--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3284 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: Snowflake/snowflake-arctic-embed-m-v1.5 widget: - source_sentence: How do I register and activate a stack with a new orchestrator using ZenML? sentences: - "hestrator registered and part of our active stack:zenml orchestrator register\ \ \\\n --flavor=airflow \\\n --local=True # set this\ \ to `False` if using a remote Airflow deployment\n\n# Register and activate a\ \ stack with the new orchestrator\nzenml stack register -o \ \ ... --set\n\nDue to dependency conflicts, we need to install the Python packages\ \ to start a local Airflow server in a separate Python environment.\n\n# Create\ \ a fresh virtual environment in which we install the Airflow server dependencies\n\ python -m venv airflow_server_environment\nsource airflow_server_environment/bin/activate\n\ \n# Install the Airflow server dependencies\npip install \"apache-airflow==2.4.0\"\ \ \"apache-airflow-providers-docker<3.8.0\" \"pydantic~=2.7.1\"\n\nBefore starting\ \ the local Airflow server, we can set a few environment variables to configure\ \ it:\n\nAIRFLOW_HOME: This variable defines the location where the Airflow server\ \ stores its database and configuration files. The default value is ~/airflow.\n\ \nAIRFLOW__CORE__DAGS_FOLDER: This variable defines the location where the Airflow\ \ server looks for DAG files. The default value is /dags.\n\nAIRFLOW__SCHEDULER__DAG_DIR_LIST_INTERVAL:\ \ This variable controls how often the Airflow scheduler checks for new or updated\ \ DAGs. By default, the scheduler will check for new DAGs every 30 seconds. This\ \ variable can be used to increase or decrease the frequency of the checks, depending\ \ on the specific needs of your pipeline.\n\nWhen running this on MacOS, you might\ \ need to set the no_proxy environment variable to prevent crashes due to a bug\ \ in Airflow (see this page for more information):\n\nexport no_proxy=*\n\nWe\ \ can now start the local Airflow server by running the following command:\n\n\ # Switch to the Python environment that has Airflow installed before running this\ \ command\nairflow standalone" - "ta stores you want to migrate, then upgrade ZenML.Decide the ZenML deployment\ \ model that you want to follow for your projects. See the ZenML deployment documentation\ \ for available deployment scenarios. If you decide on using a local or remote\ \ ZenML server to manage your pipelines, make sure that you first connect your\ \ client to it by running zenml connect.\n\nUse the zenml pipeline runs migrate\ \ CLI command to migrate your old pipeline runs:\n\nIf you want to migrate from\ \ a local SQLite metadata store, you only need to pass the path to the metadata\ \ store to the command, e.g.:\n\nzenml pipeline runs migrate PATH/TO/LOCAL/STORE/metadata.db\n\ \nIf you would like to migrate any other store, you will need to set --database_type=mysql\ \ and provide the MySQL host, username, and password in addition to the database,\ \ e.g.:\n\nzenml pipeline runs migrate DATABASE_NAME \\\n --database_type=mysql\ \ \\\n --mysql_host=URL/TO/MYSQL \\\n --mysql_username=MYSQL_USERNAME \\\n \ \ --mysql_password=MYSQL_PASSWORD\n\n\U0001F4BE The New Way (CLI Command Cheat\ \ Sheet)\n\nDeploy the server\n\nzenml deploy --aws (maybe don’t do this :) since\ \ it spins up infrastructure on AWS…)\n\nSpin up a local ZenML Server\n\nzenml\ \ up\n\nConnect to a pre-existing server\n\nzenml connect (pass in URL / etc,\ \ or zenml connect --config + yaml file)\n\nList your deployed server details\n\ \nzenml status\n\nThe ZenML Dashboard is now available\n\nThe new ZenML Dashboard\ \ is now bundled into the ZenML Python package and can be launched directly from\ \ Python. The source code lives in the ZenML Dashboard repository.\n\nTo launch\ \ it locally, simply run zenml up on your machine and follow the instructions:\n\ \n$ zenml up\nDeploying a local ZenML server with name 'local'.\nConnecting ZenML\ \ to the 'local' local ZenML server (http://127.0.0.1:8237).\nUpdated the global\ \ store configuration.\nConnected ZenML to the 'local' local ZenML server (http://127.0.0.1:8237).\n\ The local ZenML dashboard is available at 'http://127.0.0.1:8237'. You can\nconnect\ \ to it using the 'default' username and an empty password." - '🐍Configure Python environments Navigating multiple development environments. PreviousHyperAI Service ConnectorNextHandling dependencies Last updated 21 days ago' - source_sentence: How do you build a simple machine learning pipeline using ZenML decorators in the code? sentences: - 'Develop a custom data validator How to develop a custom data validator Before diving into the specifics of this component type, it is beneficial to familiarize yourself with our general guide to writing custom component flavors in ZenML. This guide provides an essential understanding of ZenML''s component flavor concepts. Base abstraction in progress! We are actively working on the base abstraction for the Data Validators, which will be available soon. As a result, their extension is not recommended at the moment. When you are selecting a data validator for your stack, you can use one of the existing flavors. If you need to implement your own Data Validator flavor, you can still do so, but keep in mind that you may have to refactor it when the base abstraction is updated. ZenML comes equipped with Data Validator