為更詳細的描述建立嵌入向量
最後有一個預測標籤看起來和評論不太相符；這很可能是因為只對類別標籤建立嵌入向量，資訊太少所致。這次改為對每個類別的描述建立嵌入向量，讓模型更能「理解」你正在分類的是餐廳評論。
以下物件可供你使用：
sentiments = [{'label': 'Positive',
'description': 'A positive restaurant review'},
{'label': 'Neutral',
'description':'A neutral restaurant review'},
{'label': 'Negative',
'description': 'A negative restaurant review'}]
reviews = ["The food was delicious!",
"The service was a bit slow but the food was good",
"The food was cold, really disappointing!"]
本練習屬於課程
Introduction to Embeddings with the OpenAI API
練習說明
- 先擷取一個只包含情緒描述的列表，然後為它們建立嵌入向量（embedding）。
動手互動練習
試著完成這個範例程式碼，體驗一下這個練習。
# Extract and embed the descriptions from sentiments
class_descriptions = ____
class_embeddings = ____
review_embeddings = create_embeddings(reviews)
def find_closest(query_vector, embeddings):
distances = []
for index, embedding in enumerate(embeddings):
dist = distance.cosine(query_vector, embedding)
distances.append({"distance": dist, "index": index})
return min(distances, key=lambda x: x["distance"])
for index, review in enumerate(reviews):
closest = find_closest(review_embeddings[index], class_embeddings)
label = sentiments[closest['index']]['label']
print(f'"{review}" was classified as {label}')