Instructions to use Linseypass/mirror-SPECTER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Linseypass/mirror-SPECTER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Linseypass/mirror-SPECTER")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Linseypass/mirror-SPECTER") model = AutoModel.from_pretrained("Linseypass/mirror-SPECTER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22bb98d0dd2e99f428b1ace1e72cfefddb3c8a46c6c503f16719270c34d1649d
- Size of remote file:
- 440 MB
- SHA256:
- 3a7247b9e06f78d7ef83da0ae7a2289d3968f8ebc7fb8b3750c1ffb3f80e69f3
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