pyPorch

app.py

@@ -1,16 +1,15 @@
-from transformers import pipeline
-question_answerer = pipeline("question-answering", model='distilbert-base-cased-distilled-squad')
+from transformers import DistilBertTokenizer, DistilBertModel
+import torch
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-cased-distilled-squad')
+model = DistilBertModel.from_pretrained('distilbert-base-cased-distilled-squad')
 
-context = r"""
-Extractive Question Answering is the task of extracting an answer from a text given a question. An example     of a
-question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
-a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.
-"""
+question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
 
-result = question_answerer(question="What is a good example of a question answering dataset?",     context=context)
-print(
-f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}"
-)
+inputs = tokenizer(question, text, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+
+print(outputs)
 
 import gradio as gr