Text Classification
Transformers
PyTorch
ONNX
English
bert
text-generation
text-embeddings-inference
Instructions to use MattStammers/Covid19_Text_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MattStammers/Covid19_Text_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MattStammers/Covid19_Text_Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MattStammers/Covid19_Text_Model") model = AutoModelForSequenceClassification.from_pretrained("MattStammers/Covid19_Text_Model") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 241d45c
Update README.md
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README.md
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@@ -13,6 +13,7 @@ repo: https://huggingface.co/MattStammers/Covid19_Text_Model?text=Comprehensive+
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paper: N/A
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widget:
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- text: "Comprehensive overview of COVID-19. Comprehensive overview of Flu"
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output:
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- label: "Covid-19-article"
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score: 0.6
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paper: N/A
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widget:
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- text: "Comprehensive overview of COVID-19. Comprehensive overview of Flu"
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example_title: "Covid 19 Article Status. Label_0 = Covid-19 probability"
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output:
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- label: "Covid-19-article"
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score: 0.6
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