Instructions to use sms1097/relevant_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sms1097/relevant_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sms1097/relevant_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sms1097/relevant_model") model = AutoModelForSequenceClassification.from_pretrained("sms1097/relevant_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e21ad6655792b5e7a300cc4b89687938e2ca926ca1d712c71df21b61532da48d
- Size of remote file:
- 536 MB
- SHA256:
- 20baf01ba69abeea1f01c8051fa33679dcecd7ad4fab5eb050ff77fb88bb7a63
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.