| from transformers import TFAutoModelForSequenceClassification, AutoTokenizer | |
| model_name = "textattack/bert-base-uncased-rotten-tomatoes" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = TFAutoModelForSequenceClassification.from_pretrained(model_name) | |
| text = "This is a positive review." | |
| inputs = tokenizer(text, return_tensors="tf") | |
| outputs = model(inputs) | |
| scores = tf.nn.softmax(outputs.logits, axis=1).numpy()[0] | |
| positive_score = scores[1] | |
| negative_score = scores[0] | |