Update README.md

README.md

@@ -21,4 +21,76 @@ 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 Antibiotic Resistance Dataset
+
+### Dataset Summary
+
+Antibiotic resistance refers to the ability of bacteria and other microorganisms to resist the effects of an antibiotic to which they are once sensitive. In this task, an input protein sequence is categorized according to which of 19 antibiotics it is resistant to. Thus, the scope of antibiotic drug development and research could be explored as an understanding in this topic is accumulated. 
+
+
+## Dataset Structure
+
+### Data Instances
+For each instance, there is a string representing the protein sequence and an integer label indicating which antibiotics a protein sequence is categorized to.  See the [antibiotic resistance dataset viewer](https://huggingface.co/datasets/Bo1015/antibiotic_resistance/viewer) to explore more examples.
+
+```
+{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
+'label':6}
+```
+
+The average  for the `seq` and the `label` are provided below:
+
+| Feature    | Mean Count |
+| ---------- | ---------------- |
+| seq    |    507   |
+
+
+
+
+### Data Fields
+
+- `seq`: a string containing the protein sequence
+- `label`: an integer label indicating which antibiotics a protein sequence is categorized to.
+
+### Data Splits
+
+The antibiotic resistance dataset has 2 splits: _train_ and _test_. Below are the statistics of the dataset.
+
+| Dataset Split | Number of Instances in Split                |
+| ------------- | ------------------------------------------- |
+| Train         | 2,072            |
+| Test          | 1,344                       |
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+The Dataset used in this task is curated by [CARD](https://card.mcmaster.ca/analyze/blast).
+
+
+### 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}
+}
+```