implementations that integrate a variety of data logging and validation libraries, frameworks and platforms. However, if you need to use a different library or service as a backend for your ZenML Data Validator, you can extend ZenML to provide your own custom Data Validator implementation. Build your own custom data validator If you want to implement your own custom Data Validator, you can follow the following steps: Create a class which inherits from the BaseDataValidator class and override one or more of the abstract methods, depending on the capabilities of the underlying library/service that you want to integrate. If you need any configuration, you can create a class which inherits from the BaseDataValidatorConfig class. Bring both of these classes together by inheriting from the BaseDataValidatorFlavor. (Optional) You should also provide some standard steps that others can easily insert into their pipelines for instant access to data validation features. Once you are done with the implementation, you can register it through the CLI. Please ensure you point to the flavor class via dot notation:' - " This is us if you want to put faces to the names!However, in order to improve\ \ ZenML and understand how it is being used, we need to use analytics to have\ \ an overview of how it is used 'in the wild'. This not only helps us find bugs\ \ but also helps us prioritize features and commands that might be useful in future\ \ releases. If we did not have this information, all we really get is pip download\ \ statistics and chatting with people directly, which while being valuable, is\ \ not enough to seriously better the tool as a whole.\n\nHow does ZenML collect\ \ these statistics?\n\nWe use Segment as the data aggregation library for all\ \ our analytics. However, before any events get sent to Segment, they first go\ \ through a central ZenML analytics server. This added layer allows us to put\ \ various countermeasures to incidents such as getting spammed with events and\ \ enables us to have a more optimized tracking process.\n\nThe client code is\ \ entirely visible and can be seen in the analytics module of our main repository.\n\ \nIf I share my email, will you spam me?\n\nNo, we won't. Our sole purpose of\ \ contacting you will be to ask for feedback (e.g. in the shape of a user interview).\ \ These interviews help the core team understand usage better and prioritize feature\ \ requests. If you have any concerns about data privacy and the usage of personal\ \ information, please contact us, and we will try to alleviate any concerns as\ \ soon as possible.\n\nVersion mismatch (downgrading)\n\nIf you've recently downgraded\ \ your ZenML version to an earlier release or installed a newer version on a different\ \ environment on the same machine, you might encounter an error message when running\ \ ZenML that says:\n\n`The ZenML global configuration version (%s) is higher than\ \ the version of ZenML \ncurrently being used (%s).`\n\nWe generally recommend\ \ using the latest ZenML version. However, there might be cases where you need\ \ to match the global configuration version with the version of ZenML installed\ \ in the current environment. To do this, run the following command:\n\nzenml\ \ downgrade" - "⛓️Build a pipeline\n\nBuilding pipelines is as simple as adding the `@step` and\ \ `@pipeline` decorators to your code.\n\n@step # Just add this decorator\ndef\ \ load_data() -> dict:\n training_data = [[1, 2], [3, 4], [5, 6]]\n labels\ \ = [0, 1, 0]\n return {'features': training_data, 'labels': labels}\n\n@step\n\ def train_model(data: dict) -> None:\n total_features = sum(map(sum, data['features']))\n\ \ total_labels = sum(data['labels'])\n\n# Train some model here\n\nprint(f\"\ Trained model using {len(data['features'])} data points. \"\n f\"Feature\ \ sum is {total_features}, label sum is {total_labels}\")\n\n@pipeline # This\ \ function combines steps together \ndef simple_ml_pipeline():\n dataset =\ \ load_data()\n train_model(dataset)\n\nYou can now run this pipeline by simply\ \ calling the function:\n\nsimple_ml_pipeline()\n\nWhen this pipeline is executed,\ \ the run of the pipeline gets logged to the ZenML dashboard where you can now\ \ go to look at its DAG and all the associated metadata. To access the dashboard\ \ you need to have a ZenML server either running locally or remotely. See our\ \ documentation on this here.\n\nCheck below for more advanced ways to build and\ \ interact with your pipeline.\n\nConfigure pipeline/step parameters\n\nName and\ \ annotate step outputs\n\nControl caching behavior\n\nRun pipeline from a pipeline\n\ \nControl the execution order of steps\n\nCustomize the step invocation ids\n\n\ Name your pipeline runs\n\nUse failure/success hooks\n\nHyperparameter tuning\n\ \nAttach metadata to steps\n\nFetch metadata within steps\n\nFetch metadata during\ \ pipeline composition\n\nEnable or disable logs storing\n\nSpecial Metadata Types\n\ \nAccess secrets in a step\n\nPreviousBest practicesNextUse pipeline/step parameters\n\ \nLast updated 1 month ago" - source_sentence: How can I integrate Large Language Models (LLMs) into my MLOps workflows using ZenML? sentences: - '🦜LLMOps guide Leverage the power of LLMs in your MLOps workflows with ZenML. Welcome to the ZenML LLMOps Guide, where we dive into the exciting world of Large Language Models (LLMs) and how to integrate them seamlessly into your MLOps