Instructions to use SamuelYang/SentMAE_BEIR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SamuelYang/SentMAE_BEIR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SamuelYang/SentMAE_BEIR")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SamuelYang/SentMAE_BEIR") model = AutoModelForMaskedLM.from_pretrained("SamuelYang/SentMAE_BEIR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 994419ce205234d12a9704ffeba8ebb54ca2641d9da4b6de121192514f13bb93
- Size of remote file:
- 438 MB
- SHA256:
- 6b7d77f9fcadadf6b9a787b0eac3025a63ea44430c4b49f0096f7c1b65fa25b2
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