--- base_model: sentence-transformers/all-MiniLM-L6-v2 datasets: [] language: - en library_name: sentence-transformers license: apache-2.0 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 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:1490 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: Can you explain how to configure the credentials for authentication to a remote MLflow tracking server in ZenML? sentences: - 'w_bucket=gs://my_bucket --provider=You can pass other configurations specific to the stack components as key-value arguments. If you don''t provide a name, a random one is generated for you. For more information about how to work use the CLI for this, please refer to the dedicated documentation section. Authentication Methods You need to configure the following credentials for authentication to a remote MLflow tracking server: tracking_uri: The URL pointing to the MLflow tracking server. If using an MLflow Tracking Server managed by Databricks, then the value of this attribute should be "databricks". tracking_username: Username for authenticating with the MLflow tracking server. tracking_password: Password for authenticating with the MLflow tracking server. tracking_token (in place of tracking_username and tracking_password): Token for authenticating with the MLflow tracking server. tracking_insecure_tls (optional): Set to skip verifying the MLflow tracking server SSL certificate. databricks_host: The host of the Databricks workspace with the MLflow-managed server to connect to. This is only required if the tracking_uri value is set to "databricks". More information: Access the MLflow tracking server from outside Databricks Either tracking_token or tracking_username and tracking_password must be specified. This option configures the credentials for the MLflow tracking service directly as stack component attributes. This is not recommended for production settings as the credentials won''t be stored securely and will be clearly visible in the stack configuration. # Register the MLflow experiment tracker zenml experiment-tracker register mlflow_experiment_tracker --flavor=mlflow \ --tracking_uri= --tracking_token= # You can also register it like this: # zenml experiment-tracker register mlflow_experiment_tracker --flavor=mlflow \ # --tracking_uri= --tracking_username= --tracking_password= # Register and set a stack with the new experiment tracker' - 'token_hex token_hex(32)or:Copyopenssl rand -hex 32Important: If you configure encryption for your SQL database secrets store, you should keep the ZENML_SECRETS_STORE_ENCRYPTION_KEY value somewhere safe and secure, as it will always be required by the ZenML server to decrypt the secrets in the database. If you lose the encryption key, you will not be able to decrypt the secrets in the database and will have to reset them. These configuration options are only relevant if you''re using the AWS Secrets Manager as the secrets store backend. ZENML_SECRETS_STORE_TYPE: Set this to aws in order to set this type of secret store. The AWS Secrets Store uses the ZenML AWS Service Connector under the hood to authenticate with the AWS Secrets Manager API. This means that you can use any of the authentication methods supported by the AWS Service Connector to authenticate with the AWS Secrets Manager API. "Version": "2012-10-17", "Statement": [ "Sid": "ZenMLSecretsStore", "Effect": "Allow", "Action": [ "secretsmanager:CreateSecret", "secretsmanager:GetSecretValue", "secretsmanager:DescribeSecret", "secretsmanager:PutSecretValue", "secretsmanager:TagResource", "secretsmanager:DeleteSecret" ], "Resource": "arn:aws:secretsmanager:::secret:zenml/*" The following configuration options are supported: ZENML_SECRETS_STORE_AUTH_METHOD: The AWS Service Connector authentication method to use (e.g. secret-key or iam-role). ZENML_SECRETS_STORE_AUTH_CONFIG: The AWS Service Connector configuration, in JSON format (e.g. {"aws_access_key_id":"","aws_secret_access_key":"","region":""}). Note: The remaining configuration options are deprecated and may be removed in a future release. Instead, you should set the ZENML_SECRETS_STORE_AUTH_METHOD and ZENML_SECRETS_STORE_AUTH_CONFIG variables to use the AWS Service Connector authentication method.' - 'tive Directory credentials or generic OIDC tokens.This authentication method only requires a GCP workload identity external account JSON file that only contains the configuration for the external account without any sensitive credentials. It allows implementing a two layer authentication scheme that keeps the set of permissions associated with implicit credentials down to the bare minimum and grants permissions to the privilege-bearing GCP service account instead. This authentication method can be used to authenticate to GCP services using credentials from other cloud providers or identity providers. When used with workloads running on AWS or Azure, it involves automatically picking up credentials from the AWS IAM or Azure AD identity associated with the workload and using them to authenticate to GCP services. This means that the result depends on the environment where the ZenML server is deployed and is thus not fully reproducible. When used with AWS or Azure implicit in-cloud authentication, this method may constitute a security risk, because it can give users access to the identity (e.g. AWS IAM role or Azure AD principal) implicitly associated with the environment where the ZenML server is running. For this reason, all implicit authentication methods are disabled by default and need to be explicitly enabled by setting the ZENML_ENABLE_IMPLICIT_AUTH_METHODS environment variable or the helm chart enableImplicitAuthMethods configuration option to true in the ZenML deployment. By default, the GCP connector generates temporary OAuth 2.0 tokens from the external account credentials and distributes them to clients. The