Duplicate from flwrlabs/code-alpaca-20k

Co-authored-by: Yan Gao <yangao381@users.noreply.huggingface.co>

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README.md

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+---
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- code
+pretty_name: CodeAlpaca 20K
+size_categories:
+- 10K<n<100K
+license: apache-2.0
+---
+# Dataset Card for CodeAlpaca 20K
+
+This dataset originates from the [Code Alpaca repository](https://github.com/sahil280114/codealpaca).
+The CodeAlpaca 20K dataset is specifically used for training code generation models.
+
+## Dataset Details
+
+### Dataset Description
+
+Each sample is comprised of three columns: instruction, input and output. 
+
+- **Language(s):** English
+- **License:** Apache-2.0 License
+
+### Dataset Sources
+
+The code from the original repository was adopted to post it here. 
+
+- **Repository:** https://github.com/sahil280114/codealpaca
+
+## Uses
+
+This dataset is primarily used for training code generation models. It can also be utilized in Federated Learning settings by partitioning the data into multiple shards (e.g. [FlowerTune LLM Leaderboard](https://flower.ai/benchmarks/llm-leaderboard/)).
+
+
+### Direct Use in FL
+This dataset can be used in FL settings. We recommend using [Flower Datasets](https://flower.ai/docs/datasets/) (flwr-datasets) and [Flower](https://flower.ai/docs/framework/) (flwr).
+
+To partition the dataset, do the following. 
+1. Install the package.
+```bash
+pip install flwr-datasets
+```
+2. Use the HF Dataset under the hood in Flower Datasets.
+```python
+from flwr_datasets import FederatedDataset
+from flwr_datasets.partitioner import IidPartitioner
+
+fds = FederatedDataset(
+    dataset="flwrlabs/code-alpaca-20k",
+    partitioners={"train": IidPartitioner(num_partitions=10)}
+)
+partition = fds.load_partition(partition_id=0)
+```
+
+## Dataset Structure
+
+The dataset contains only train split. Each sample is comprised of columns:
+* `instruction`: str - description of the coding task the model should perform. Each of the 20K instructions is unique.
+* `input`: str - optional context or input for the coding task.
+* `output`: str - the answer to the instruction as generated by `text-davinci-003`.
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset was created as a part of the [Code Alpaca repository](https://github.com/sahil280114/codealpaca).
+
+#### Data Collection and Processing
+
+For the preprocessing details, please refer to the source code.
+
+## Citation
+
+When working on the this dataset, please cite the original GitHub repository. If you're using this dataset with Flower Datasets, you can cite Flower.
+
+**BibTeX:**
+```
+@misc{codealpaca,
+  author = {Sahil Chaudhary},
+  title = {Code Alpaca: An Instruction-following LLaMA model for code generation},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/sahil280114/codealpaca}},
+}
+```
+```
+@article{DBLP:journals/corr/abs-2007-14390,
+  author       = {Daniel J. Beutel and
+                  Taner Topal and
+                  Akhil Mathur and
+                  Xinchi Qiu and
+                  Titouan Parcollet and
+                  Nicholas D. Lane},
+  title        = {Flower: {A} Friendly Federated Learning Research Framework},
+  journal      = {CoRR},
+  volume       = {abs/2007.14390},
+  year         = {2020},
+  url          = {https://arxiv.org/abs/2007.14390},
+  eprinttype    = {arXiv},
+  eprint       = {2007.14390},
+  timestamp    = {Mon, 03 Aug 2020 14:32:13 +0200},
+  biburl       = {https://dblp.org/rec/journals/corr/abs-2007-14390.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+## Dataset Card Contact
+
+In case of any doubts, please contact [Flower Labs](https://flower.ai/).