Feature Extraction
Transformers
Safetensors
bert
author-embedding
embeddings
m5
repository-library
research-library
t1_metadata
text-embeddings-inference
Instructions to use PeytonT/author-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeytonT/author-embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PeytonT/author-embedding")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("PeytonT/author-embedding") model = AutoModel.from_pretrained("PeytonT/author-embedding") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb254b23e2c854751f57bb02088a7f601fc304a34cb702803ad7a929c13f3fe6
- Size of remote file:
- 5.84 kB
- SHA256:
- 026793c3971aa1140ebf99afd766b22dc1e3b0bffaa97c3fa66c906ddefa426a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.