Update README.md

README.md

@@ -26,4 +26,79 @@ configs:
     path: data/valid-*
   - split: test
     path: data/test-*
+license: apache-2.0
+task_categories:
+- text-classification
+tags:
+- chemistry
+- biology
+size_categories:
+- 1K<n<10K
 ---
+
+
+# Dataset Card for Optimal PH Dataset
+
+### Dataset Summary
+
+Enzyme functions normally under a specific range of pH of the surrounding environment. However, an optimal pH for the reaction will largely boost the catalytic ability.
+
+## Dataset Structure
+
+### Data Instances
+For each instance, there is a string representing the protein sequence and a float value indicating the optimal ph for a given enzyme’s catalytic effect.  See the [optimal ph dataset viewer](https://huggingface.co/datasets/Bo1015/optimal_ph/viewer) to explore more examples.
+
+```
+{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
+'label':6.5}
+```
+
+The average  for the `seq` and the `label` are provided below:
+
+| Feature    | Mean Count |
+| ---------- | ---------------- |
+| seq    |    427   |
+| label  |    7.2   |
+
+
+
+
+### Data Fields
+
+- `seq`: a string containing the protein sequence
+- `label`: a float value indicating the optimal ph for a given enzyme’s catalytic effect.
+
+### Data Splits
+
+The Optimal PH dataset has 3 splits: _train_, _valid_, and _test_. Below are the statistics of the dataset.
+
+| Dataset Split | Number of Instances in Split                |
+| ------------- | ------------------------------------------- |
+| Train         | 7,124                      |
+| Valid         | 760                        |
+| Test          | 1,971                           |
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The dataset is collected from [EpHod](https://github.com/jafetgado/EpHod), which established a deep learning method to predict the optimal enzyme catalytic pH based on protein sequence only. 
+
+### 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}
+}
+```