Instructions to use malteos/aspect-scibert-dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use malteos/aspect-scibert-dataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="malteos/aspect-scibert-dataset")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("malteos/aspect-scibert-dataset") model = AutoModel.from_pretrained("malteos/aspect-scibert-dataset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from malteos/aspect-scibert-dataset: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/malteos/aspect-scibert-dataset/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://malteos/aspect-scibert-dataset/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/malteos/aspect-scibert-dataset/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- b0533556d0806854ec1863be5d860025f5ce3de2f0f6125efc3e5cd71aec4d17
- Size of remote file:
- 440 MB
- SHA256:
- e57902e53192ebf962f234edfcae00e0063553ad1dc6a535ad296bc05a121965
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