Instructions to use bondarchukb/bert-ads-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bondarchukb/bert-ads-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bondarchukb/bert-ads-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bondarchukb/bert-ads-classification") model = AutoModelForSequenceClassification.from_pretrained("bondarchukb/bert-ads-classification") - Notebooks
- Google Colab
- Kaggle
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README.md
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# bert-base-cased for Advertisement Classification
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This is
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<b>Labels</b>:
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0 -> non-advertisement;
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# bert-base-cased for Advertisement Classification
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This is bert-base-cased model trained on the binary dataset prepared for advertisement classification. This model is suitable for English.
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<b>Labels</b>:
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0 -> non-advertisement;
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