Feature Extraction
Transformers
Safetensors
bert
research-library
repository-library
repo-paper-alignment
c2
t5_cross
v2
text-embeddings-inference
Instructions to use PeytonT/research-library-c2-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeytonT/research-library-c2-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PeytonT/research-library-c2-v2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("PeytonT/research-library-c2-v2") model = AutoModel.from_pretrained("PeytonT/research-library-c2-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 930ac78b8809861c778bb19a8919bcf9dc35eef5d02c3d5ed6c6f49aa63dddfb
- Size of remote file:
- 5.84 kB
- SHA256:
- 383b0d9367d26f54dbca2373a9a6dd8668214300e2bc5d29cf0b9a381693d4d6
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