Update README.md

README.md

@@ -26,4 +26,80 @@ configs:
     path: data/valid-*
   - split: test
     path: data/test-*
+license: apache-2.0
+task_categories:
+- text-classification
+tags:
+- chemistry
+- biology
+size_categories:
+- 10K<n<100K
 ---
+
+
+# Dataset Card for Peptide-HLA/MHC Affinity  Dataset
+
+### Dataset Summary
+
+The human leukocyte antigen (HLA) gene encodes major histo-compatibility complex (MHC) proteins, which can bind to peptide fragments and be presented to the cell surface for subsequent T cell receptors (TCRs) recognition. Accurately predicting the interaction between peptide sequence and HLA molecule will boost the understanding of immune responses, antigen presentation, and designing therapeutic interventions such as peptide-based vaccines or immunotherapies. 
+
+## Dataset Structure
+
+### Data Instances
+For each instance, there is a string representing the protein sequence and an integer label indicating that whether a given paired peptide and HLA sequence can bind or not.  See the [peptide-HLA/MHC affinity dataset viewer](https://huggingface.co/datasets/Bo1015/peptide_HLA_MHC_affinity/viewer) to explore more examples.
+
+```
+{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
+'label':1}
+```
+
+The average  for the `seq` and the `label` are provided below:
+
+| Feature    | Mean Count |
+| ---------- | ---------------- |
+| seq    |    45   |
+| label (0)   |    0.5   |
+| label (1)   |    0.5   |
+
+
+
+
+### Data Fields
+
+- `seq`: a string containing the protein sequence
+- `label`: an integer label indicating that whether a given paired peptide and HLA sequence can bind or not.
+
+### Data Splits
+
+The Peptide-HLA/MHC Affinity dataset has 3 splits: _train_, _valid_, and _test_. Below are the statistics of the dataset.
+
+| Dataset Split | Number of Instances in Split                |
+| ------------- | ------------------------------------------- |
+| Train         | 57,357                         |
+| Valid         | 7,008                       |
+| Test          | 8,406                                 |
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+The modeling data is from [Wu et al](https://www.biorxiv.org/content/10.1101/2023.04.24.538196v1). The raw dataset contains millions of samples, we used the same split and downsample 1% for training and 5% for validation and testing (57,357 training samples, 7,008 validation samples and 8,406 test samples).
+
+
+### 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}
+}
+```