Instructions to use brugmark/all-MiniLM-L6-v2-personal-project-default-2024-02-16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brugmark/all-MiniLM-L6-v2-personal-project-default-2024-02-16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="brugmark/all-MiniLM-L6-v2-personal-project-default-2024-02-16")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("brugmark/all-MiniLM-L6-v2-personal-project-default-2024-02-16") model = AutoModelForMaskedLM.from_pretrained("brugmark/all-MiniLM-L6-v2-personal-project-default-2024-02-16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d319680df755930fa572884e32e4a7d77f31f1611b0585cbfdc0acd02c7d5d06
- Size of remote file:
- 4.66 kB
- SHA256:
- 387d775f233a1a25f25263a47787f9b832318ab10a26c4cbef351d9cf7d990eb
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