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:
- 451c7ec8e27657fb5df6fe7b46780229ac84eb4e5453da8b00b630a1d5a964a9
- Size of remote file:
- 536 MB
- SHA256:
- 29b4aefd5fd041329bebbe60f71e264638f13bf6e8a4eeaafda2a428d5bcc120
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.