Update README.md

README.md

@@ -26,4 +26,78 @@ configs:
     path: data/valid-*
   - split: test
     path: data/test-*
+license: apache-2.0
+task_categories:
+- token-classification
+tags:
+- chemistry
+- biology
+size_categories:
+- 10K<n<100K
 ---
+
+
+# Dataset Card for Stability Stability Dataset
+
+### Dataset Summary
+
+The Stability Stability task is to predict the concentration of protease at which a protein can retain its folded state. Protease, being integral to numerous biological processes, bears significant relevance and a profound comprehension of protein stability during protease interaction can offer immense value, especially in the creation of novel therapeutics.
+
+## Dataset Structure
+
+### Data Instances
+For each instance, there is a string representing the protein sequence and a float number indicating the stability score. See the [stability prediction dataset viewer](https://huggingface.co/datasets/Bo1015/stability_prediction/viewer) to explore more examples.
+
+```
+{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
+'label':0.17}
+```
+
+The average  for the `seq` and the `label` are provided below:
+
+| Feature    | Mean Count |
+| ---------- | ---------------- |
+| seq    |    45   |
+| label    |    0.34   |
+
+
+
+### Data Fields
+
+- `seq`: a string containing the protein sequence
+- `label`: a float number indicating the stability score of each sequence.
+
+### Data Splits
+
+The secondary structure prediction dataset has 3 splits: _train_, _valid_, and _test_. Below are the statistics of the dataset.
+
+| Dataset Split | Number of Instances in Split                |
+| ------------- | ------------------------------------------- |
+| Train         | 53,614                          |
+| Valid         | 2,512                              |
+| Test          | 12,851                                      |
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The dataset applied in this task is initially sourced from [Rocklin et al](https://www.science.org/doi/10.1126/science.aan0693) and subsequently collected within the [TAPE](https://github.com/songlab-cal/tape).
+
+### 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}
+}
+```