Instructions to use khazen2/DistilBERT_Hemingway_SAR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use khazen2/DistilBERT_Hemingway_SAR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="khazen2/DistilBERT_Hemingway_SAR")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("khazen2/DistilBERT_Hemingway_SAR") model = AutoModelForMaskedLM.from_pretrained("khazen2/DistilBERT_Hemingway_SAR") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a6c92ec61f487180d26f0f4559395255a9a91c15a835503b67bf6ae8740a59b
- Size of remote file:
- 3.39 kB
- SHA256:
- 58854af3807717bf446888f6b5f98c4e990d6200f40f61266ce1534592ede6a9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.