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:
- 388d2212d72be7f3eb86d1de03b1a3791d2bbff74cbb8a8077bd4a61cafdc9fd
- Size of remote file:
- 4.66 kB
- SHA256:
- df89df61b84ded72d02ff4835a2b8c5b60aca9d2d6b90b2f2b5c3e742846d5d1
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