tokens have a limited lifetime of 1 hour. This behavior can be disabled by setting the generate_temporary_tokens configuration option to False, in which case, the connector will distribute the external account credentials JSON to clients instead (not recommended).' - source_sentence: What is an example of a ZenML server YAML configuration file? sentences: - 'sing a type annotation. Tuple vs multiple outputsIt is impossible for ZenML to detect whether you want your step to have a single output artifact of type Tuple or multiple output artifacts just by looking at the type annotation. We use the following convention to differentiate between the two: When the return statement is followed by a tuple literal (e.g. return 1, 2 or return (value_1, value_2)) we treat it as a step with multiple outputs. All other cases are treated as a step with a single output of type Tuple. from zenml import step from typing_extensions import Annotated from typing import Tuple # Single output artifact @step def my_step() -> Tuple[int, int]: output_value = (0, 1) return output_value # Single output artifact with variable length @step def my_step(condition) -> Tuple[int, ...]: if condition: output_value = (0, 1) else: output_value = (0, 1, 2) return output_value # Single output artifact using the `Annotated` annotation @step def my_step() -> Annotated[Tuple[int, ...], "my_output"]: return 0, 1 # Multiple output artifacts @step def my_step() -> Tuple[int, int]: return 0, 1 # Not allowed: Variable length tuple annotation when using # multiple output artifacts @step def my_step() -> Tuple[int, ...]: return 0, 1 Step output names By default, ZenML uses the output name output for single output steps and output_0, output_1, ... for steps with multiple outputs. These output names are used to display your outputs in the dashboard and fetch them after your pipeline is finished. If you want to use custom output names for your steps, use the Annotated type annotation: from typing_extensions import Annotated # or `from typing import Annotated on Python 3.9+ from typing import Tuple from zenml import step @step def square_root(number: int) -> Annotated[float, "custom_output_name"]: return number ** 0.5 @step def divide(a: int, b: int) -> Tuple[ Annotated[int, "quotient"], Annotated[int, "remainder"] ]: return a // b, a % b' - 'HyperAI Orchestrator Orchestrating your pipelines to run on HyperAI.ai instances. HyperAI is a cutting-edge cloud compute platform designed to make AI accessible for everyone. The HyperAI orchestrator is an orchestrator flavor that allows you to easily deploy your pipelines on HyperAI instances. This component is only meant to be used within the context of a remote ZenML deployment scenario. Usage with a local ZenML deployment may lead to unexpected behavior! When to use it You should use the HyperAI orchestrator if: you''re looking for a managed solution for running your pipelines. you''re a HyperAI customer. Prerequisites You will need to do the following to start using the HyperAI orchestrator: Have a running HyperAI instance. It must be accessible from the internet (or at least from the IP addresses of your ZenML users) and allow SSH key based access (passwords are not supported). Ensure that a recent version of Docker is installed. This version must include Docker Compose, meaning that the command docker compose works. Ensure that the appropriate NVIDIA Driver is installed on the HyperAI instance (if not already installed by the HyperAI team). Ensure that the NVIDIA Container Toolkit is installed and configured on the HyperAI instance. Note that it is possible to omit installing the NVIDIA Driver and NVIDIA Container Toolkit. However, you will then be unable to use the GPU from within your ZenML pipeline. Additionally, you will then need to disable GPU access within the container when configuring the Orchestrator component, or the pipeline will not start correctly. How it works' - 'fied, or a string, in which case it must be a path# to a CA certificate bundle to use or the CA bundle value itself verify_ssl: Here is an example of a ZenML server YAML configuration file: url: https://ac8ef63af203226194a7725ee71d85a-7635928635.us-east-1.elb.amazonaws.com/zenml verify_ssl: | -----BEGIN CERTIFICATE----- ... -----END CERTIFICATE----- To disconnect from the current ZenML server and revert to using the local default database, use the following command: zenml disconnect How does it work? Here''s an architecture diagram that shows how the workflow looks like when you do zenml deploy. The deploy CLI makes use of a "recipe" inside the zenml-io/zenml repository to deploy the server on the right cloud. Any configuration that you pass with the CLI, is sent to the recipe as input variables. PreviousDeploying ZenML NextDeploy with Docker Last updated 15 days ago' - source_sentence: When should I update my service account name to ensure security? sentences: - 'y update. Important noticeEvery API key issued is a potential gateway to access your data, secrets and infrastructure. It''s important to regularly rotate API keys and deactivate or delete service accounts and API keys that are no longer needed. PreviousConnect in with your User (interactive) NextInteract with secrets Last updated 15 days ago' - 'Connect in with your User (interactive) You can authenticate your clients with the ZenML Server using the ZenML CLI and the web based login. This can be executed with the command: zenml connect --url https://... This command will start a series of steps to validate the device from where you are connecting that will happen in your browser. You can choose whether to mark your respective device as trusted or not. If you choose not to click Trust this device, a 24-hour token will be issued for authentication services. Choosing to trust the device will issue a 30-day token instead. To see all devices you''ve permitted, use