Instructions to use Kk2k/Distilbert_domain_adapted_ecomm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kk2k/Distilbert_domain_adapted_ecomm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Kk2k/Distilbert_domain_adapted_ecomm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Kk2k/Distilbert_domain_adapted_ecomm") model = AutoModelForMaskedLM.from_pretrained("Kk2k/Distilbert_domain_adapted_ecomm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7da8d7a5bd940c81c5b32efb7bcfa52c5688f9edfb7b728d8dbb0f3d1c4a6e22
- Size of remote file:
- 269 MB
- SHA256:
- fc0ed380c53e4cb6d8bae75d26ccda908d961e8e40bce7e345391ae025cfe8d0
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