Instructions to use manishiitg/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use manishiitg/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="manishiitg/output")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("manishiitg/output") model = AutoModelForMaskedLM.from_pretrained("manishiitg/output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e88fa4bee892a535b514359df36db80d0ce36cb595a06866412ac5eb9f36c8e8
- Size of remote file:
- 503 MB
- SHA256:
- 54a33ae6b78ecf870e8fc3d65888ec530e3cf7822b3b967eb4df00044f223944
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