the following command: zenml authorized-device list Additionally, the following command allows you to more precisely inspect one of these devices: zenml authorized-device describe For increased security, you can invalidate a token using the zenml device lock command followed by the device ID. This helps provide an extra layer of security and control over your devices. zenml authorized-device lock To keep things simple, we can summarize the steps: Use the zenml connect --url command to start a device flow and connect to a zenml server. Choose whether to trust the device when prompted. Check permitted devices with zenml devices list. Invalidate a token with zenml device lock .... Important notice Using the ZenML CLI is a secure and comfortable way to interact with your ZenML tenants. It''s important to always ensure that only trusted devices are used to maintain security and privacy. Don''t forget to manage your device trust levels regularly for optimal security. Should you feel a device trust needs to be revoked, lock the device immediately. Every token issued is a potential gateway to access your data, secrets and infrastructure. PreviousConnect to a server NextConnect with a Service Account Last updated 19 days ago' - '━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━┷━━━━━━━┷━━━━━━━━┛A lot more is hidden behind a Service Connector Type than a name and a simple list of resource types. Before using a Service Connector Type to configure a Service Connector, you probably need to understand what it is, what it can offer and what are the supported authentication methods and their requirements. All this can be accessed directly through the CLI. Some examples are included here. Showing information about the gcp Service Connector Type: zenml service-connector describe-type gcp Example Command Output ╔══════════════════════════════════════════════════════════════════════════════╗ ║ 🔵 GCP Service Connector (connector type: gcp) ║ ╚══════════════════════════════════════════════════════════════════════════════╝ Authentication methods: 🔒 implicit 🔒 user-account 🔒 service-account 🔒 oauth2-token 🔒 impersonation Resource types: 🔵 gcp-generic 📦 gcs-bucket 🌀 kubernetes-cluster 🐳 docker-registry Supports auto-configuration: True Available locally: True Available remotely: True The ZenML GCP Service Connector facilitates the authentication and access to managed GCP services and resources. These encompass a range of resources, including GCS buckets, GCR container repositories and GKE clusters. The connector provides support for various authentication methods, including GCP user accounts, service accounts, short-lived OAuth 2.0 tokens and implicit authentication. To ensure heightened security measures, this connector always issues short-lived OAuth 2.0 tokens to clients instead of long-lived credentials. Furthermore, it includes automatic configuration and detection of credentials locally configured through the GCP CLI. This connector serves as a general means of accessing any GCP service by issuing OAuth 2.0 credential objects to clients. Additionally, the connector can handle specialized authentication for GCS, Docker and Kubernetes Python clients. It' - source_sentence: Where can I find the instructions to clone the ZenML quickstart repository and set up the stack? sentences: - 'into play when the component is ultimately in use.The design behind this interaction lets us separate the configuration of the flavor from its implementation. This way we can register flavors and components even when the major dependencies behind their implementation are not installed in our local setting (assuming the CustomArtifactStoreFlavor and the CustomArtifactStoreConfig are implemented in a different module/path than the actual CustomArtifactStore). Enabling Artifact Visualizations with Custom Artifact Stores ZenML automatically saves visualizations for many common data types and allows you to view these visualizations in the ZenML dashboard. Under the hood, this works by saving the visualizations together with the artifacts in the artifact store. In order to load and display these visualizations, ZenML needs to be able to load and access the corresponding artifact store. This means that your custom artifact store needs to be configured in a way that allows authenticating to the back-end without relying on the local environment, e.g., by embedding the authentication credentials in the stack component configuration or by referencing a secret. Furthermore, for deployed ZenML instances, you need to install the package dependencies of your artifact store implementation in the environment where you have deployed ZenML. See the Documentation on deploying ZenML with custom Docker images for more information on how to do that. PreviousAzure Blob Storage NextContainer Registries Last updated 19 days ago' - 't_repository: str user: Optional[str] resources:cpu_count: Optional[PositiveFloat] gpu_count: Optional[NonNegativeInt] memory: Optional[ConstrainedStrValue] step_operator: Optional[str] success_hook_source: attribute: Optional[str] module: str type: SourceType train_model: enable_artifact_metadata: Optional[bool] enable_artifact_visualization: Optional[bool] enable_cache: Optional[bool] enable_step_logs: Optional[bool] experiment_tracker: Optional[str] extra: Mapping[str, Any] failure_hook_source: attribute: Optional[str] module: str type: SourceType model: audience: Optional[str] description: Optional[str] ethics: Optional[str] license: Optional[str] limitations: Optional[str] name: str save_models_to_registry: bool suppress_class_validation_warnings: bool tags: Optional[List[str]] trade_offs: Optional[str] use_cases: Optional[str] version: Union[ModelStages, int, str, NoneType] was_created_in_this_run: bool name: Optional[str] outputs: {} parameters: {} settings: docker: apt_packages: List[str] build_context_root: