Instructions to use fvancesco/tmp_date with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fvancesco/tmp_date with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="fvancesco/tmp_date")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fvancesco/tmp_date") model = AutoModelForMaskedLM.from_pretrained("fvancesco/tmp_date") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1eda0945a5e7cc6f61cdf6a6bcea14fdee20563ff180ace80378795852ecf0ce
- Size of remote file:
- 499 MB
- SHA256:
- d824ed1ffb6c3fd3295fd15cd38f20473b00715dd9e2702d965c39c6b8c7f2a0
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