Feature Extraction
Transformers
English
context-compression
rag
extractive-summarization
token-classification
Instructions to use gziz/snippet-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gziz/snippet-extraction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="gziz/snippet-extraction")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("gziz/snippet-extraction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e49623c34224f0cd6ef50dd3117c59787412f3b5337d242de5e14b883ad505d
- Size of remote file:
- 596 MB
- SHA256:
- cac692287b8dbdf4cbff85d176ab18380cb6c78e9d3c1e7cef29fc720f7eca8b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.