Optional[str] build_options: Mapping[str, Any] copy_files: bool copy_global_config: bool dockerfile: Optional[str] dockerignore: Optional[str] environment: Mapping[str, Any] install_stack_requirements: bool parent_image: Optional[str] python_package_installer: PythonPackageInstaller replicate_local_python_environment: Union[List[str], PythonEnvironmentExportMethod, NoneType] required_hub_plugins: List[str] required_integrations: List[str] requirements: Union[NoneType, str, List[str]] skip_build: bool source_files: SourceFileMode target_repository: str user: Optional[str] resources: cpu_count: Optional[PositiveFloat] gpu_count: Optional[NonNegativeInt] memory: Optional[ConstrainedStrValue] step_operator: Optional[str] success_hook_source: attribute: Optional[str] module: str type: SourceType' - 'as the ZenML quickstart. You can clone it like so:git clone --depth 1 git@github.com:zenml-io/zenml.git cd zenml/examples/quickstart pip install -r requirements.txt zenml init To run a pipeline using the new stack: Set the stack as active on your clientCopyzenml stack set a_new_local_stack Run your pipeline code:Copypython run.py --training-pipeline Keep this code handy as we''ll be using it in the next chapters! PreviousDeploying ZenML NextConnecting remote storage Last updated 19 days ago' - source_sentence: How do I register and connect an S3 artifact store in ZenML using the interactive mode? sentences: - 'hich Resource Name to use in the interactive mode:zenml artifact-store register s3-zenfiles --flavor s3 --path=s3://zenfiles zenml service-connector list-resources --resource-type s3-bucket --resource-id s3://zenfiles zenml artifact-store connect s3-zenfiles --connector aws-multi-type Example Command Output $ zenml artifact-store register s3-zenfiles --flavor s3 --path=s3://zenfiles Running with active workspace: ''default'' (global) Running with active stack: ''default'' (global) Successfully registered artifact_store `s3-zenfiles`. $ zenml service-connector list-resources --resource-type s3-bucket --resource-id zenfiles The ''s3-bucket'' resource with name ''zenfiles'' can be accessed by service connectors configured in your workspace: ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓ ┃ CONNECTOR ID │ CONNECTOR NAME │ CONNECTOR TYPE │ RESOURCE TYPE │ RESOURCE NAMES ┃ ┠──────────────────────────────────────┼──────────────────────┼────────────────┼───────────────┼────────────────┨ ┃ 4a550c82-aa64-4a48-9c7f-d5e127d77a44 │ aws-multi-type │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃ ┠──────────────────────────────────────┼──────────────────────┼────────────────┼───────────────┼────────────────┨ ┃ 66c0922d-db84-4e2c-9044-c13ce1611613 │ aws-multi-instance │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃ ┠──────────────────────────────────────┼──────────────────────┼────────────────┼───────────────┼────────────────┨ ┃ 65c82e59-cba0-4a01-b8f6-d75e8a1d0f55 │ aws-single-instance │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃ ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛ $ zenml artifact-store connect s3-zenfiles --connector aws-multi-type Running with active workspace: ''default'' (global) Running with active stack: ''default'' (global) Successfully connected artifact store `s3-zenfiles` to the following resources:' - '👣Step Operators Executing individual steps in specialized environments. The step operator enables the execution of individual pipeline steps in specialized runtime environments that are optimized for certain workloads. These specialized environments can give your steps access to resources like GPUs or distributed processing frameworks like Spark. Comparison to orchestrators: The orchestrator is a mandatory stack component that is responsible for executing all steps of a pipeline in the correct order and providing additional features such as scheduling pipeline runs. The step operator on the other hand is used to only execute individual steps of the pipeline in a separate environment in case the environment provided by the orchestrator is not feasible. When to use it A step operator should be used if one or more steps of a pipeline require resources that are not available in the runtime environments provided by the orchestrator. An example would be a step that trains a computer vision model and requires a GPU to run in a reasonable time, combined with a Kubeflow orchestrator running on a Kubernetes cluster that does not contain any GPU nodes. In that case, it makes sense to include a step operator like SageMaker, Vertex, or AzureML to execute the training step with a GPU. Step Operator Flavors Step operators to execute steps on one of the big cloud providers are provided by the following ZenML integrations: Step Operator Flavor Integration Notes SageMaker sagemaker aws Uses SageMaker to execute steps Vertex vertex gcp Uses Vertex AI to execute steps AzureML azureml azure Uses AzureML to execute steps Spark spark spark Uses Spark on Kubernetes to execute steps in a distributed manner Custom Implementation custom Extend the step operator abstraction and provide your own implementation If you would like to see the available flavors of step operators, you can use the command: zenml step-operator flavor list How to use it' - 'Azure Container Registry Storing container images in Azure. The Azure container registry is a container registry flavor that comes built-in with ZenML and uses the Azure Container Registry to store container images. When to use it You should use the Azure container registry if: one or more components of your stack need to pull or push container images. you have access to Azure. If you''re not using Azure, take a look at the other container registry flavors. How to deploy it Go here and choose a subscription, resource group, location, and registry name. Then click on Review + Create and to create your container registry. How to