Instructions to use cajcodes/DistilBERT-PoliticalBias with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cajcodes/DistilBERT-PoliticalBias with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cajcodes/DistilBERT-PoliticalBias")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cajcodes/DistilBERT-PoliticalBias") model = AutoModelForSequenceClassification.from_pretrained("cajcodes/DistilBERT-PoliticalBias") - Notebooks
- Google Colab
- Kaggle
Fix the author name field in the BibTex citation
#2
by matous-volf - opened
README.md
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@@ -60,7 +60,7 @@ license: mit
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If you use this model or dataset, please cite as follows:
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```
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@misc{cajcodes_distilbert_political_bias,
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author = Christopher Jones,
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title = {DistilBERT-PoliticalBias: A Novel Approach to Detecting and Reducing Political Bias in Text},
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year = {2024},
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howpublished = {\url{https://huggingface.co/cajcodes/DistilBERT-PoliticalBias}},
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If you use this model or dataset, please cite as follows:
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```
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@misc{cajcodes_distilbert_political_bias,
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author = {Christopher Jones},
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title = {DistilBERT-PoliticalBias: A Novel Approach to Detecting and Reducing Political Bias in Text},
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year = {2024},
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howpublished = {\url{https://huggingface.co/cajcodes/DistilBERT-PoliticalBias}},
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