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:
- 8865ac11e26137705e1eb97dc4df7ed52e667906f127c6197efc8a2cd06d6b9e
- Size of remote file:
- 90.9 MB
- SHA256:
- d77e39f35a0ea5a083b3d4365200c627cc9d573276559cd8d86ac9e34eafdf48
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