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 +2 -2
RepoPipeline.py
CHANGED
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@@ -157,9 +157,9 @@ class RepoPipeline(Pipeline):
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def generate_embeddings(self, text_sets, max_length):
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assert max_length < 1024
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return torch.
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if text_sets is None or len(text_sets) == 0 \
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-
else torch.
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def _forward(self, extracted_infos: List, max_length=512) -> List:
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model_outputs = []
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def generate_embeddings(self, text_sets, max_length):
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assert max_length < 1024
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return torch.zeros((1, 768), device=self.device) \
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if text_sets is None or len(text_sets) == 0 \
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else torch.cat([self.encode(text, max_length) for text in text_sets], dim=0)
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def _forward(self, extracted_infos: List, max_length=512) -> List:
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model_outputs = []
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