Update README.md

README.md

@@ -34,40 +34,46 @@ The model used by **RepoSim4Py** is **UniXcoder** fine-tuned on [code search tas
 
 ## Uses
 
-Below is an example of how to use the RepoSim pipeline to easily generate embeddings for GitHub Python repositories.
+Below is an example of how to use the RepoSim4Py 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)
+model = pipeline(model="Henry65/RepoSim4Py", 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):
+Output (Long numpy 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, ...]}]
+  'code_embeddings': array([[-2.07551336e+00,  2.81387949e+00,  2.35216689e+00, ...]], dtype=float32),
+  'mean_code_embedding': array([[-2.07551336e+00,  2.81387949e+00,  2.35216689e+00, ...]], dtype=float32),
+  'doc_embeddings': array([[-2.37494540e+00,  5.40957630e-01,  2.29580235e+00, ...]], dtype=float32),
+  'mean_doc_embedding': array([[-2.37494540e+00,  5.40957630e-01,  2.29580235e+00, ...]], dtype=float32),
+  'requirement_embeddings': array([[0., 0., 0., ...]], dtype=float32),
+  'mean_requirement_embedding': array([[0., 0., 0., ...]], dtype=float32),
+  'readme_embeddings': array([[-2.1671042 ,  2.8404987 ,  1.4761417 , ...]], dtype=float32),
+  'mean_readme_embedding': array([[-1.91171765e+00,  1.65386486e+00,  9.49612021e-01, ...]], dtype=float32),
+  'mean_repo_embedding': array([[-2.0755134,  2.8138795,  2.352167 , ...]], dtype=float32),
+  'code_embeddings_shape': (1, 768)
+  'mean_code_embedding_shape': (1, 768)
+  'doc_embeddings_shape': (1, 768)
+  'mean_doc_embedding_shape': (1, 768)
+  'requirement_embeddings_shape': (1, 768)
+  'mean_requirement_embedding_shape': (1, 768)
+  'readme_embeddings_shape': (3, 768)
+  'mean_readme_embedding_shape': (1, 768)
+  'mean_repo_embedding_shape': (1, 3072)
+}]
 ```
+More specific information please refer to [Example.py](https://github.com/RepoMining/RepoSim4Py/blob/main/Script/Example.py). Note that "github_token" is unnecessary.
 
 ## Training Details
 
@@ -75,8 +81,8 @@ Please follow the original [UniXcoder](https://github.com/microsoft/CodeBERT/tre
 
 ## 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. 
+We used the [awesome-python](https://github.com/vinta/awesome-python) list which contains over 400 Python repositories categorized in different topics, in order to label similar repositories.
+The evaluation metrics and results can be found in the RepoSim4Py repository, under the [Embedding](https://github.com/RepoMining/RepoSim4Py/tree/main/Embedding) 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.
@@ -85,5 +91,5 @@ Many thanks to authors of the UniXcoder model and the AdvTest dataset, as well a
 - **awesome-python** (https://github.com/vinta/awesome-python)
 
 ## Authors
-- **Zihao Li** (https://github.com/lazyhope)
+- **Honglin Zhang** (https://github.com/liaomu0926)
 - **Rosa Filgueira** (https://www.rosafilgueira.com)