文件中的列应为： "dataset_name": { "file_name": "dataset_name.json", "columns": { "prompt": "instruction", "query": "input", "response": "output", "system": "system", "history": "history" } } • 对于 sharegpt 格式的数据集，dataset_info files=[os.path.abspath('doc.pdf')]) messages = [] while True: query = input('user question: ') messages.append({'role': 'user', 'content': query}) response = [] for response in bot.run(messages=messages): 检索增强（RAG） 现在您可以输入查询，Qwen1.5 将基于索引文档的内容提供答案。 query_engine = index.as_query_engine() your_query = "query here>" print(query_engine.query(your_query).response) 1.16 Langchain This guide helps you

各种机器学习问题 25 办比赛14来完成这项工作。 搜索 有时，我们不仅仅希望输出一个类别或一个实值。在信息检索领域，我们希望对一组项目进行排序。以网络 搜索为例，目标不是简单的“查询（query）‐网页（page）”分类，而是在海量搜索结果中找到用户最需要的 那部分。搜索结果的排序也十分重要，学习算法需要输出有序的元素子集。换句话说，如果要求我们输出字 母表中的前5个字母，返回“A、 以简单地使用 参数化的全连接层，甚至是非参数化的最大汇聚层或平均汇聚层。 因此，“是否包含自主性提示”将注意力机制与全连接层或汇聚层区别开来。在注意力机制的背景下，自主性 提示被称为查询（query）。给定任何查询，注意力机制通过注意力汇聚（attention pooling）将选择引导至 感官输入（sensory inputs，例如中间特征表示）。在注意力机制中，这些感官输入被称为值（value）。更通 key_size, query_size, num_hiddens, dropout, **kwargs): super(AdditiveAttention, self).__init__(**kwargs) self.W_k = nn.Linear(key_size, num_hiddens, bias=False) self.W_q = nn.Linear(query_size, num_hiddens

������������ Ø �� ���������������� ���������� ������� Query = �� � �� �� �� � 1 � � Doc = � �� �� � �� ��� �� �� � � � �� ?_���� ������� Query = A B C D E Doc = X !" B Y C #` Z �����������BM25/CTR/CQR �����������BM25/CTR/CQR http://singhal.info/ieee2001.pdf Query = A B C D E Doc = X !" B Y C #` Z �����������BM25/CTR/CQR http://singhal.info/ieee2001.pdf Query = A B C D E Doc = X !" B Y C #` Z ��������� �����������BM25/CTR/CQR http://singhal.info/ieee2001.pdf Query = A B C D E Doc = X !" B Y C #` Z ������ �����������BM25/CTR/CQR http://singhal.info/ieee2001.pdf Query = A B C D E Doc = X !" B Y C #` Z

�����batch_size*6, 128) Soft attention Attention size = 100 �batch_size*6, 128) Cosine similar Query semantic Title.1 semantic Title.2 semantic Title.3 semantic Title.4 semantic Title.5 semantic Cosine.5 softmax �batch_size, 5, 1] loss Softmax and loss 语k模型——总结 ������ • ���query������������, ���query ��������� • ground truth���NDCG��1% ������ • ��FastText Vector���embedding • ���+���

teacher. “Near miss” examples Learner can query an oracle about class of an unlabeled example in the environment Learner can construct an arbitrary example and query an oracle for its label Learner can design Basic idea: Traditional supervised learning algorithms passively accept training data. Instead, query for annotations on informative images from the unlabeled data. Theoretical results show that large

(Luong) mechanism learns three weight matrices namely WQ (query weight), WK (key weight) and WV (value weight) which are used to compute the query, key and value matrices for input sequences. Then, a softmax softmax is applied to the scaled dot product of query and key matrices to obtain a score matrix (figure 4-16). Finally, the values are weighted based on the positional relationship encoded in the score matrix dimensions of each element. The weight matrices WQ, WK, and WV are identically shaped as (d, dk). The query, key and the value matrices are computed as follows: The resulting Q, K, and V matrices are shaped

install a Conda package manager, and add the conda path to your PYTHONPATH for example, using export PYTHONPATH="/opt/conda/lib/python3.8/site-packages" if your Conda package manager was installed in /opt/conda install a Conda package manager, and add the conda path to your PYTHONPATH for example, using export PYTHONPATH="/opt/conda/lib/python3.8/site-packages" if your Conda package manager was installed in /opt/conda install a Conda package manager, and add the conda path to your PYTHONPATH for example, using export PYTHONPATH="/opt/conda/lib/python3.8/site-packages" if your Conda package manager was installed in /opt/conda

puts everything together and runs the search for 150 episodes. controller = Controller() child_manager = ChildManager() start_state = np.array([random.randrange(len(STATE_SPACE[0]))]) for episode in reshape(TIMESTEP_ADDRESS_SPACE) # Evaluate the child generated by the controller reward, accuracy = child_manager.get_rewards(config) print( 'Episode: {} Reward: {} Accuracy: {}'.format( episode, reward, accuracy

• 解决方案  同义词 ？  归一化 ？ 預報 =》预报， 五岁 =》 5岁 目前商品搜索中的一些问题 7 人工智能／深度学习在搜索中的应用：网页／电商搜索 • 基于深度学习的（Query, Document）分数是Google搜索引擎中第3重要的排序信 号 • 亚马逊（Amazon／A9)电子商务搜索引擎中， 深度学习还在实验阶段， 尚未进入生产线。 8 • 搜索数值矢量化

们的大脑会把注意力放在主要的 信息上，这就是大脑的注意力机 制。 8 1.Transformer介绍 每个词的Attention计算 每个词的Q会跟整个序列中每一个K计算得分，然后基于得分再分配特征 Q: query，要去查询的 K: key，等着被查的 V: value，实际的特征信息 9 1.Transformer介绍 Attention的优点 1.参数少：相比于 CNN、RNN ，其复杂度更小，参数也更少。所以对算力的要求