Instructions to use smeylan/childes-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smeylan/childes-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="smeylan/childes-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("smeylan/childes-bert") model = AutoModelForMaskedLM.from_pretrained("smeylan/childes-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01c0161b510d70a191e87a1abccfc7f47ea80e87a1e5f56a0f94ee93e1d4c88a
- Size of remote file:
- 438 MB
- SHA256:
- 246a786ed25d99de4a34223410c9ec6b85b1c83a9ea39023793d3551fd604788
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