Text Classification
Transformers
PyTorch
Safetensors
distilbert
anthropology
text-embeddings-inference
Instructions to use Chantland/Hraf_MultiLabel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chantland/Hraf_MultiLabel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Chantland/Hraf_MultiLabel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Chantland/Hraf_MultiLabel") model = AutoModelForSequenceClassification.from_pretrained("Chantland/Hraf_MultiLabel") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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---
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tags:
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- anthropology
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- text-classification
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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 score of 140 passages not used for training is .94.
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<br><br><br>
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For a quick demo, try typing in a sentence or even a paragraph in the <b>Hosted inference API</b> then pressing "compute"!
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