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license: "mit" |
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--- |
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This is a fine-tuned RoBERTa model that takes text (up to a few sentences) and predicts to what extent it contains empathic language. |
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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 RobertaTokenizer, RobertaModel |
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from transformers import AutoModelForSequenceClassification |
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from transformers import TFAutoModelForSequenceClassification |
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from transformers import AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/bert_empathy") |
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model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/bert_empathy") |
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def roberta(x): |
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encoded_input = tokenizer(x, 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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return scores.numpy()[1] |
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``` |
