Instructions to use paragon-analytics/bert_resil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paragon-analytics/bert_resil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="paragon-analytics/bert_resil")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/bert_resil") model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/bert_resil") - Notebooks
- Google Colab
- Kaggle
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license: "mit"
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Example classification:
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```python
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license: "mit"
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This model takes text (up to a few sentences) and predicts whether the text contains resilience messaging. Resilience messaging is a text message that is about being able to a) "adapt to change” and b) “bounce back after illness or hardship". The predictive model is a fine-tuned RoBERTa NLP model. To see example use cases, please visit https://huggingface.co/spaces/paragon-analytics/ResText.
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Example classification:
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```python
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