pipelines using ZenML. This guide is designed for ML practitioners and MLOps engineers looking to harness the potential of LLMs while maintaining the robustness and scalability of their workflows. In this guide, we''ll explore various aspects of working with LLMs in ZenML, including: RAG with ZenML RAG in 85 lines of code Understanding Retrieval-Augmented Generation (RAG) Data ingestion and preprocessing Embeddings generation Storing embeddings in a vector database Basic RAG inference pipeline Evaluation and metrics Evaluation in 65 lines of code Retrieval evaluation Generation evaluation Evaluation in practice Reranking for better retrieval Understanding reranking Implementing reranking in ZenML Evaluating reranking performance Improve retrieval by finetuning embeddings Synthetic data generation Finetuning embeddings with Sentence Transformers Evaluating finetuned embeddings Finetuning LLMs with ZenML To follow along with the examples and tutorials in this guide, ensure you have a Python environment set up with ZenML installed. Familiarity with the concepts covered in the Starter Guide and Production Guide is recommended. We''ll showcase a specific application over the course of this LLM guide, showing how you can work from a simple RAG pipeline to a more complex setup that involves finetuning embeddings, reranking retrieved documents, and even finetuning the LLM itself. We''ll do this all for a use case relevant to ZenML: a question answering system that can provide answers to common questions about ZenML. This will help you understand how to apply the concepts covered in this guide to your own projects.' - ' data with tags Get arbitrary artifacts in a stepHandle custom data types Load artifacts into memory Datasets in ZenML Manage big data Skipping materialization Passing artifacts between pipelines Register Existing Data as a ZenML Artifact πŸ“ŠVisualizing artifacts Default visualizations Creating custom visualizations Displaying visualizations in the dashboard Disabling visualizations πŸͺ†Use the Model Control Plane Registering a Model Deleting a Model Associate a pipeline with a Model Connecting artifacts via a Model Controlling Model versions Load a Model in code Promote a Model Linking model binaries/data to a Model Load artifacts from Model πŸ“ˆTrack metrics and metadata Attach metadata to a model Attach metadata to an artifact Attach metadata to steps Group metadata Special Metadata Types Fetch metadata within steps Fetch metadata during pipeline composition πŸ‘¨β€πŸŽ€Popular integrations Run on AWS Run on GCP Run on Azure Kubeflow Kubernetes MLflow Skypilot πŸ”ŒConnect services (AWS, GCP, Azure, K8s etc) Service Connectors guide Security best practices Docker Service Connector Kubernetes Service Connector AWS Service Connector GCP Service Connector Azure Service Connector HyperAI Service Connector 🐍Configure Python environments Handling dependencies Configure the server environment πŸ”ŒConnect to a server Connect in with your User (interactive) Connect with a Service Account πŸ”Interact with secrets 🐞Debug and solve issues 🀝Contribute to ZenML Implement a custom integration Stack Components πŸ“œOverview πŸ”‹Orchestrators Local Orchestrator Local Docker Orchestrator Kubeflow Orchestrator Kubernetes Orchestrator Google Cloud VertexAI Orchestrator AWS Sagemaker Orchestrator AzureML Orchestrator Databricks Orchestrator Tekton Orchestrator Airflow Orchestrator Skypilot VM Orchestrator HyperAI Orchestrator Lightning AI Orchestrator Develop a custom orchestrator πŸͺArtifact Stores Local Artifact Store Amazon Simple Cloud Storage (S3) Google Cloud Storage (GCS)' - 'Troubleshoot the deployed server Troubleshooting tips for your ZenML deployment In this document, we will go over some common issues that you might face when deploying ZenML and how to solve them. Viewing logs Analyzing logs is a great way to debug issues. Depending on whether you have a Kubernetes (using Helm or zenml deploy) or a Docker deployment, you can view the logs in different ways. If you are using Kubernetes, you can view the logs of the ZenML server using the following method: Check all pods that are running your ZenML deployment. kubectl -n get pods If you see that the pods aren''t running, you can use the command below to get the logs for all pods at once. kubectl -n logs -l app.kubernetes.io/name=zenml Note that the error can either be from the zenml-db-init container that connects to the MySQL database or from the zenml container that runs the server code. If the get pods command shows that the pod is failing in the Init state then use zenml-db-init as the container name, otherwise use zenml. kubectl -n logs -l app.kubernetes.io/name=zenml -c You can also use the --tail flag to limit the number of lines to show or the --follow flag to follow the logs in real-time. If you are using Docker, you can view the logs of the ZenML server using the following method: If you used the zenml up --docker CLI command to deploy the Docker ZenML server, you can check the logs with the command: zenml logs -f If you used the docker run command to manually deploy the Docker ZenML server, you can check the logs with the command: docker logs zenml -f If you used the docker compose command to manually deploy the Docker ZenML server, you can check the logs with the command: docker compose -p zenml logs -f Fixing database connection problems' - source_sentence: How can you disable artifact