Instructions to use subhasisj/zh-TAPT-MLM-MiniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subhasisj/zh-TAPT-MLM-MiniLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subhasisj/zh-TAPT-MLM-MiniLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subhasisj/zh-TAPT-MLM-MiniLM") model = AutoModelForMaskedLM.from_pretrained("subhasisj/zh-TAPT-MLM-MiniLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c817fd51edadfac59db79178c12fb84bd39546a74e6669baa7aaa394a8353be
- Size of remote file:
- 17.1 MB
- SHA256:
- 8666fdab5ac4930b05db388cb079d1e8848f7db3e786d5f53994e3666d18a0b4
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