find the registry URI The Azure container registry URI should have the following format: .azurecr.io # Examples: zenmlregistry.azurecr.io myregistry.azurecr.io To figure out the URI for your registry: Go to the Azure portal. In the search bar, enter container registries and select the container registry you want to use. If you don''t have any container registries yet, check out the deployment section on how to create one. Use the name of your registry to fill the template .azurecr.io and get your URI. How to use it To use the Azure container registry, we need: Docker installed and running. The registry URI. Check out the previous section on the URI format and how to get the URI for your registry. We can then register the container registry and use it in our active stack: zenml container-registry register \ --flavor=azure \ --uri= # Add the container registry to the active stack zenml stack update -c You also need to set up authentication required to log in to the container registry. Authentication Methods' model-index: - name: zenml/finetuned-all-MiniLM-L6-v2 results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 384 type: dim_384 metrics: - type: cosine_accuracy@1 value: 0.3132530120481928 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6144578313253012 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7168674698795181 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7891566265060241 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.3132530120481928 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.20481927710843373 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1433734939759036 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.0789156626506024 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.3132530120481928 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6144578313253012 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7168674698795181 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7891566265060241 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5579120329651274 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.48292933639319197 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4907452723782479 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.2891566265060241 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6144578313253012 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7108433734939759 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7650602409638554 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2891566265060241 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.20481927710843373 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14216867469879516 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07650602409638553 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.2891566265060241 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6144578313253012 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7108433734939759 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7650602409638554 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5394043126982406 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.46553595333715836 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4739275972429515 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.28313253012048195 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5481927710843374 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6506024096385542 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7168674698795181 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.28313253012048195 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.1827309236947791 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1301204819277108 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07168674698795179 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.28313253012048195 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5481927710843374 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6506024096385542 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7168674698795181 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5067699591037801 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.43858529355517323 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.44791284428498435 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.24096385542168675 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.46987951807228917 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5843373493975904 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6807228915662651 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.24096385542168675 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.1566265060240964 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11686746987951806 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.06807228915662648 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.24096385542168675 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.46987951807228917 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.5843373493975904 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.6807228915662651 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.45307543718220417 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3806679097341751 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.389050349953244 name: Cosine Map@100 --- # zenml/finetuned-all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 tokens - **Similarity Function:** Cosine Similarity - **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': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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 🤗 Hub model = SentenceTransformer("zenml/finetuned-all-MiniLM-L6-v2") # Run inference sentences = [ 'How do I register and connect an S3 artifact store in ZenML using the interactive mode?', "hich Resource Name to use in the interactive mode:zenml artifact-store register s3-zenfiles --flavor s3 --path=s3://zenfiles\n\nzenml service-connector list-resources --resource-type s3-bucket --resource-id s3://zenfiles\n\nzenml artifact-store connect s3-zenfiles --connector aws-multi-type\n\nExample Command Output\n\n$ zenml artifact-store register s3-zenfiles --flavor s3 --path=s3://zenfiles\n\nRunning with active workspace: 'default' (global)\n\nRunning with active stack: 'default' (global)\n\nSuccessfully registered artifact_store `s3-zenfiles`.