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:
- e8e1fb1b3e1761c295e47a76cd2103c47de9edeffc8e5f5010b5447e2da9dc8a
- Size of remote file:
- 539 MB
- SHA256:
- c370bdd19ec1f3f2a466e9077591a6c49fb0b7b89dc5bb495ce4ef2f5a21c587
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