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docs/tr/docs/project-generation.md
... müsaitliğime ve diğer faktörlere bağlı olarak daha sonra gelebilir. 😅 🎉 ## Machine Learning modelleri, spaCy ve FastAPI GitHub: <a href="https://github.com/microsoft/cookiecutter-spacy-fastapi" class="external-link" target="_blank">https://github.com/microsoft/cookiecutter-spacy-fastapi</a> ### Machine Learning modelleri, spaCy ve FastAPI - Features * **spaCy** NER model entegrasyonu.
Created: Sun Dec 28 07:19:09 GMT 2025 - Last Modified: Mon Jul 29 23:35:07 GMT 2024 - 6K bytes - Click Count (0) -
.github/workflows/release-branch-cherrypick.yml
token: ${{ secrets.JENKINS_TOKEN }} base: ${{ github.event.inputs.release_branch }} branch: ${{ github.event.inputs.release_branch }}-${{ steps.cherrypick.outputs.SHORTSHA }} reviewers: learning-to-play body: |Created: Tue Dec 30 12:39:10 GMT 2025 - Last Modified: Mon Dec 01 09:57:00 GMT 2025 - 3.1K bytes - Click Count (0) -
docs/de/docs/tutorial/path-params.md
{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *} /// tip | Tipp Falls Sie sich fragen, was „AlexNet“, „ResNet“ und „LeNet“ ist, das sind Namen von <abbr title="Genau genommen, Deep-Learning-Modellarchitekturen">Modellen</abbr> für maschinelles Lernen. /// ### Einen *Pfad-Parameter* deklarieren { #declare-a-path-parameter }Created: Sun Dec 28 07:19:09 GMT 2025 - Last Modified: Wed Dec 17 20:41:43 GMT 2025 - 10.5K bytes - Click Count (0) -
src/main/java/org/codelibs/fess/score/LtrQueryRescorer.java
import org.codelibs.fess.util.ComponentUtil; import org.opensearch.search.rescore.QueryRescorerBuilder; import org.opensearch.search.rescore.RescorerBuilder; /** * Learning to Rank query rescorer implementation. */ public class LtrQueryRescorer implements QueryRescorer { /** * Default constructor. */ public LtrQueryRescorer() { // Default constructorCreated: Sat Dec 20 09:19:18 GMT 2025 - Last Modified: Thu Jul 17 08:28:31 GMT 2025 - 1.7K bytes - Click Count (0) -
tests/test_tutorial/test_path_params/test_tutorial005.py
client = TestClient(app) def test_get_enums_alexnet(): response = client.get("/models/alexnet") assert response.status_code == 200 assert response.json() == {"model_name": "alexnet", "message": "Deep Learning FTW!"} def test_get_enums_lenet(): response = client.get("/models/lenet") assert response.status_code == 200 assert response.json() == {"model_name": "lenet", "message": "LeCNN all the images"}
Created: Sun Dec 28 07:19:09 GMT 2025 - Last Modified: Sat Dec 27 18:19:10 GMT 2025 - 4.1K bytes - Click Count (0) -
docs/fr/docs/history-design-future.md
Voici un petit bout de cette histoire. ## Alternatives Je crée des API avec des exigences complexes depuis plusieurs années (Machine Learning, systèmes distribués, jobs asynchrones, bases de données NoSQL, etc), en dirigeant plusieurs équipes de développeurs. Dans ce cadre, j'ai dû étudier, tester et utiliser de nombreuses alternatives.
Created: Sun Dec 28 07:19:09 GMT 2025 - Last Modified: Sat Oct 11 17:48:49 GMT 2025 - 4.9K bytes - Click Count (0) -
docs/pt/docs/_llm-test.md
Algum texto /// /// check | Verifique Algum texto /// /// tip | Dica Algum texto /// /// warning | Atenção Algum texto /// /// danger | Cuidado Algum texto /// //// //// tab | Informações Abas e blocos `Info`/`Note`/`Warning`/etc. devem ter a tradução do seu título adicionada após uma barra vertical (`|`).
Created: Sun Dec 28 07:19:09 GMT 2025 - Last Modified: Wed Dec 17 10:17:03 GMT 2025 - 12.4K bytes - Click Count (0) -
docs/en/data/topic_repos.yml
html_url: https://github.com/zhanymkanov/fastapi-best-practices stars: 14644 owner_login: zhanymkanov owner_html_url: https://github.com/zhanymkanov - name: machine-learning-zoomcamp html_url: https://github.com/DataTalksClub/machine-learning-zoomcamp stars: 12320 owner_login: DataTalksClub owner_html_url: https://github.com/DataTalksClub - name: fastapi_mcp html_url: https://github.com/tadata-org/fastapi_mcp
Created: Sun Dec 28 07:19:09 GMT 2025 - Last Modified: Mon Dec 01 13:17:29 GMT 2025 - 16K bytes - Click Count (0) -
src/main/java/org/codelibs/fess/opensearch/query/StoredLtrQueryBuilder.java
import org.opensearch.index.query.AbstractQueryBuilder; import org.opensearch.index.query.QueryBuilder; import org.opensearch.index.query.QueryShardContext; /** * A query builder for a stored LTR (Learning to Rank) query. * This builder constructs a query that uses a pre-trained LTR model * to re-rank search results based on a given set of features. */
Created: Sat Dec 20 09:19:18 GMT 2025 - Last Modified: Thu Jul 17 08:28:31 GMT 2025 - 7.6K bytes - Click Count (0) -
docs/pt/docs/tutorial/path-params.md
{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *} /// tip | Dica Se você está se perguntando, "AlexNet", "ResNet" e "LeNet" são apenas nomes de <abbr title="Tecnicamente, arquiteturas de modelos de Deep Learning">modelos</abbr> de Aprendizado de Máquina. /// ### Declare um parâmetro de path { #declare-a-path-parameter }Created: Sun Dec 28 07:19:09 GMT 2025 - Last Modified: Wed Dec 17 20:41:43 GMT 2025 - 9.8K bytes - Click Count (0)