|
39 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
40 |
+
- lr_scheduler_type: linear
|
41 |
+
- num_epochs: 100
|
42 |
+
- mixed_precision_training: Native AMP
|
43 |
+
|
44 |
+
### Training results
|
45 |
+
|
46 |
+
| Training Loss | Epoch | Step | Validation Loss |
|
47 |
+
|:-------------:|:-----:|:----:|:---------------:|
|
48 |
+
| 0.8607 | 1.0 | 62 | 0.6257 |
|
49 |
+
| 0.6177 | 2.0 | 124 | 0.6120 |
|
50 |
+
| 0.6098 | 3.0 | 186 | 0.6062 |
|
51 |
+
| 0.604 | 4.0 | 248 | 0.6052 |
|
52 |
+
| 0.5999 | 5.0 | 310 | 0.6040 |
|
53 |
+
| 0.5982 | 6.0 | 372 | 0.5996 |
|
54 |
+
| 0.5985 | 7.0 | 434 | 0.5985 |
|
55 |
+
| 0.5956 | 8.0 | 496 | 0.5968 |
|
56 |
+
| 0.5936 | 9.0 | 558 | 0.5950 |
|
57 |
+
| 0.5908 | 10.0 | 620 | 0.5941 |
|
58 |
+
| 0.5904 | 11.0 | 682 | 0.5932 |
|
59 |
+
| 0.59 | 12.0 | 744 | 0.5917 |
|
60 |
+
| 0.5877 | 13.0 | 806 | 0.5921 |
|
61 |
+
| 0.5847 | 14.0 | 868 | 0.5903 |
|
62 |
+
| 0.5831 | 15.0 | 930 | 0.5887 |
|
63 |
+
| 0.5852 | 16.0 | 992 | 0.5878 |
|
64 |
+
| 0.5805 | 17.0 | 1054 | 0.5872 |
|
65 |
+
| 0.5795 | 18.0 | 1116 | 0.5853 |
|
66 |
+
| 0.5754 | 19.0 | 1178 | 0.5869 |
|
67 |
+
| 0.5757 | 20.0 | 1240 | 0.5839 |
|
68 |
+
| 0.5722 | 21.0 | 1302 | 0.5831 |
|
69 |
+
| 0.5693 | 22.0 | 1364 | 0.5811 |
|
70 |
+
| 0.5667 | 23.0 | 1426 | 0.5802 |
|
71 |
+
| 0.5652 | 24.0 | 1488 | 0.5775 |
|
72 |
+
| 0.5608 | 25.0 | 1550 | 0.5788 |
|
73 |
+
| 0.5591 | 26.0 | 1612 | 0.5724 |
|
74 |
+
| 0.5538 | 27.0 | 1674 | 0.5736 |
|
75 |
+
| 0.552 | 28.0 | 1736 | 0.5689 |
|
76 |
+
| 0.5483 | 29.0 | 1798 | 0.5689 |
|
77 |
+
| 0.5442 | 30.0 | 1860 | 0.5671 |
|
78 |
+
| 0.5405 | 31.0 | 1922 | 0.5658 |
|
79 |
+
| 0.537 | 32.0 | 1984 | 0.5605 |
|
80 |
+
| 0.5349 | 33.0 | 2046 | 0.5575 |
|
81 |
+
| 0.5275 | 34.0 | 2108 | 0.5569 |
|
82 |
+
| 0.5227 | 35.0 | 2170 | 0.5537 |
|
83 |
+
| 0.52 | 36.0 | 2232 | 0.5509 |
|
84 |
+
| 0.5173 | 37.0 | 2294 | 0.5504 |
|
85 |
+
| 0.5123 | 38.0 | 2356 | 0.5435 |
|
86 |
+
| 0.5088 | 39.0 | 2418 | 0.5472 |
|
87 |
+
| 0.5037 | 40.0 | 2480 | 0.5383 |
|
88 |
+
| 0.501 | 41.0 | 2542 | 0.5379 |
|
89 |
+
| 0.4931 | 42.0 | 2604 | 0.5365 |
|
90 |
+
| 0.4923 | 43.0 | 2666 | 0.5328 |
|
91 |
+
| 0.4879 | 44.0 | 2728 | 0.5301 |
|
92 |
+
| 0.482 | 45.0 | 2790 | 0.5295 |
|
93 |
+
| 0.4805 | 46.0 | 2852 | 0.5261 |
|
94 |
+
| 0.4772 | 47.0 | 2914 | 0.5221 |
|
95 |
+
| 0.4738 | 48.0 | 2976 | 0.5234 |
|
96 |
+
| 0.4674 | 49.0 | 3038 | 0.5210 |
|
97 |
+
