Instructions to use DunnBC22/medium-base-News_About_Gold with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/medium-base-News_About_Gold with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/medium-base-News_About_Gold")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/medium-base-News_About_Gold") model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/medium-base-News_About_Gold", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 359b246dfafca086dfe8d5c8f0932a702a02d2d3728926f4754a02d0e65a9b3a
- Size of remote file:
- 465 MB
- SHA256:
- 6dbcce696bd7f1de8cee290e6f652be920910138bf93f3bd82f4b3500d7db35c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.