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license: "mit" |
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--- |
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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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import torch |
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import tensorflow as tf |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/bert_resil") |
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model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/bert_resil") |
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encoded_input = tokenizer("We will survive this.", return_tensors='pt') |
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output = model(**encoded_input) |
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scores = output[0][0].detach().numpy() |
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scores = tf.nn.softmax(scores) |
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``` |
