Feature Extraction
Transformers
PyTorch
roberta
code-understanding
unixcoder
text-embeddings-inference
Instructions to use Lazyhope/RepoSim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lazyhope/RepoSim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Lazyhope/RepoSim")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Lazyhope/RepoSim") model = AutoModel.from_pretrained("Lazyhope/RepoSim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Lazyhope/RepoSim: direct link, hf CLI and curl.
- Browser
- Download file 2.14 MB
-
https://huggingface.co/Lazyhope/RepoSim/resolve/main/tokenizer.json
- Command line
-
hf download hf://Lazyhope/RepoSim/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Lazyhope/RepoSim/resolve/main/tokenizer.json
2.14 MB
File too large to display, you can check the raw version instead.