visualization in ZenML? sentences: - 'Secret management Configuring the secrets store. PreviousCustom secret storesNextZenML Pro Last updated 21 days ago' - ' visit our PyPi package page. Running with Dockerzenml is also available as a Docker image hosted publicly on DockerHub. Use the following command to get started in a bash environment with zenml available: docker run -it zenmldocker/zenml /bin/bash If you would like to run the ZenML server with Docker: docker run -it -d -p 8080:8080 zenmldocker/zenml-server Deploying the server Though ZenML can run entirely as a pip package on a local system, complete with the dashboard. You can do this easily: pip install "zenml[server]" zenml up # opens the dashboard locally However, advanced ZenML features are dependent on a centrally-deployed ZenML server accessible to other MLOps stack components. You can read more about it here. For the deployment of ZenML, you have the option to either self-host it or register for a free ZenML Pro account. PreviousIntroductionNextCore concepts Last updated 20 days ago' - "Disabling visualizations\n\nDisabling visualizations.\n\nIf you would like to\ \ disable artifact visualization altogether, you can set enable_artifact_visualization\ \ at either pipeline or step level:\n\n@step(enable_artifact_visualization=False)\n\ def my_step():\n ...\n\n@pipeline(enable_artifact_visualization=False)\ndef\ \ my_pipeline():\n ...\n\nPreviousDisplaying visualizations in the dashboardNextUse\ \ the Model Control Plane\n\nLast updated 21 days ago" - source_sentence: How can I programmatically manage secrets using the ZenML Client API, and what are some of the methods available for tasks like fetching, updating, and deleting secrets? sentences: - "tack:\n\nzenml stack register-secrets []The ZenML client API offers\ \ a programmatic interface to create, e.g.:\n\nfrom zenml.client import Client\n\ \nclient = Client()\nclient.create_secret(\n name=\"my_secret\",\n values={\n\ \ \"username\": \"admin\",\n \"password\": \"abc123\"\n }\n)\n\ \nOther Client methods used for secrets management include get_secret to fetch\ \ a secret by name or id, update_secret to update an existing secret, list_secrets\ \ to query the secrets store using a variety of filtering and sorting criteria,\ \ and delete_secret to delete a secret. The full Client API reference is available\ \ here.\n\nSet scope for secrets\n\nZenML secrets can be scoped to a user. This\ \ allows you to create secrets that are only accessible to one user.\n\nBy default,\ \ all created secrets are scoped to the active user. To create a secret and scope\ \ it to your active user instead, you can pass the --scope argument to the CLI\ \ command:\n\nzenml secret create \\\n --scope user \\\n --=\ \ \\\n --=\n\nScopes also act as individual namespaces. When\ \ you are referencing a secret by name in your pipelines and stacks, ZenML will\ \ look for a secret with that name scoped to the active user.\n\nAccessing registered\ \ secrets\n\nReference secrets in stack component attributes and settings\n\n\ Some of the components in your stack require you to configure them with sensitive\ \ information like passwords or tokens, so they can connect to the underlying\ \ infrastructure. Secret references allow you to configure these components in\ \ a secure way by not specifying the value directly but instead referencing a\ \ secret by providing the secret name and key. Referencing a secret for the value\ \ of any string attribute of your stack components, simply specify the attribute\ \ using the following syntax: {{.}}\n\nFor example:\n\ \n# Register a secret called `mlflow_secret` with key-value pairs for the\n# username\ \ and password to authenticate with the MLflow tracking server" - 'y to the active stack zenml stack update -c Additionally, we''ll need to log in to the container registry so Docker can pull and push images. This will require your DockerHub account name and either your password or preferably a personal access token. docker login For more information and a full list of configurable attributes of the dockerhub container registry, check out the SDK Docs . PreviousDefault Container RegistryNextAmazon Elastic Container Registry (ECR) Last updated 4 months ago' - 'nect the stack component to the Service Connector:$ zenml step-operator register --flavor kubernetes Running with active stack: ''default'' (repository) Successfully registered step operator ``. $ zenml service-connector list-resources --resource-type kubernetes-cluster -e The following ''kubernetes-cluster'' resources can be accessed by service connectors that you have configured: ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━┓ ┃ CONNECTOR ID β”‚ CONNECTOR NAME β”‚ CONNECTOR TYPE β”‚ RESOURCE TYPE β”‚ RESOURCE NAMES ┃ ┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────────────┼─────────────────────┨ ┃ e33c9fac-5daa-48b2-87bb-0187d3782cde β”‚ aws-iam-multi-eu β”‚ πŸ”Ά aws β”‚ πŸŒ€ kubernetes-cluster β”‚ kubeflowmultitenant ┃ ┃ β”‚ β”‚ β”‚ β”‚ zenbox ┃ ┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────────────┼─────────────────────┨ ┃ ed528d5a-d6cb-4fc4-bc52-c3d2d01643e5 β”‚ aws-iam-multi-us β”‚ πŸ”Ά aws β”‚ πŸŒ€ kubernetes-cluster β”‚ zenhacks-cluster ┃ ┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────────────┼─────────────────────┨ ┃ 1c54b32a-4889-4417-abbd-42d3ace3d03a β”‚ gcp-sa-multi β”‚ πŸ”΅ gcp β”‚ πŸŒ€ kubernetes-cluster β”‚ zenml-test-cluster ┃ ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━┛' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: zenml/finetuned-snowflake-arctic-embed-m-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 384 type: dim_384 metrics: - type: cosine_accuracy@1 value: 0.1917808219178082 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5095890410958904 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6986301369863014 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.810958904109589 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.1917808219178082 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.16986301369863013 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.13972602739726026 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08109589041095888 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1917808219178082 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5095890410958904 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6986301369863014 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.810958904109589 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.490826354124735 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.38868232224396587 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3947220516402755 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.19726027397260273 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5150684931506849 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6931506849315069 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8191780821917808 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.19726027397260273 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.17168949771689496 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.13863013698630136 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08191780821917807 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.19726027397260273 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5150684931506849 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6931506849315069 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8191780821917808 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4973578695114294 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3948053924766253 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.39989299235069015 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.19452054794520549 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5013698630136987 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.673972602739726 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7835616438356164 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.19452054794520549 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.16712328767123283 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1347945205479452 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07835616438356163 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.19452054794520549 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5013698630136987 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.673972602739726 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7835616438356164 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.47814279525126957 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.38079147640791483 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.38821398163789955 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.1780821917808219 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.4602739726027397 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6547945205479452 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7753424657534247 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.1780821917808219 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15342465753424658 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.13095890410958905 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07753424657534246 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1780821917808219 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.4602739726027397 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6547945205479452 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7753424657534247 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4625139710379368 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.36313002826701446 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.37046958498969434 name: Cosine Map@100 --- # zenml/finetuned-snowflake-arctic-embed-m-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [Snowflake/snowflake-arctic-embed-m-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the πŸ€— Hub model = SentenceTransformer("zenml/finetuned-snowflake-arctic-embed-m-v1.5") # Run inference sentences = [ 'How can I programmatically manage secrets using the ZenML Client API, and what are some of the methods available for tasks like fetching, updating, and deleting secrets?', 'tack:\n\nzenml stack register-secrets []The ZenML client API offers a programmatic interface to create, e.g.:\n\nfrom zenml.client import Client\n\nclient = Client()\nclient.create_secret(\n name="my_secret",\n values={\n "username": "admin",\n "password": "abc123"\n }\n)\n\nOther Client methods used for secrets management include get_secret to fetch a secret by name or id, update_secret to update an existing secret, list_secrets to query the secrets store using a variety of filtering and sorting criteria, and delete_secret to delete a secret. The full Client API reference is available here.