Instructions to use microsoft/unixcoder-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/unixcoder-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="microsoft/unixcoder-base", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/unixcoder-base") model = AutoModel.from_pretrained("microsoft/unixcoder-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74b7004f2ae9c9e17c67a0ccabaaa5eebe1381a0c6439712b7d52c71328b73ed
- Size of remote file:
- 504 MB
- SHA256:
- b5a4aa079e24e6f520fa8bc4ba436ebc3266fdbf9c88607f6b0ad95f6e243b2c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.