Instructions to use titangmz/PNC_test_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use titangmz/PNC_test_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="titangmz/PNC_test_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("titangmz/PNC_test_v2") model = AutoModelForSequenceClassification.from_pretrained("titangmz/PNC_test_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 254dfd4b5a151754efe744f757e048d2faad62adbda064cabaec4c8923483aeb
- Size of remote file:
- 5.3 kB
- SHA256:
- c759482d54ed6269388a69fb08c1bcfdb9e1253aa618c03acdb04f396b20daa0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.