Instructions to use mehedi67/basic_transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mehedi67/basic_transformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mehedi67/basic_transformer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mehedi67/basic_transformer") model = AutoModelForSequenceClassification.from_pretrained("mehedi67/basic_transformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 522f89cfb4486e796901a6e63602d030a44e1ef7696eaf870984e7d636388985
- Size of remote file:
- 442 MB
- SHA256:
- 5f18e7459fb00485d824e0497333408039e226edd2268fb77fa449b8a5275277
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