Update README.md

README.md

@@ -27,3 +27,66 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# INFORMATION FROM [HERE](https://huggingface.co/datasets/biomap-research/temperature_stability) PLEASE CITE THEIR PAPER BELOW
+
+### Dataset Summary
+
+The accurate prediction of protein thermal stability has far-reaching implications in both academic and industrial spheres. This task primarily aims to predict a protein’s capacity to preserve its structural stability under a temperature condition of 65 degrees Celsius. 
+
+## Dataset Structure
+
+### Data Instances
+For each instance, there is a string representing the protein sequence and an integer label indicating whether the protein can maintain its structural stability at a temperature of 65 degrees Celsius. See the [temperature stability dataset viewer](https://huggingface.co/datasets/Bo1015/temperature_stability/viewer) to explore more examples.
+
+```
+{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
+'label':1}
+```
+
+The average  for the `seq` and the `label` are provided below:
+
+| Feature    | Mean Count |
+| ---------- | ---------------- |
+| seq    | 300      |
+
+
+### Data Fields
+
+- `seq`: a string containing the protein sequence
+- `label`: an integer label indicating the structural stability of each sequence.
+
+### Data Splits
+
+The temperature stability dataset has 3 splits: _train_, _valid_, and _test_. Below are the statistics of the dataset.
+
+| Dataset Split | Number of Instances in Split                |
+| ------------- | ------------------------------------------- |
+| Train         | 283,057                            |
+| Valid         | 62,973                              |
+| Test          | 73,205                                      |
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+We adapted the dataset strategy from [TemStaPro](https://academic.oup.com/bioinformatics/article/40/4/btae157/7632735).
+
+### Licensing Information
+
+The dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0). 
+
+### Citation
+If you find our work useful, please consider citing the following paper:
+
+```
+@misc{chen2024xtrimopglm,
+  title={xTrimoPGLM: unified 100B-scale pre-trained transformer for deciphering the language of protein},
+  author={Chen, Bo and Cheng, Xingyi and Li, Pan and Geng, Yangli-ao and Gong, Jing and Li, Shen and Bei, Zhilei and Tan, Xu and Wang, Boyan and Zeng, Xin and others},
+  year={2024},
+  eprint={2401.06199},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  note={arXiv preprint arXiv:2401.06199}
+}
+```