Feature Extraction
Transformers
PyTorch
roberta
code-understanding
unixcoder
text-embeddings-inference
Instructions to use Henry65/RepoSim4Py with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Henry65/RepoSim4Py with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Henry65/RepoSim4Py")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Henry65/RepoSim4Py") model = AutoModel.from_pretrained("Henry65/RepoSim4Py") - Notebooks
- Google Colab
- Kaggle
Update RepoPipeline.py
Browse files- RepoPipeline.py +1 -1
RepoPipeline.py
CHANGED
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@@ -143,7 +143,7 @@ def extract_information(repos, headers=None):
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# 4. Extracting requirements.
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elif member.name.endswith("requirements.txt") and member.isfile():
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try:
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-
lines = tar.extractfile(member).
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# extract readme
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requirements_set = extract_requirements(lines)
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repo_info["requirements"].update(requirements_set)
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# 4. Extracting requirements.
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elif member.name.endswith("requirements.txt") and member.isfile():
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try:
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+
lines = tar.extractfile(member).readlines()
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# extract readme
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requirements_set = extract_requirements(lines)
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repo_info["requirements"].update(requirements_set)
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