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Chunking large documents via ingest pipelines p...
text = passage.text + ' ' + envSplit[i++]}..."field_map": { "_ingest._value.text": "text_field" }, "model_id": "...www.elastic.co/search-labs/blog/chunking-via-ingest-pipelines -
custom.css
sk-text-image-grid-small div.text-box, div.sk-text-image-grid-small...div.sk-text-image-grid-small div.text-box, div.sk-text-image-grid-small...scikit-learn.org/stable/_static/styles/custom.css -
AI & ML search engine — Take search to the next...
Master long text with automatic chunking Handle long text passages...passages easily with semantic_text . Elastic's open inference API with...www.elastic.co/enterprise-search/machine-learning -
fetch_20newsgroups — scikit-learn 1.5.2 documen...
on a Text Dataset Semi-supervised Classification on a Text Dataset...Classification of text documents using sparse features Clustering text documents...scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html -
sg_gallery-rendered-html.css
var(--sg-text-color); background-color: var(--sg-text-color);...html[data-theme="light"] { --sg-text-color: #000; --sg-background-color:...scikit-learn.org/dev/_static/sg_gallery-rendered-html.css -
sphinx-design.min.css
sd-text-justify{text-align:justify !important}.sd-text-left{text-align:left...{text-align:left !important}.sd-text-right{text-align:right ...scikit-learn.org/stable/_static/sphinx-design.min.css -
clustering.rst.txt
math:: \text{ARI} = \frac{\text{RI} - E[\text{RI}]}{\max(\text{RI})...math:: \text{AMI} = \frac{\text{MI} - E[\text{MI}]}{\text{mean}(H(U),...scikit-learn.org/stable/_sources/modules/clustering.rst.txt -
What is vector search? Better search with ML | ...
including text and images, transforming it...the unstructured data beyond text, such as videos, images, and...www.elastic.co/what-is/vector-search -
HashingVectorizer — scikit-learn 1.5.2 document...
classification of text documents Out-of-core classification of text documents...Clustering text documents using k-means Clustering text documents...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.html -
f1_score — scikit-learn 1.5.2 documentation
\[\text{F1} = \frac{2 * \text{TP}}{2 * \text{TP} + \text{FP}...\text{FP} + \text{FN}}\] Where \(\text{TP}\) is the number of true positives,...scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html