Instructions to use mjavadmt/simple_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjavadmt/simple_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mjavadmt/simple_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mjavadmt/simple_classification") model = AutoModelForSequenceClassification.from_pretrained("mjavadmt/simple_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c17fea63c64b32adf58918e86ce55f1a57027f8f99b7a6c14e9193012c6b2162
- Size of remote file:
- 433 MB
- SHA256:
- 7450cf72cc23e1a983adde833bf14cdf5f7c85d68e90834b7b934bd246976340
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