Update README.md

README.md

@@ -21,4 +21,77 @@ configs:
     path: data/train-*
   - split: test
     path: data/test-*
+license: apache-2.0
+task_categories:
+- text-classification
+tags:
+- chemistry
+- biology
 ---
+
+
+# Dataset Card for Solubility Prediction Dataset
+
+### Dataset Summary
+
+This solubility prediction task involves a binary classification of a heterogenous set of proteins, assessing them as either soluble or insoluble. The solubility metric is a crucial design parameter in ensuring protein efficacy, with particular relevance in the pharmaceutical domain. 
+
+## Dataset Structure
+
+### Data Instances
+For each instance, there is a string representing the protein sequence and an integer label indicating that the protein sequence is soluble or insoluble. See the [solubility prediction dataset viewer](https://huggingface.co/datasets/Bo1015/solubility_prediction/viewer) to explore more examples.
+
+```
+{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
+'label':1}
+```
+
+The average  for the `seq` and the `label` are provided below:
+
+| Feature    | Mean Count |
+| ---------- | ---------------- |
+| seq    |    298   |
+| label (0)   |    0.58   |
+| label (1)   |    0.42   |
+
+
+
+
+### Data Fields
+
+- `seq`: a string containing the protein sequence
+- `label`: an integer label indicating that the protein sequence is soluble or insoluble.
+
+### Data Splits
+
+The solubility prediction dataset has 2 splits: _train_ and _test_. Below are the statistics of the dataset.
+
+| Dataset Split | Number of Instances in Split                |
+| ------------- | ------------------------------------------- |
+| Train         | 62,478                         |
+| Test          | 6,942                                   |
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The initialized dataset is adapted from [DeepSol](https://academic.oup.com/bioinformatics/article/34/15/2605/4938490). Within this framework, any protein exhibiting a sequence identity of 30% or greater to any protein within the test subset is eliminated from both the training subsets, ensuring robust and unbiased evaluation.
+
+### 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}
+}
+```