Instructions to use hamedhf/first_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hamedhf/first_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hamedhf/first_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hamedhf/first_model") model = AutoModelForSequenceClassification.from_pretrained("hamedhf/first_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed4ed98a8c6eb3b495512ae8e404b7a15197951d375ac6c5c3178ab226c7e282
- Size of remote file:
- 433 MB
- SHA256:
- 9de0c114d8685e461442cabfcc729302d20e5164e1d211574eb222885768c92b
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