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Evaluating RAG Metrics Across Different Retrieval Methods

Towards AI

LangChain Docs This text splitter takes a list of characters. from langchain.text_splitter import RecursiveCharacterTextSplitterfrom langchain_community.vectorstores import Chromafrom langchain_community.embeddings import HuggingFaceBgeEmbeddingsmodel_name = "BAAI/bge-large-en-v1.5"encode_kwargs Now, to give it a test!

AI 117
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PII Redaction and Entity Detection In 13 New Languages ??????

AssemblyAI

Travailler chez ### ############# me permet de contribuer positivement à la vie des gens en leur offrant tranquillité d'esprit et sécurité financière. Mon rôle consiste à aider nos clients à trouver les meilleures solutions d'assurance pour répondre à leurs besoins spécifiques.

Python 59
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Implementing a custom trainable component for relation extraction

Explosion

def instance_forward(model: Model[List[Doc], Floats2d], docs: List[Doc], is_train: bool) -> Tuple[Floats2d, Callable]: pooling = model.get_ref("pooling") tok2vec = model.get_ref("tok2vec") get_instances = model.attrs["get_instances"] all_instances = [get_instances(doc) for doc in docs] tokvecs, bp_tokvecs = tok2vec(docs, is_train) #.

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Token Masking Strategies for LLMs

Towards AI

import stanzastanza.download('en')# Text used in our examplestext = "Huntington's disease is a neurodegenerative autosomal disease results due to expansion of polymorphic CAG repeats in the huntingtin gene. We will start with a document in the code examples to see how the different strategies work.

AI 57
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Introducing spaCy v2.2

Explosion

You can now write commands like the following, just as you would when training the parser, entity recognizer or tagger: python -m spacy train en /output /train /dev --pipeline textcat --textcat-arch simple_cnn --textcat-multilabel You can read more about the data format required in the API docs.

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How to Train a Custom LLM Embedding Model

DagsHub

The model is trained on top of BAAI/bge-base-en-v1.5. The BAAI general embedding series includes the bge-base-en-v1.5 Additionally, the GIST Large Embedding v0 model is fine-tuned on top of the BAAI/bge-large-en-v1.5 For this example we will be using avsolatorio/GIST-large-Embedding-v0 from Aivin Solatorio.

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spaCy v3's project and config systems are pretty great

Explosion

With v3, I only have to think about my dataset’s Doc representation, nothing more. As a guide, I usually refer to spaCy’s top-level API docs , especially the batch_by_words section. However, if you are intent on making the jump, be sure to check out the migration guide from the spaCy docs.