| from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering | |
| import tensorflow as tf | |
| checkpoint = "distilbert-base-cased-distilled-squad" | |
| tokenizer = AutoTokenizer.from_pretrained(checkpoint) | |
| model = TFAutoModelForQuestionAnswering.from_pretrained(checkpoint) | |
| def question_answering_tf(question, context): | |
| inputs = tokenizer(question, context, return_tensors="tf") | |
| #print(inputs["input_ids"]) | |
| #print(tokenizer.decode(inputs["input_ids"][0])) | |
| outputs = model(inputs) | |
| #print(outputs.start_logits) | |
| #print(outputs.end_logits) | |
| start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0]) | |
| end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0]) | |
| print(start_index, end_index) | |
| return tokenizer.decode(inputs["input_ids"][0][start_index: end_index+1]) |