Instructions to use NLPGenius/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NLPGenius/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NLPGenius/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NLPGenius/results") model = AutoModelForSequenceClassification.from_pretrained("NLPGenius/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 832565317316d03d2627b5fd6935fffb98b2ea3ad77dfb2b365da31c9cc1ea40
- Size of remote file:
- 5.24 kB
- SHA256:
- ffc3aa25e849d3d843915393b208bc164ade333a6ca478627d177b03be855fb6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.