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:
- 2de7cf77507f09c64b46e6a280014e387081d531116e11eabc782f1272026583
- Size of remote file:
- 623 Bytes
- SHA256:
- a42d605b8f8605eeb4e345904db66d8edd67327001dde9beee92b5da13dd1d31
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