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
- Xet hash:
- c6abd1868f23c2323ca651fe3157b7934d0918e34e397963e3c476f072302a6d
- Size of remote file:
- 504 MB
- SHA256:
- ec359ccef197b85f9cea791cc2af5728aafd1adcd06713e2a3fb8290c43df3e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.