\n\nSet scope for secrets\n\nZenML secrets can be scoped to a user. This allows you to create secrets that are only accessible to one user.\n\nBy default, all created secrets are scoped to the active user. To create a secret and scope it to your active user instead, you can pass the --scope argument to the CLI command:\n\nzenml secret create \\\n --scope user \\\n --= \\\n --=\n\nScopes also act as individual namespaces. When you are referencing a secret by name in your pipelines and stacks, ZenML will look for a secret with that name scoped to the active user.\n\nAccessing registered secrets\n\nReference secrets in stack component attributes and settings\n\nSome of the components in your stack require you to configure them with sensitive information like passwords or tokens, so they can connect to the underlying infrastructure. Secret references allow you to configure these components in a secure way by not specifying the value directly but instead referencing a secret by providing the secret name and key. Referencing a secret for the value of any string attribute of your stack components, simply specify the attribute using the following syntax: {{.}}\n\nFor example:\n\n# Register a secret called `mlflow_secret` with key-value pairs for the\n# username and password to authenticate with the MLflow tracking server', "nect the stack component to the Service Connector:$ zenml step-operator register --flavor kubernetes\nRunning with active stack: 'default' (repository)\nSuccessfully registered step operator ``.\n\n$ zenml service-connector list-resources --resource-type kubernetes-cluster -e\nThe following 'kubernetes-cluster' resources can be accessed by service connectors that you have configured:\n┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━┓\n┃ CONNECTOR ID β”‚ CONNECTOR NAME β”‚ CONNECTOR TYPE β”‚ RESOURCE TYPE β”‚ RESOURCE NAMES ┃\n┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────────────┼─────────────────────┨\n┃ e33c9fac-5daa-48b2-87bb-0187d3782cde β”‚ aws-iam-multi-eu β”‚ πŸ”Ά aws β”‚ πŸŒ€ kubernetes-cluster β”‚ kubeflowmultitenant ┃\n┃ β”‚ β”‚ β”‚ β”‚ zenbox ┃\n┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────────────┼─────────────────────┨\n┃ ed528d5a-d6cb-4fc4-bc52-c3d2d01643e5 β”‚ aws-iam-multi-us β”‚ πŸ”Ά aws β”‚ πŸŒ€ kubernetes-cluster β”‚ zenhacks-cluster ┃\n┠──────────────────────────────────────┼───────────────────────┼────────────────┼───────────────────────┼─────────────────────┨\n┃ 1c54b32a-4889-4417-abbd-42d3ace3d03a β”‚ gcp-sa-multi β”‚ πŸ”΅ gcp β”‚ πŸŒ€ kubernetes-cluster β”‚ zenml-test-cluster ┃\n┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━┛", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_384` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.1918 | | cosine_accuracy@3 | 0.5096 | | cosine_accuracy@5 | 0.6986 | | cosine_accuracy@10 | 0.811 | | cosine_precision@1 | 0.1918 | | cosine_precision@3 | 0.1699 | | cosine_precision@5 | 0.1397 | | cosine_precision@10 | 0.0811 | | cosine_recall@1 | 0.1918 | | cosine_recall@3 | 0.5096 | | cosine_recall@5 | 0.6986 | | cosine_recall@10 | 0.811 | | cosine_ndcg@10 | 0.4908 | | cosine_mrr@10 | 0.3887 | | **cosine_map@100** | **0.3947** | #### Information Retrieval * Dataset: `dim_256` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.1973 | | cosine_accuracy@3 | 0.5151 | | cosine_accuracy@5 | 0.6932 | | cosine_accuracy@10 | 0.8192 | | cosine_precision@1 | 0.1973 | | cosine_precision@3 | 0.1717 | | cosine_precision@5 | 0.1386 | | cosine_precision@10 | 0.0819 | | cosine_recall@1 | 0.1973 | | cosine_recall@3 | 0.5151 | | cosine_recall@5 | 0.6932 | | cosine_recall@10 | 0.8192 | | cosine_ndcg@10 | 0.4974 | | cosine_mrr@10 | 0.3948 | | **cosine_map@100** | **0.3999** | #### Information Retrieval * Dataset: `dim_128` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.1945 | | cosine_accuracy@3 | 0.5014 | | cosine_accuracy@5 | 0.674 | | cosine_accuracy@10 | 0.7836 | | cosine_precision@1 | 0.1945 | | cosine_precision@3 | 0.1671 | | cosine_precision@5 | 0.1348 | | cosine_precision@10 | 0.0784 | | cosine_recall@1 | 0.1945 | | cosine_recall@3 | 0.5014 | | cosine_recall@5 | 0.674 | | cosine_recall@10 | 0.7836 | | cosine_ndcg@10 | 0.4781 | | cosine_mrr@10 | 0.3808 | | **cosine_map@100** | **0.3882** | #### Information Retrieval * Dataset: `dim_64` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.1781 | | cosine_accuracy@3 | 0.4603 | | cosine_accuracy@5 | 0.6548 | | cosine_accuracy@10 | 0.7753 | | cosine_precision@1 | 0.1781 | | cosine_precision@3 | 0.1534 | | cosine_precision@5 | 0.131 | | cosine_precision@10 | 0.0775 | | cosine_recall@1 | 0.1781 | | cosine_recall@3 | 0.4603 | | cosine_recall@5 | 0.6548 | | cosine_recall@10 | 0.7753 | | cosine_ndcg@10 | 0.4625 | | cosine_mrr@10 | 0.3631 | | **cosine_map@100** | **0.3705** | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 3,284 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details |
  • min: 11 tokens
  • mean: 22.84 tokens
  • max: 48 tokens
|
  • min: 17 tokens
  • mean: 322.65 tokens
  • max: 512 tokens
| * Samples: | positive | anchor | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | How can impersonating accounts and assuming roles enhance flexibility and control in authentication methods for Service Connectors within ZenML? | ethods.

