Instructions to use sms1097/utility_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sms1097/utility_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sms1097/utility_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sms1097/utility_model") model = AutoModelForSequenceClassification.from_pretrained("sms1097/utility_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5cfc3ee31651f59b9ec07ae246553a4113efd0e6e60e70b1d9a3e3e26c515031
- Size of remote file:
- 268 MB
- SHA256:
- 36206af61423b638ef0e2055880b2f221974319405ebb1ebf98bcfb1b25fb13a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.