Add new SentenceTransformer model.

README.md

@@ -29,7 +29,7 @@ tags:
 - generated_from_trainer
 - dataset_size:1490
 - loss:MatryoshkaLoss
-- loss:MultipleNegativesRankingLoss
+- loss:TripletLoss
 widget:
 - source_sentence: Where is the global configuration directory located in ZenML's
     default setup?
@@ -113,6 +113,7 @@ widget:
 
 
     As shown above, the global config directory stores the following information:'
+  - How do you configure the network settings on a Linux server?
   - 'Reranking for better retrieval
 
 
@@ -145,132 +146,9 @@ widget:
 
 
     Last updated 1 month ago'
-  - '─────────────────────────────────────────────────┨┃ RESOURCE TYPES   │ 🔵 gcp-generic,
-    📦 gcs-bucket, 🌀 kubernetes-cluster, 🐳 docker-registry ┃
-
-
-    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
-
-
-    ┃ RESOURCE NAME    │ <multiple>                                                               ┃
-
-
-    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
-
-
-    ┃ SECRET ID        │ 4694de65-997b-4929-8831-b49d5e067b97                                     ┃
-
-
-    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
-
-
-    ┃ SESSION DURATION │ N/A                                                                      ┃
-
-
-    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
-
-
-    ┃ EXPIRES IN       │ 59m46s                                                                   ┃
-
-
-    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
-
-
-    ┃ OWNER            │ default                                                                  ┃
-
-
-    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
-
-
-    ┃ WORKSPACE        │ default                                                                  ┃
-
-
-    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
-
-
-    ┃ SHARED           │ ➖                                                                       ┃
-
-
-    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
-
-
-    ┃ CREATED_AT       │ 2023-05-19 09:04:33.557126                                               ┃
-
-
-    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
-
-
-    ┃ UPDATED_AT       │ 2023-05-19 09:04:33.557127                                               ┃
-
-
-    ┗━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
-
-
-    Configuration
-
-
-    ┏━━━━━━━━━━━━┯━━━━━━━━━━━━┓'
 - source_sentence: Where can I find the instructions to enable CUDA for GPU-backed
     hardware in ZenML SDK Docs?
   sentences:
-  - 'Configure a code repository
-
-
-    Connect a Git repository to ZenML to track code changes and collaborate on MLOps
-    projects.
-
-
-    Throughout the lifecycle of a MLOps pipeline, it can get quite tiresome to always
-    wait for a Docker build every time after running a pipeline (even if the local
-    Docker cache is used). However, there is a way to just have one pipeline build
-    and keep reusing it until a change to the pipeline environment is made: by connecting
-    a code repository.
-
-
-    With ZenML, connecting to a Git repository optimizes the Docker build processes.
-    It also has the added bonus of being a better way of managing repository changes
-    and enabling better code collaboration. Here is how the flow changes when running
-    a pipeline:
-
-
-    You trigger a pipeline run on your local machine. ZenML parses the @pipeline function
-    to determine the necessary steps.
-
-
-    The local client requests stack information from the ZenML server, which responds
-    with the cloud stack configuration.
-
-
-    The local client detects that we''re using a code repository and requests the
-    information from the git repo.
-
-
-    Instead of building a new Docker image, the client checks if an existing image
-    can be reused based on the current Git commit hash and other environment metadata.
-
-
-    The client initiates a run in the orchestrator, which sets up the execution environment
-    in the cloud, such as a VM.
-
-
-    The orchestrator downloads the code directly from the Git repository and uses
-    the existing Docker image to run the pipeline steps.
-
-
-    Pipeline steps execute, storing artifacts in the cloud-based artifact store.
-
-
-    Throughout the execution, the pipeline run status and metadata are reported back
-    to the ZenML server.
-
-
-    By connecting a Git repository, you avoid redundant builds and make your MLOps
-    processes more efficient. Your team can work on the codebase simultaneously, with
-    ZenML handling the version tracking and ensuring that the correct code version
-    is always used for each run.
-
-
-    Creating a GitHub Repository'
   - 'Migration guide 0.39.1 → 0.41.0
 
 
@@ -397,499 +275,248 @@ widget:
     param_1: int, param_2: Optional[float] = None
 
