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:
- 622cedd223a873ab1735b424c4ba827ca0af366ed97a75a223aa6ab72e516e56
- Size of remote file:
- 536 MB
- SHA256:
- 3fc0a57299e7ce0b0d6ed599fbfc6a11a8073670fdb5fc35c14c6f47f598c233
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