metadata
license: mit
This is a fine-tuned RoBERTa model that takes text (up to a few sentences) and predicts to what extent it contains empathic language.
Example classification:
import torch
import tensorflow as tf
from transformers import RobertaTokenizer, RobertaModel
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/bert_empathy")
model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/bert_empathy")
def roberta(x):
encoded_input = tokenizer(x, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = tf.nn.softmax(scores)
return scores.numpy()[1]