Instructions to use Sifter/product-category-bert-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sifter/product-category-bert-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sifter/product-category-bert-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sifter/product-category-bert-classifier") model = AutoModelForSequenceClassification.from_pretrained("Sifter/product-category-bert-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Sifter/product-category-bert-classifier: direct link, hf CLI and curl.
- Browser
- Download file 314 Bytes
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https://huggingface.co/Sifter/product-category-bert-classifier/resolve/main/README.md
- Command line
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hf download hf://Sifter/product-category-bert-classifier/README.md
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curl -L -o README.md https://huggingface.co/Sifter/product-category-bert-classifier/resolve/main/README.md
314 Bytes
metadata
library_name: transformers
tags:
- autotrain
- text-classification
base_model: google-bert/bert-base-uncased
widget:
- text: I love AutoTrain
datasets:
- Sifter/product-category
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
No validation metrics available