Impersonating accounts and assuming rolesThese types of authentication methods require more work to set up because multiple permission-bearing accounts and roles need to be provisioned in advance depending on the target audience. On the other hand, they also provide the most flexibility and control. Despite their operational cost, if you are a platform engineer and have the infrastructure know-how necessary to understand and set up the authentication resources, this is for you.

These authentication methods deliver another way of configuring long-lived credentials in your Service Connectors without exposing them to clients. They are especially useful as an alternative to cloud provider Service Connectors authentication methods that do not support automatically downscoping the permissions of issued temporary tokens.

The processes of account impersonation and role assumption are very similar and can be summarized as follows:

you configure a Service Connector with long-lived credentials associated with a primary user account or primary service account (preferable). As a best practice, it is common to attach a reduced set of permissions or even no permissions to these credentials other than those that allow the account impersonation or role assumption operation. This makes it more difficult to do any damage if the primary credentials are accidentally leaked.

in addition to the primary account and its long-lived credentials, you also need to provision one or more secondary access entities in the cloud platform bearing the effective permissions that will be needed to access the target resource(s):

one or more IAM roles (to be assumed)

one or more service accounts (to be impersonated)

the Service Connector configuration also needs to contain the name of a target IAM role to be assumed or a service account to be impersonated. | | How can I specify the kubeflow_hostname and provide authentication details for multi-tenant Kubeflow deployments when using ZenML? | strator_flavor import KubeflowOrchestratorSettingskubeflow_settings = KubeflowOrchestratorSettings(
client_args={},
user_namespace="my_namespace",
pod_settings={
"affinity": {...},
"tolerations": [...]
}
)

@pipeline(
settings={
"orchestrator": kubeflow_settings
}
)

This allows specifying client arguments, user namespace, pod affinity/tolerations, and more.

