Instructions to use subhasisj/de-TAPT-MLM-MiniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subhasisj/de-TAPT-MLM-MiniLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subhasisj/de-TAPT-MLM-MiniLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subhasisj/de-TAPT-MLM-MiniLM") model = AutoModelForMaskedLM.from_pretrained("subhasisj/de-TAPT-MLM-MiniLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e91332634dbc4b422d45e3d8434ced69cc44d70ade20733cca6a0c4ba6e931fe
- Size of remote file:
- 472 MB
- SHA256:
- 049da4b465f18c977784f874b8489daa38f7785ccc81d16b6a89312b2a6e69b9
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