Instructions to use HyoungGeun/Capstone_KeywordSearch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HyoungGeun/Capstone_KeywordSearch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="HyoungGeun/Capstone_KeywordSearch")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("HyoungGeun/Capstone_KeywordSearch") model = AutoModelForTokenClassification.from_pretrained("HyoungGeun/Capstone_KeywordSearch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed1c0bfe3ed9f19cd9a9748e1637e195ed3dfbee559ad0392125ee27d8370099
- Size of remote file:
- 436 MB
- SHA256:
- f045d90ac4f984f911ae76526e9f5c02b6b4e055344b8edfa3e30f522eb60a0e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.