\n\n$ zenml service-connector list-resources --resource-type s3-bucket --resource-id zenfiles\n\nThe 's3-bucket' resource with name 'zenfiles' can be accessed by service connectors configured in your workspace:\n\n┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┓\n\n┃ CONNECTOR ID │ CONNECTOR NAME │ CONNECTOR TYPE │ RESOURCE TYPE │ RESOURCE NAMES ┃\n\n┠──────────────────────────────────────┼──────────────────────┼────────────────┼───────────────┼────────────────┨\n\n┃ 4a550c82-aa64-4a48-9c7f-d5e127d77a44 │ aws-multi-type │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃\n\n┠──────────────────────────────────────┼──────────────────────┼────────────────┼───────────────┼────────────────┨\n\n┃ 66c0922d-db84-4e2c-9044-c13ce1611613 │ aws-multi-instance │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃\n\n┠──────────────────────────────────────┼──────────────────────┼────────────────┼───────────────┼────────────────┨\n\n┃ 65c82e59-cba0-4a01-b8f6-d75e8a1d0f55 │ aws-single-instance │ 🔶 aws │ 📦 s3-bucket │ s3://zenfiles ┃\n\n┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┛\n\n$ zenml artifact-store connect s3-zenfiles --connector aws-multi-type\n\nRunning with active workspace: 'default' (global)\n\nRunning with active stack: 'default' (global)\n\nSuccessfully connected artifact store `s3-zenfiles` to the following resources:", "Azure Container Registry\n\nStoring container images in Azure.\n\nThe Azure container registry is a container registry flavor that comes built-in with ZenML and uses the Azure Container Registry to store container images.\n\nWhen to use it\n\nYou should use the Azure container registry if:\n\none or more components of your stack need to pull or push container images.\n\nyou have access to Azure. If you're not using Azure, take a look at the other container registry flavors.\n\nHow to deploy it\n\nGo here and choose a subscription, resource group, location, and registry name. Then click on Review + Create and to create your container registry.\n\nHow to find the registry URI\n\nThe Azure container registry URI should have the following format:\n\n.azurecr.io\n\n# Examples:\n\nzenmlregistry.azurecr.io\n\nmyregistry.azurecr.io\n\nTo figure out the URI for your registry:\n\nGo to the Azure portal.\n\nIn the search bar, enter container registries and select the container registry you want to use. If you don't have any container registries yet, check out the deployment section on how to create one.\n\nUse the name of your registry to fill the template .azurecr.io and get your URI.\n\nHow to use it\n\nTo use the Azure container registry, we need:\n\nDocker installed and running.\n\nThe registry URI. Check out the previous section on the URI format and how to get the URI for your registry.\n\nWe can then register the container registry and use it in our active stack:\n\nzenml container-registry register \\\n\n--flavor=azure \\\n\n--uri=\n\n# Add the container registry to the active stack\n\nzenml stack update -c \n\nYou also need to set up authentication required to log in to the container registry.\n\nAuthentication Methods", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # 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 | 0.3133 | | cosine_accuracy@3 | 0.6145 | | cosine_accuracy@5 | 0.7169 | | cosine_accuracy@10 | 0.7892 | | cosine_precision@1 | 0.3133 | | cosine_precision@3 | 0.2048 | | cosine_precision@5 | 0.1434 | | cosine_precision@10 | 0.0789 | | cosine_recall@1 | 0.3133 | | cosine_recall@3 | 0.6145 | | cosine_recall@5 | 0.7169 | | cosine_recall@10 | 0.7892 | | cosine_ndcg@10 | 0.5579 | | cosine_mrr@10 | 0.4829 | | **cosine_map@100** | **0.4907** | #### 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 | 0.2892 | | cosine_accuracy@3 | 0.6145 | | cosine_accuracy@5 | 0.7108 | | cosine_accuracy@10 | 0.7651 | | cosine_precision@1 | 0.2892 | | cosine_precision@3 | 0.2048 | | cosine_precision@5 | 0.1422 | | cosine_precision@10 | 0.0765 | | cosine_recall@1 | 0.2892 | | cosine_recall@3 | 0.6145 | | cosine_recall@5 | 0.7108 | | cosine_recall@10 | 0.7651 | | cosine_ndcg@10 | 0.5394 | | cosine_mrr@10 | 0.4655 | | **cosine_map@100** | **0.4739** | #### 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 | 0.2831 | | cosine_accuracy@3 | 0.5482 | | cosine_accuracy@5 | 0.6506 | | cosine_accuracy@10 | 0.7169 | | cosine_precision@1 | 0.2831 | | cosine_precision@3 | 0.1827 | | cosine_precision@5 | 0.1301 | | cosine_precision@10 | 0.0717 | | cosine_recall@1 | 0.2831 | | cosine_recall@3 | 0.5482 | | cosine_recall@5 | 0.6506 | | cosine_recall@10 | 0.7169 | | cosine_ndcg@10 | 0.5068 | | cosine_mrr@10 | 0.4386 | | **cosine_map@100** | **0.4479** | #### 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 | 0.241 | | cosine_accuracy@3 | 0.4699 | | cosine_accuracy@5 | 0.5843 | | cosine_accuracy@10 | 0.6807 | | cosine_precision@1 | 0.241 | | cosine_precision@3 | 0.1566 | | cosine_precision@5 | 0.1169 | | cosine_precision@10 | 0.0681 | | cosine_recall@1 | 0.241 | | cosine_recall@3 | 0.4699 | | cosine_recall@5 | 0.5843 | | cosine_recall@10 | 0.6807 | | cosine_ndcg@10 | 0.4531 | | cosine_mrr@10 | 0.3807 | | **cosine_map@100** | **0.3891** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 1,490 training samples * Columns: positive and anchor * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details |

