Instructions to use Taykhoom/UTR-LM-MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taykhoom/UTR-LM-MLM with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Taykhoom/UTR-LM-MLM", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
CHANGED
|
@@ -102,12 +102,15 @@ The model follows standard HF conventions and can be fine-tuned with any Trainer
|
|
| 102 |
## Citation
|
| 103 |
|
| 104 |
```bibtex
|
| 105 |
-
@article{
|
| 106 |
-
title = {A 5'UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions},
|
| 107 |
-
author = {Chu, Yanyi and
|
| 108 |
-
journal = {
|
| 109 |
-
|
| 110 |
-
|
|
|
|
|
|
|
|
|
|
| 111 |
}
|
| 112 |
```
|
| 113 |
|
|
|
|
| 102 |
## Citation
|
| 103 |
|
| 104 |
```bibtex
|
| 105 |
+
@article{chu2024utrlm,
|
| 106 |
+
title = {A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions},
|
| 107 |
+
author = {Chu, Yanyi and Yu, Dan and Li, Yupeng and Huang, Kaixuan and Shen, Yue and Cong, Le and Zhang, Jason and Wang, Mengdi},
|
| 108 |
+
journal = {Nature Machine Intelligence},
|
| 109 |
+
volume = {6},
|
| 110 |
+
number = {4},
|
| 111 |
+
pages = {449--460},
|
| 112 |
+
year = {2024},
|
| 113 |
+
doi = {10.1038/s42256-024-00823-9}
|
| 114 |
}
|
| 115 |
```
|
| 116 |
|