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 README.md
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README.md
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@@ -47,7 +47,7 @@ Then specify one (or multiple repositories in a tuple) as input and get the resu
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repo_infos = model("lazyhope/python-hello-world")
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print(repo_infos)
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```
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Output (Long numpy outputs are omitted):
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```python
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[{'name': 'lazyhope/python-hello-world',
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'topics': [],
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repo_infos = model("lazyhope/python-hello-world")
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print(repo_infos)
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```
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Output (Long numpy array outputs are omitted):
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```python
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[{'name': 'lazyhope/python-hello-world',
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'topics': [],
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