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:
- 6429893614a6105d3c6845b071a9391974a7b158bd5e618634fc7805e1692b2b
- Size of remote file:
- 536 MB
- SHA256:
- b5c5129f2f391575432ac8c93fa8852c65ce25d17f894cd455e3cd2e98a3f464
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