Instructions to use malteos/aspect-scibert-method with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use malteos/aspect-scibert-method with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="malteos/aspect-scibert-method")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("malteos/aspect-scibert-method") model = AutoModel.from_pretrained("malteos/aspect-scibert-method", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from malteos/aspect-scibert-method: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/malteos/aspect-scibert-method/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://malteos/aspect-scibert-method/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/malteos/aspect-scibert-method/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- 78e8c5c60e104ba92f2933b2da54d820872af94cac04ddfc54a2f9a6cf6f6d5c
- Size of remote file:
- 440 MB
- SHA256:
- 527bd71bf6bac1e77ed2d5a46767f13f1e8759c3c5dfb71d47b9c771407dcd7f
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