Downloads · 30 days
41
5% of all-time downloads
DNABert_K6_G_quad
roa7n/DNABert_K6_G_quad
DNABert_K6_G_quad is a text classification model from roa7n. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
All-time downloads
791
Public
Repo size
168 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin357 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Oct 27, 2022
- Updated
- Oct 28, 2022
- SHA
- b2fc072a
Try a prompt
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
DNABert_K6_G_quad
This model is a fine-tuned version of armheb/DNA_bert_6 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2424
- Accuracy: 0.9737
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0927 | 1.0 | 9375 | 0.0818 | 0.9719 |
| 0.0681 | 2.0 | 18750 | 0.0714 | 0.9756 |
| 0.0607 | 3.0 | 28125 | 0.0863 | 0.9734 |
| 0.055 | 4.0 | 37500 | 0.0787 | 0.9757 |
| 0.0496 | 5.0 | 46875 | 0.0882 | 0.9758 |
| 0.0445 | 6.0 | 56250 | 0.0968 | 0.9752 |
| 0.0391 | 7.0 | 65625 | 0.1024 | 0.9755 |
| 0.0345 | 8.0 | 75000 | 0.1108 | 0.9739 |
| 0.0304 | 9.0 | 84375 | 0.1235 | 0.9745 |
| 0.0261 | 10.0 | 93750 | 0.1348 | 0.9730 |
| 0.023 | 11.0 | 103125 | 0.1427 | 0.9733 |
| 0.0197 | 12.0 | 112500 | 0.1462 | 0.9738 |
| 0.0182 | 13.0 | 121875 | 0.1570 | 0.9730 |
| 0.0145 | 14.0 | 131250 | 0.1757 | 0.9729 |
| 0.0122 | 15.0 | 140625 | 0.1911 | 0.9735 |
| 0.0108 | 16.0 | 150000 | 0.1977 | 0.9736 |
| 0.01 | 17.0 | 159375 | 0.1993 | 0.9732 |
| 0.0083 | 18.0 | 168750 | 0.2172 | 0.9736 |
| 0.0074 | 19.0 | 178125 | 0.2242 | 0.9740 |
| 0.0059 | 20.0 | 187500 | 0.2245 | 0.9732 |
| 0.0058 | 21.0 | 196875 | 0.2306 | 0.9733 |
| 0.0043 | 22.0 | 206250 | 0.2414 | 0.9737 |
| 0.0044 | 23.0 | 215625 | 0.2394 | 0.9735 |
| 0.0039 | 24.0 | 225000 | 0.2420 | 0.9736 |
| 0.0032 | 25.0 | 234375 | 0.2424 | 0.9737 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Oct 27, 2022
- Updated
- Oct 28, 2022