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Build financial search applications using the Amazon Bedrock Cohere multilingual embedding model

AWS Machine Learning Blog

Solution overview Financial analysts need to digest a lot of content, such as financial publications and news media, in order to stay informed. Establish Cohere client co = cohere_aws.Client(mode=cohere_aws.Mode.BEDROCK) model_id = "cohere.embed-multilingual-v3" # Embed documents docs = top_80_df['text'].to_list()

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Use Amazon SageMaker Studio to build a RAG question answering solution with Llama 2, LangChain, and Pinecone for fast experimentation

Flipboard

We use two AWS Media & Entertainment Blog posts as the sample external data, which we convert into embeddings with the BAAI/bge-small-en-v1.5 Solution overview The following diagram illustrates the solution architecture. Deploy the BAAI/bge-small-en-v1.5 em_model_name = "BAAI/bge-small-en" em_model_path = f"./em-model"

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Emotion Classification with SpaCy v3 & Comet

Heartbeat

Emotion Recognition Dataset Overview The dataset to be used in the application described in the blog post was created for an emotion classification task. Now let’s train a multi-label text classifier using SpaCy-v3 on the Huggingface — dair-ai/emotion dataset and track the model trainings and record the results with Comet ML!

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Implementing a custom trainable component for relation extraction

Explosion

Overview We start by creating a new spaCy pipeline component that predicts relationships between genes and proteins. Let’s translate this example into a schematic overview of the neural network. def instance_forward(model: Model[List[Doc], Floats2d], docs: List[Doc], is_train: bool) -> Tuple[Floats2d, Callable]: #.

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Flag harmful language in spoken conversations with Amazon Transcribe Toxicity Detection

AWS Machine Learning Blog

Amazon Transcribe supports the following audio formats: MP3, MP4, WAV, FLAC, AMR, OGG, or WebM LanguageCode – Set to en-US. MediaFileUri – Enter the URI location of the audio file on Amazon S3. As of this writing, Toxicity Detection only supports US English language. We also described how you can parse the toxicity detection JSON output.

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Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning Blog

Solution overview In this post, we demonstrate the use of Mixtral-8x7B Instruct text generation combined with the BGE Large En embedding model to efficiently construct a RAG QnA system on an Amazon SageMaker notebook using the parent document retriever tool and contextual compression technique. We use an ml.t3.medium

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Introducing custom pipelines and extensions for spaCy v2.0

Explosion

One of the best improvements is a new system for adding pipeline components and registering extensions to the Doc , Span and Token objects. For an overview of the new models, see the models directory. This has been especially true of the core Doc , Token and Span objects. As the release candidate for spaCy v2.0 In spaCy v2.0