Update the model card and make the pipeline public

README.md

@@ -1,3 +1,85 @@
 ---
 license: mit
+tags:
+- code-understanding
+- unixcoder
 ---
+
+# RepoSim
+
+An approach to compare semantic similarities between Python repositories.
+
+## Model Details
+
+**RepoSim** is a pipeline used to create embeddings for specified Python repositories on GitHub. For each repository, it extracts and encodes all functions' source code and docstrings into embeddings, then average them to get the mean code embeddings and the mean docstring embeddings, which can be used to perform various tasks such as cosine similarity comparison.
+
+### Model Description
+
+The model used by **RepoSim** is **UniXcoder** fine-tuned on [code search task](https://github.com/microsoft/CodeBERT/tree/master/UniXcoder/downstream-tasks/code-search), using the [AdvTest](https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-Adv) dataset.
+
+- **Pipeline developed by:** [Lazyhope](https://huggingface.co/Lazyhope)
+- **Repository:** [RepoSim](https://github.com/RepoAnalysis/RepoSim)
+- **Model type:** **code understanding**
+- **Language(s):** **Python**
+- **License:** **MIT**
+
+### Model Sources
+
+- **Repository:** [UniXcoder](https://github.com/microsoft/CodeBERT/tree/master/UniXcoder)
+- **Paper:** [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://arxiv.org/pdf/2203.03850.pdf)
+
+## Uses
+
+Below is an example of how to use the RepoSim pipeline to easily generate embeddings for GitHub Python repositories.
+
+First, initialise the pipeline:
+```python
+from transformers import pipeline
+
+model = pipeline(model="Lazyhope/RepoSim", trust_remote_code=True)
+```
+Then specify one (or multiple repositories in a tuple) as input and get the result as a list of dictionaries:
+```python
+repo_infos = model("lazyhope/python-hello-world")
+print(repo_infos)
+```
+Output (Long tensor outputs are omitted):
+```python
+[{'name': 'lazyhope/python-hello-world',
+  'topics': [],
+  'license': 'MIT',
+  'stars': 0,
+  'code_embeddings': [["def main():\n    print('Hello World!')",
+    [-2.0755109786987305,
+     2.813878297805786,
+     2.352170467376709, ...]]],
+  'mean_code_embedding': [-2.0755109786987305,
+   2.813878297805786,
+   2.352170467376709, ...],
+  'doc_embeddings': [['Prints hello world',
+    [-2.3749449253082275,
+     0.5409570336341858,
+     2.2958014011383057, ...]]],
+  'mean_doc_embedding': [-2.3749449253082275,
+   0.5409570336341858,
+   2.2958014011383057, ...]}]
+```
+
+## Training Details
+
+Please follow the original [UniXcoder](https://github.com/microsoft/CodeBERT/tree/master/UniXcoder/downstream-tasks/code-search) page for details of fine-tuning it on code search task.
+
+## Evaluation
+
+We used the [awesome-python](https://github.com/vinta/awesome-python) list which contains over 500 Python repositories categorized in different topics, in order to label similar repositories.
+The evaluation metrics and results can be found in the RepoSim repository, under the [notebooks](https://github.com/RepoAnalysis/RepoSim/tree/main/notebooks) folder. 
+
+## Acknowledgements 
+Many thanks to authors of the UniXcoder model and the AdvTest dataset, as well as the awesome python list for providing a useful baseline.
+- **UniXcoder** (https://github.com/microsoft/CodeBERT/tree/master/UniXcoder)
+- **AdvTest** (https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-Adv)
+- **awesome-python** (https://github.com/vinta/awesome-python)
+
+## Authors
+- **Zihao Li** (https://github.com/lazyhope)
+- **Rosa Filgueira** (https://www.rosafilgueira.com)