为更详细的描述生成嵌入向量
最后一个预测标签似乎与评论不太匹配；很可能是因为只对类别标签做嵌入，信息量不足。这一次，将改为对每个类别的描述进行嵌入，让模型更好地"理解"您在对餐厅评论做分类。
您可以使用以下对象：
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!"]
本练习是课程的一部分
使用 OpenAI API 的 Embeddings 入门
练习说明
- 提取一个包含情感描述的列表，并为其生成嵌入向量。
交互式实操练习
通过完成这段示例代码来试试这个练习。
# 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}')