min: 9 tokens
mean: 21.23 tokens
max: 49 tokens

min: 23 tokens
mean: 237.64 tokens
max: 256 tokens

| * Samples: | positive | anchor | |:---------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | How can you leverage MLflow for tracking and visualizing experiment results in ZenML? |

MLflow

Logging and visualizing experiments with MLflow.

The MLflow Experiment Tracker is an Experiment Tracker flavor provided with the MLflow ZenML integration that uses the MLflow tracking service to log and visualize information from your pipeline steps (e.g. models, parameters, metrics).

When would you want to use it?

MLflow Tracking is a very popular tool that you would normally use in the iterative ML experimentation phase to track and visualize experiment results. That doesn't mean that it cannot be repurposed to track and visualize the results produced by your automated pipeline runs, as you make the transition toward a more production-oriented workflow.

You should use the MLflow Experiment Tracker:

if you have already been using MLflow to track experiment results for your project and would like to continue doing so as you are incorporating MLOps workflows and best practices in your project through ZenML.

if you are looking for a more visually interactive way of navigating the results produced from your ZenML pipeline runs (e.g. models, metrics, datasets)

if you or your team already have a shared MLflow Tracking service deployed somewhere on-premise or in the cloud, and you would like to connect ZenML to it to share the artifacts and metrics logged by your pipelines

You should consider one of the other Experiment Tracker flavors if you have never worked with MLflow before and would rather use another experiment tracking tool that you are more familiar with.

How do you deploy it?