 
-    ) -> Tuple[Annotated[int, "int_output"], Annotated[str, "str_output"]]:
-
-
-    result = int(param_1 * (param_2 or 1))
-
-
-    result_uri = get_step_context().get_output_artifact_uri()
-
-
-    return result, result_uri
-
-
-    # Run the Step separately
-
-
-    my_step()
-
-
-    # Define a Pipeline
-
-
-    @pipeline'
-  - ' SDK Docs .
-
-
-    Enabling CUDA for GPU-backed hardwareNote that if you wish to use this step operator
-    to run steps on a GPU, you will need to follow the instructions on this page to
-    ensure that it works. It requires adding some extra settings customization and
-    is essential to enable CUDA for the GPU to give its full acceleration.
-
-
-    PreviousStep Operators
-
-
-    NextGoogle Cloud VertexAI
-
-
-    Last updated 19 days ago'
-- source_sentence: What are the special metadata types supported by ZenML and how
-    are they used?
-  sentences:
-  - 'Special Metadata Types
-
-
-    Tracking your metadata.
-
-
-    ZenML supports several special metadata types to capture specific kinds of information.
-    Here are examples of how to use the special types Uri, Path, DType, and StorageSize:
-
-
-    from zenml.metadata.metadata_types import StorageSize, DType
-
-
-    from zenml import log_artifact_metadata
-
-
-    log_artifact_metadata(
-
-
-    metadata={
-
-
-    "dataset_source": Uri("gs://my-bucket/datasets/source.csv"),
-
-
-    "preprocessing_script": Path("/scripts/preprocess.py"),
-
-
-    "column_types": {
-
-
-    "age": DType("int"),
-
-
-    "income": DType("float"),
-
-
-    "score": DType("int")
-
-
-    },
-
-
-    "processed_data_size": StorageSize(2500000)
-
-
-    In this example:
-
-
-    Uri is used to indicate a dataset source URI.
-
-
-    Path is used to specify the filesystem path to a preprocessing script.
-
-
-    DType is used to describe the data types of specific columns.
-
-
-    StorageSize is used to indicate the size of the processed data in bytes.
-
-
-    These special types help standardize the format of metadata and ensure that it
-    is logged in a consistent and interpretable manner.
-
-
-    PreviousGroup metadata
-
-
-    NextFetch metadata within steps
-
-
-    Last updated 19 days ago'
-  - 's is achieved using the log_model_metadata method:from zenml import get_step_context,
-    step, log_model_metadata
-
-
-    @step
-
-
-    def svc_trainer(
-
-
-    X_train: pd.DataFrame,
-
-
-    y_train: pd.Series,
-
-
-    gamma: float = 0.001,
-
-
-    ) -> Annotated[ClassifierMixin, "sklearn_classifier"],:
-
-
-    # Train and score model
-
-
-    ...
-
-
-    model.fit(dataset[0], dataset[1])
-
-
-    accuracy = model.score(dataset[0], dataset[1])
-
-
-    model = get_step_context().model
-
-
-    log_model_metadata(
-
-
-    # Model name can be omitted if specified in the step or pipeline context
-
-
-    model_name="iris_classifier",
-
-
-    # Passing None or omitting this will use the `latest` version
-
-
-    version=None,
-
-
-    # Metadata should be a dictionary of JSON-serializable values
-
-
-    metadata={"accuracy": float(accuracy)}
-
-
-    # A dictionary of dictionaries can also be passed to group metadata
-
-
-    #  in the dashboard
-
-
-    # metadata = {"metrics": {"accuracy": accuracy}}
-
-
-    from zenml.client import Client
-
-
-    # Get an artifact version (in this the latest `iris_classifier`)
-
-
-    model_version = Client().get_model_version(''iris_classifier'')
-
-
-    # Fetch it''s metadata
-
-
-    model_version.run_metadata["accuracy"].value
-
-
-    The ZenML Pro dashboard offers advanced visualization features for artifact exploration,
-    including a dedicated artifacts tab with metadata visualization:
-
-
-    Choosing log metadata with artifacts or model versions depends on the scope and
-    purpose of the information you wish to capture. Artifact metadata is best for
-    details specific to individual outputs, while model version metadata is suitable
-    for broader information relevant to the overall model. By utilizing ZenML''s metadata
-    logging capabilities and special types, you can enhance the traceability, reproducibility,
-    and analysis of your ML workflows.
-
-
-    Once metadata has been logged to a model, we can retrieve it easily with the client:
-
-
-    from zenml.client import Client
-
-
-    client = Client()
-
-
-    model = client.get_model_version("my_model", "my_version")
-
-
-    print(model.run_metadata["metadata_key"].value)'
-  - 'Hugging Face
-
-
-    Deploying models to Huggingface Inference Endpoints with Hugging Face :hugging_face:.
-
-
-    Hugging Face Inference Endpoints provides a secure production solution to easily
-    deploy any transformers, sentence-transformers, and diffusers models on a dedicated
-    and autoscaling infrastructure managed by Hugging Face. An Inference Endpoint
-    is built from a model from the Hub.
-
-
-    This service provides dedicated and autoscaling infrastructure managed by Hugging
-    Face, allowing you to deploy models without dealing with containers and GPUs.
-
-
-    When to use it?
-
-
-    You should use Hugging Face Model Deployer:
-
-
-    if you want to deploy Transformers, Sentence-Transformers, or Diffusion models
-    on dedicated and secure infrastructure.
-
-
-    if you prefer a fully-managed production solution for inference without the need
-    to handle containers and GPUs.
-
-
-    if your goal is to turn your models into production-ready APIs with minimal infrastructure
-    or MLOps involvement
-
-
-    Cost-effectiveness is crucial, and you want to pay only for the raw compute resources
-    you use.
-
-
-    Enterprise security is a priority, and you need to deploy models into secure offline
-    endpoints accessible only via a direct connection to your Virtual Private Cloud
-    (VPCs).
-
-
-    If you are looking for a more easy way to deploy your models locally, you can
-    use the MLflow Model Deployer flavor.
-
-
-    How to deploy it?
-
-
-    The Hugging Face Model Deployer flavor is provided by the Hugging Face ZenML integration,
-    so you need to install it on your local machine to be able to deploy your models.
-    You can do this by running the following command:
-
+    ) -> Tuple[Annotated[int, "int_output"], Annotated[str, "str_output"]]:
 