Multi-Tenancy Deployments

For multi-tenant Kubeflow deployments, specify the kubeflow_hostname ending in /pipeline when registering the orchestrator:

zenml orchestrator register \
--flavor=kubeflow \
--kubeflow_hostname= # e.g. https://mykubeflow.example.com/pipeline

And provide the namespace, username and password in the orchestrator settings:

kubeflow_settings = KubeflowOrchestratorSettings(
client_username="admin",
client_password="abc123",
user_namespace="namespace_name"
)

@pipeline(
settings={
"orchestrator": kubeflow_settings
}
)

For more advanced options and details, refer to the full Kubeflow Orchestrator documentation.

PreviousRun on AzureNextKubernetes

Last updated 21 days ago | | How can I contribute to the ZenML project? | ding ZenML to learn more!

Do you support Windows?ZenML officially supports Windows if you're using WSL. Much of ZenML will also work on Windows outside a WSL environment, but we don't officially support it and some features don't work (notably anything that requires spinning up a server process).

Do you support Macs running on Apple Silicon?

Yes, ZenML does support Macs running on Apple Silicon. You just need to make sure that you set the following environment variable:

export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES

This is a known issue with how forking works on Macs running on Apple Silicon and it will enable you to use ZenML and the server. This environment variable is needed if you are working with a local server on your Mac, but if you're just using ZenML as a client / CLI and connecting to a deployed server then you don't need to set it.

How can I make ZenML work with my custom tool? How can I extend or build on ZenML?

This depends on the tool and its respective MLOps category. We have a full guide on this over here!

How can I contribute?

We develop ZenML together with our community! To get involved, the best way to get started is to select any issue from the good-first-issue label. If you would like to contribute, please review our Contributing Guide for all relevant details.

How can I speak with the community?

The first point of the call should be our Slack group. Ask your questions about bugs or specific use cases and someone from the core team will respond.

Which license does ZenML use?

ZenML is distributed under the terms of the Apache License Version 2.0. A complete version of the license is available in the LICENSE.md in this repository. Any contribution made to this project will be licensed under the Apache License Version 2.0.

PreviousCommunity & content

Last updated 3 months ago | * Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 384, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `tf32`: False - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: False - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | dim_384_cosine_map@100 | dim_256_cosine_map@100 | dim_128_cosine_map@100 | dim_64_cosine_map@100 | |:----------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| | 0.1949 | 10 | 1.0787 | - | - | - | - | | 0.3898 | 20 | 0.5131 | - | - | - | - | | 0.5847 | 30 | 0.2011 | - | - | - | - | | 0.7795 | 40 | 0.1264 | - | - | - | - | | 0.9744 | 50 | 0.0754 | - | - | - | - | | 0.9939 | 51 | - | 0.3934 | 0.3870 | 0.3630 | 0.3397 | | 1.1693 | 60 | 0.0581 | - | - | - | - | | 1.3642 | 70 | 0.0543 | - | - | - | - | | 1.5591 | 80 | 0.0467 | - | - | - | - | | 1.7540 | 90 | 0.0154 | - | - | - | - | | 1.9488 | 100 | 0.0407 | - | - | - | - | | **1.9878** | **102** | **-** | **0.4025** | **0.4052** | **0.387** | **0.3672** | | 2.1437 | 110 | 0.0098 | - | - | - | - | | 2.3386 | 120 | 0.0229 | - | - | - | - | | 2.5335 | 130 | 0.0184 | - | - | - | - | | 2.7284 | 140 | 0.009 | - | - | - | - | | 2.9233 | 150 | 0.0308 | - | - | - | - | | 2.9817 | 153 | - | 0.3919 | 0.4006 | 0.3855 | 0.3718 | | 3.1181 | 160 | 0.0048 | - | - | - | - | | 3.3130 | 170 | 0.0079 | - | - | - | - | | 3.5079 | 180 | 0.0156 | - | - | - | - | | 3.7028 | 190 | 0.0075 | - | - | - | - | | 3.8977 | 200 | 0.0254 | - | - | - | - | | 3.9756 | 204 | - | 0.3947 | 0.3999 | 0.3882 | 0.3705 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.10 - Sentence Transformers: 3.2.1 - Transformers: 4.43.1 - PyTorch: 2.5.1+cu124 - Accelerate: 1.0.1 - Datasets: 3.0.2 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```