Instructions to use HammadHARahim/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HammadHARahim/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HammadHARahim/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HammadHARahim/results") model = AutoModelForSequenceClassification.from_pretrained("HammadHARahim/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae767b1cfd525e61ba200d962463c0e25defdd52694793dbc19776e9fdad7008
- Size of remote file:
- 5.71 kB
- SHA256:
- c25f2df7e0894d94d24ce2c437f9419de79004b06f84ba322f51cc115be23f8e
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