The MLflow Experiment Tracker flavor is provided by the MLflow ZenML integration, you need to install it on your local machine to be able to register an MLflow Experiment Tracker and add it to your stack:

zenml integration install mlflow -y

The MLflow Experiment Tracker can be configured to accommodate the following MLflow deployment scenarios:

| | What are the required integrations for running pipelines with a Docker-based orchestrator in ZenML? |

ctivated by installing the respective integration:Integration Materializer Handled Data Types Storage Format bentoml BentoMaterializer bentoml.Bento .bento deepchecks DeepchecksResultMateriailzer deepchecks.CheckResult , deepchecks.SuiteResult .json evidently EvidentlyProfileMaterializer evidently.Profile .json great_expectations GreatExpectationsMaterializer great_expectations.ExpectationSuite , great_expectations.CheckpointResult .json huggingface HFDatasetMaterializer datasets.Dataset , datasets.DatasetDict Directory huggingface HFPTModelMaterializer transformers.PreTrainedModel Directory huggingface HFTFModelMaterializer transformers.TFPreTrainedModel Directory huggingface HFTokenizerMaterializer transformers.PreTrainedTokenizerBase Directory lightgbm LightGBMBoosterMaterializer lgbm.Booster .txt lightgbm LightGBMDatasetMaterializer lgbm.Dataset .binary neural_prophet NeuralProphetMaterializer NeuralProphet .pt pillow PillowImageMaterializer Pillow.Image .PNG polars PolarsMaterializer pl.DataFrame , pl.Series .parquet pycaret PyCaretMaterializer Any sklearn , xgboost , lightgbm or catboost model .pkl pytorch PyTorchDataLoaderMaterializer torch.Dataset , torch.DataLoader .pt pytorch PyTorchModuleMaterializer torch.Module .pt scipy SparseMaterializer scipy.spmatrix .npz spark SparkDataFrameMaterializer pyspark.DataFrame .parquet spark SparkModelMaterializer pyspark.Transformer pyspark.Estimator tensorflow KerasMaterializer tf.keras.Model Directory tensorflow TensorflowDatasetMaterializer tf.Dataset Directory whylogs WhylogsMaterializer whylogs.DatasetProfileView .pb xgboost XgboostBoosterMaterializer xgb.Booster .json xgboost XgboostDMatrixMaterializer xgb.DMatrix .binary

If you are running pipelines with a Docker-based orchestrator, you need to specify the corresponding integration as required_integrations in the DockerSettings of your pipeline in order to have the integration materializer available inside your Docker container. See the pipeline configuration documentation for more information.

| | What is the difference between the stack component settings at registration time and runtime for ZenML? |

ettings to specify AzureML step operator settings.Difference between stack component settings at registration-time vs real-time

For stack-component-specific settings, you might be wondering what the difference is between these and the configuration passed in while doing zenml stack-component register  --config1=configvalue --config2=configvalue, etc. The answer is that the configuration passed in at registration time is static and fixed throughout all pipeline runs, while the settings can change.

A good example of this is the MLflow Experiment Tracker, where configuration which remains static such as the tracking_url is sent through at registration time, while runtime configuration such as the experiment_name (which might change every pipeline run) is sent through as runtime settings.

Even though settings can be overridden at runtime, you can also specify default values for settings while configuring a stack component. For example, you could set a default value for the nested setting of your MLflow experiment tracker: zenml experiment-tracker register  --flavor=mlflow --nested=True

This means that all pipelines that run using this experiment tracker use nested MLflow runs unless overridden by specifying settings for the pipeline at runtime.

Using the right key for Stack-component-specific settings

When specifying stack-component-specific settings, a key needs to be passed. This key should always correspond to the pattern: .

For example, the SagemakerStepOperator supports passing in estimator_args. The way to specify this would be to use the key step_operator.sagemaker

@step(step_operator="nameofstepoperator", settings= {"step_operator.sagemaker": {"estimator_args": {"instance_type": "m7g.medium"}}})

def my_step():

...

# Using the class

@step(step_operator="nameofstepoperator", settings= {"step_operator.sagemaker": SagemakerStepOperatorSettings(instance_type="m7g.medium")})

def my_step():

...

or in YAML:

steps:

my_step:

| * 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`: 32 - `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 - `bf16`: True - `tf32`: True - `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`: 32 - `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 - `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`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: True - `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`: True - `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 - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional

### Training Logs | Epoch | Step | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_384_cosine_map@100 | dim_64_cosine_map@100 | |:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| | 0.6667 | 1 | 0.4153 | 0.4312 | 0.4460 | 0.3779 | | **2.0** | **3** | **0.4465** | **0.4643** | **0.4824** | **0.3832** | | 2.6667 | 4 | 0.4479 | 0.4739 | 0.4907 | 0.3891 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.1+cu121 - Accelerate: 0.31.0 - Datasets: 2.19.1 - 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} } ```