Instructions to use Chantland/HRAF_Multilabel_SubClasses with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chantland/HRAF_Multilabel_SubClasses with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Chantland/HRAF_Multilabel_SubClasses")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Chantland/HRAF_Multilabel_SubClasses") model = AutoModelForSequenceClassification.from_pretrained("Chantland/HRAF_Multilabel_SubClasses") - Notebooks
- Google Colab
- Kaggle
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- text-classification
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Multi-Label Text classification model used to decode if passages contain a misfortunate event, a cause for misfortune, and/or an action to mollify or prevent some misfortune. The current F1 micro score of 140 passages not used for training is .678. individual class f1 scores shown below.
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- anthropology
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- text-classification
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widget:
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- text: "The baire say -- and the Indians believe -- that illnesses are due to bodies placed by the maeréboe on the part of the ailing organism. Many times they say that it is an insect, a stone, a tooth, a claw, /Illustration appears here/ 102 etc."
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example_title: "Bororo"
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---
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Multi-Label Text classification model used to decode if passages contain a misfortunate event, a cause for misfortune, and/or an action to mollify or prevent some misfortune. The current F1 micro score of 140 passages not used for training is .678. individual class f1 scores shown below.
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