bert_empathy / README.md
paragon-analytics's picture
Create README.md
763e962
|
raw
history blame
725 Bytes
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]