Instructions to use DunnBC22/squeezebert-uncased-News_About_Gold with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/squeezebert-uncased-News_About_Gold with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DunnBC22/squeezebert-uncased-News_About_Gold")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DunnBC22/squeezebert-uncased-News_About_Gold") model = AutoModelForSequenceClassification.from_pretrained("DunnBC22/squeezebert-uncased-News_About_Gold") - Notebooks
- Google Colab
- Kaggle
squeezebert-uncased-News_About_Gold / runs /Jun06_15-23-02_Brians-Mac-mini /events.out.tfevents.1686082988.Brians-Mac-mini.65964.0
- Xet hash:
- 8aec81f2e8ad8b7353e2e437e3b9f838e9f772fabe151be520bd441e75c32786
- Size of remote file:
- 9.7 kB
- SHA256:
- bfa5738bded5aabb221dac1142a9e420c5921c83d5773daad45c6ec703cea01c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.