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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Henry65/RepoSim4Py: direct link, hf CLI and curl.
- Browser
- Download file 2.14 MB
-
https://huggingface.co/Henry65/RepoSim4Py/resolve/main/tokenizer.json
- Command line
-
hf download hf://Henry65/RepoSim4Py/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Henry65/RepoSim4Py/resolve/main/tokenizer.json
2.14 MB
File too large to display, you can check the raw version instead.