Instructions to use kyLELEng/bert-ag-news-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kyLELEng/bert-ag-news-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kyLELEng/bert-ag-news-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kyLELEng/bert-ag-news-classifier") model = AutoModelForSequenceClassification.from_pretrained("kyLELEng/bert-ag-news-classifier") - Notebooks
- Google Colab
- Kaggle
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# bert-ag-news-classifier
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This model is a fine-tuned version of `google-bert/bert-base-uncased` on the [`fancyzhx/ag_news`](https://huggingface.co/datasets/fancyzhx/ag_news) dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2339
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- Accuracy: 0.9461
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# bert-ag-news-classifier
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This model is a fine-tuned version of [`google-bert/bert-base-uncased`](https://huggingface.co/google-bert/bert-base-uncased) on the [`fancyzhx/ag_news`](https://huggingface.co/datasets/fancyzhx/ag_news) dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2339
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- Accuracy: 0.9461
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