Instructions to use liamvbetts/SNHv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use liamvbetts/SNHv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="liamvbetts/SNHv2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("liamvbetts/SNHv2") model = AutoModelForSequenceClassification.from_pretrained("liamvbetts/SNHv2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51f36dee94de05469407843b4f43af0d8b7fd151511e44bfd253e675707c0b4d
- Size of remote file:
- 268 MB
- SHA256:
- fececdbee6ef311a8b7410301ddd8c74436eeb90e846969986afd09cbbaed4b1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.