Instructions to use malteos/aspect-scibert-task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use malteos/aspect-scibert-task with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="malteos/aspect-scibert-task")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("malteos/aspect-scibert-task") model = AutoModel.from_pretrained("malteos/aspect-scibert-task", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from malteos/aspect-scibert-task: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/malteos/aspect-scibert-task/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://malteos/aspect-scibert-task@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/malteos/aspect-scibert-task/resolve/refs%2Fpr%2F1/pytorch_model.bin
440 MB
- Xet hash:
- b9d390b9591f7ebf89cfef7fad70455dc9e2bf5f1a721f08351b562522307a22
- Size of remote file:
- 440 MB
- SHA256:
- e5eb4524b26155a992f3441b0a96c3fdc61ff67fb4f4409cb066cef3d9e57897
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