Feature Extraction
Transformers
PyTorch
roberta
code-understanding
unixcoder
text-embeddings-inference
Instructions to use Lazyhope/RepoSim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lazyhope/RepoSim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Lazyhope/RepoSim")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Lazyhope/RepoSim") model = AutoModel.from_pretrained("Lazyhope/RepoSim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Fix a typo
Browse files- pipeline.py +2 -2
pipeline.py
CHANGED
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@@ -160,7 +160,7 @@ class RepoEmbeddingPipeline(Pipeline):
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for func in repo_info["funcs"]
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]
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entry["code_embeddings"] = code_embeddings
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entry["
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np.mean([x[1] for x in code_embeddings], axis=0).tolist()
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if code_embeddings
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else None
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@@ -171,7 +171,7 @@ class RepoEmbeddingPipeline(Pipeline):
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for doc in repo_info["docs"]
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]
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entry["doc_embeddings"] = doc_embeddings
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entry["
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np.mean([x[1] for x in doc_embeddings], axis=0).tolist()
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if doc_embeddings
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else None
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for func in repo_info["funcs"]
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]
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entry["code_embeddings"] = code_embeddings
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+
entry["mean_code_embedding"] = (
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np.mean([x[1] for x in code_embeddings], axis=0).tolist()
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if code_embeddings
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else None
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for doc in repo_info["docs"]
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]
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entry["doc_embeddings"] = doc_embeddings
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+
entry["mean_doc_embedding"] = (
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np.mean([x[1] for x in doc_embeddings], axis=0).tolist()
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if doc_embeddings
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else None
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