Update README.md

README.md

@@ -21,4 +21,87 @@ configs:
     path: data/train-*
   - split: test
     path: data/test-*
+license: apache-2.0
+task_categories:
+- text-classification
+tags:
+- chemistry
+- biology
+- medical
+size_categories:
+- 1K<n<10K
 ---
+
+
+# Dataset Card for Localization Prediction Dataset
+
+### Dataset Summary
+
+The task of Protein Subcellular Localization Prediction bears substantial relevance in bioinformatics, owing to its contributions to proteomics research and its potential to augment our comprehension of protein function and disease mechanisms. In this task, the input to the model is an amino acid sequence of a protein, which is transformed into an output comprising a probability distribution over 10 unique subcellular localization categories. 
+
+## Dataset Structure
+
+### Data Instances
+For each instance, there is a string representing the protein sequence and an integer label indicating which subcellular position the protein sequence locates at.  See the [localization prediction dataset viewer](https://huggingface.co/datasets/Bo1015/localization_prediction/viewer) to explore more examples.
+
+```
+{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
+'label':6}
+```
+
+The average  for the `seq` and the `label` are provided below:
+
+| Feature    | Mean Count |
+| ---------- | ---------------- |
+| seq    |    544   |
+| label (0) |    0.01   |
+| label (1)  |    0.10   |
+| label (2)  |    0.20   |
+| label (3)  |    0.03   |
+| label (4)  |    0.07   |
+| label (5)  |    0.06   |
+| label (6)  |    0.11   |
+| label (7)  |    0.34   |
+| label (8)  |    0.06   |
+| label (9)  |    0.02   |
+
+
+
+
+### Data Fields
+
+- `seq`: a string containing the protein sequence
+- `label`: an integer label indicating which subcellular position the protein sequence locates at.
+
+### Data Splits
+
+The localization prediction dataset has 2 splits: _train_ and _test_. Below are the statistics of the dataset.
+
+| Dataset Split | Number of Instances in Split                |
+| ------------- | ------------------------------------------- |
+| Train         | 6,622                   |
+| Test          | 1,842                           |
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+The dataset applied for this task is derived from Uniprot, meticulously curated by [DeepLoc](https://academic.oup.com/bioinformatics/article/33/21/3387/3931857).
+
+### 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}
+}
+```