| 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("cardiffnlp/twitter-roberta-base-sentiment") | |
| 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] |