| 0.4646 | 50.0 | 3100 | 0.5169 |
|
98 |
+
| 0.4621 | 51.0 | 3162 | 0.5142 |
|
99 |
+
| 0.4574 | 52.0 | 3224 | 0.5129 |
|
100 |
+
| 0.4552 | 53.0 | 3286 | 0.5127 |
|
101 |
+
| 0.4539 | 54.0 | 3348 | 0.5124 |
|
102 |
+
| 0.4506 | 55.0 | 3410 | 0.5076 |
|
103 |
+
| 0.4457 | 56.0 | 3472 | 0.5082 |
|
104 |
+
| 0.4454 | 57.0 | 3534 | 0.5027 |
|
105 |
+
| 0.4398 | 58.0 | 3596 | 0.5019 |
|
106 |
+
| 0.4386 | 59.0 | 3658 | 0.4998 |
|
107 |
+
| 0.4332 | 60.0 | 3720 | 0.4970 |
|
108 |
+
| 0.4277 | 61.0 | 3782 | 0.4995 |
|
109 |
+
| 0.4273 | 62.0 | 3844 | 0.4962 |
|
110 |
+
| 0.4235 | 63.0 | 3906 | 0.4909 |
|
111 |
+
| 0.4201 | 64.0 | 3968 | 0.4913 |
|
112 |
+
| 0.4198 | 65.0 | 4030 | 0.4899 |
|
113 |
+
| 0.4182 | 66.0 | 4092 | 0.4919 |
|
114 |
+
| 0.4157 | 67.0 | 4154 | 0.4902 |
|
115 |
+
| 0.4104 | 68.0 | 4216 | 0.4881 |
|
116 |
+
| 0.4095 | 69.0 | 4278 | 0.4881 |
|
117 |
+
| 0.4077 | 70.0 | 4340 | 0.4861 |
|
118 |
+
| 0.4064 | 71.0 | 4402 | 0.4868 |
|
119 |
+
| 0.4041 | 72.0 | 4464 | 0.4826 |
|
120 |
+
| 0.4029 | 73.0 | 4526 | 0.4833 |
|
121 |
+
| 0.3976 | 74.0 | 4588 | 0.4819 |
|
122 |
+
| 0.3997 | 75.0 | 4650 | 0.4809 |
|
123 |
+
| 0.3974 | 76.0 | 4712 | 0.4801 |
|
124 |
+
| 0.3953 | 77.0 | 4774 | 0.4783 |
|
125 |
+
| 0.3938 | 78.0 | 4836 | 0.4775 |
|
126 |
+
| 0.3934 | 79.0 | 4898 | 0.4762 |
|
127 |
+
| 0.3923 | 80.0 | 4960 | 0.4742 |
|
128 |
+
| 0.3893 | 81.0 | 5022 | 0.4742 |
|
129 |
+
| 0.3909 | 82.0 | 5084 | 0.4740 |
|
130 |
+
| 0.3856 | 83.0 | 5146 | 0.4739 |
|
131 |
+
| 0.3904 | 84.0 | 5208 | 0.4740 |
|
132 |
+
| 0.3883 | 85.0 | 5270 | 0.4701 |
|
133 |
+
| 0.3865 | 86.0 | 5332 | 0.4727 |
|
134 |
+
| 0.3809 | 87.0 | 5394 | 0.4736 |
|
135 |
+
| 0.3853 | 88.0 | 5456 | 0.4704 |
|
136 |
+
| 0.3821 | 89.0 | 5518 | 0.4704 |
|
137 |
+
| 0.3809 | 90.0 | 5580 | 0.4701 |
|
138 |
+
| 0.3814 | 91.0 | 5642 | 0.4698 |
|
139 |
+
| 0.3795 | 92.0 | 5704 | 0.4702 |
|
140 |
+
| 0.3804 | 93.0 | 5766 | 0.4692 |
|
141 |
+
| 0.377 | 94.0 | 5828 | 0.4683 |
|
142 |
+
| 0.3812 | 95.0 | 5890 | 0.4692 |
|
143 |
+
| 0.3806 | 96.0 | 5952 | 0.4683 |
|
144 |
+
| 0.3745 | 97.0 | 6014 | 0.4690 |
|
145 |
+
| 0.3825 | 98.0 | 6076 | 0.4684 |
|
146 |
+
| 0.374 | 99.0 | 6138 | 0.4687 |
|
147 |
+
| 0.3795 | 100.0 | 6200 | 0.4684 |
|
148 |
+
|
149 |
+
|
150 |
+
### Framework versions
|
151 |
+
|
152 |
+
- Transformers 4.21.1
|
153 |
+
- Pytorch 1.12.0+cu113
|
154 |
+
- Datasets 2.4.0
|
155 |
+
- Tokenizers 0.12.1
|