Instructions to use EmmaL1/CustomModel_amazon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EmmaL1/CustomModel_amazon with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EmmaL1/CustomModel_amazon")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EmmaL1/CustomModel_amazon") model = AutoModelForSequenceClassification.from_pretrained("EmmaL1/CustomModel_amazon") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b7300039291b803ce9d30f158b806a449f5d18187fdacb8c3120289c0e632f7
- Size of remote file:
- 669 MB
- SHA256:
- dc306ab2dddb0f27a720a44f2efe226f973d832424f9a81f77f6fc85effafd1b
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