Instructions to use fabikru/trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fabikru/trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="fabikru/trainer")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fabikru/trainer") model = AutoModelForMaskedLM.from_pretrained("fabikru/trainer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f5e7d4bc052969657d972148f9cd392e9ab8c097056203a9dfd2b945a6dea141
- Size of remote file:
- 5.91 kB
- SHA256:
- 385f6814dec1b9e30ba5a183b4d506f225719ec3a0b4980f9196c2af493b9812
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