-    zenml integration install huggingface -y
 
+    result = int(param_1 * (param_2 or 1))
 
-    To register the Hugging Face model deployer with ZenML you need to run the following
-    command:
 
+    result_uri = get_step_context().get_output_artifact_uri()
 
-    zenml model-deployer register <MODEL_DEPLOYER_NAME> --flavor=huggingface --token=<YOUR_HF_TOKEN>
-    --namespace=<YOUR_HF_NAMESPACE>
 
+    return result, result_uri
 
-    Here,
 
+    # Run the Step separately
 
-    token parameter is the Hugging Face authentication token. It can be managed through
-    Hugging Face settings.'
-- source_sentence: What are the benefits of deploying stack components directly from
-    the ZenML CLI?
-  sentences:
-  - 'cess this particular Kubernetes cluster in AWS ?":zenml service-connector verify
-    aws-multi-type --resource-type s3-bucket
 
+    my_step()
 
-    Example Command Output
+
+    # Define a Pipeline
 
 
-    Service connector ''aws-multi-type'' is correctly configured with valid credentials
-    and has access to the following resources:
+    @pipeline'
+  - How do I integrate Google Cloud VertexAI into my existing Kubernetes cluster?
+  - ' SDK Docs .
 
 
-    ┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+    Enabling CUDA for GPU-backed hardwareNote that if you wish to use this step operator
+    to run steps on a GPU, you will need to follow the instructions on this page to
+    ensure that it works. It requires adding some extra settings customization and
+    is essential to enable CUDA for the GPU to give its full acceleration.
 
 
-    ┃ RESOURCE TYPE │ RESOURCE NAMES                        ┃
+    PreviousStep Operators
 
 
-    ┠───────────────┼───────────────────────────────────────┨
+    NextGoogle Cloud VertexAI
 
 
-    ┃ 📦 s3-bucket  │ s3://aws-ia-mwaa-715803424590         ┃
+    Last updated 19 days ago'
+- source_sentence: What are the special metadata types supported by ZenML and how
+    are they used?
+  sentences:
+  - 'Special Metadata Types
 
 
-    ┃               │ s3://zenfiles                         ┃
+    Tracking your metadata.
 
 
-    ┃               │ s3://zenml-demos                      ┃
+    ZenML supports several special metadata types to capture specific kinds of information.
+    Here are examples of how to use the special types Uri, Path, DType, and StorageSize:
 
 
-    ┃               │ s3://zenml-generative-chat            ┃
+    from zenml.metadata.metadata_types import StorageSize, DType
 
 
-    ┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
+    from zenml import log_artifact_metadata
 
 
-    zenml service-connector verify aws-multi-type --resource-type kubernetes-cluster
-    --resource-id zenhacks-cluster
+    log_artifact_metadata(
 
 
-    Example Command Output
+    metadata={
 
 
-    Service connector ''aws-multi-type'' is correctly configured with valid credentials
-    and has access to the following resources:
+    "dataset_source": Uri("gs://my-bucket/datasets/source.csv"),
 
 
-    ┏━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━┓
+    "preprocessing_script": Path("/scripts/preprocess.py"),
 
 
-    ┃     RESOURCE TYPE     │ RESOURCE NAMES   ┃
+    "column_types": {
 
 
-    ┠───────────────────────┼──────────────────┨
+    "age": DType("int"),
 
 
-    ┃ 🌀 kubernetes-cluster │ zenhacks-cluster ┃
+    "income": DType("float"),
 
 
-    ┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┛
+    "score": DType("int")
 
 
-    Verifying the multi-instance Service Connector displays all the resources that
-    it can access. We can also scope the verification to a single resource:
+    },
 
 
-    zenml service-connector verify aws-s3-multi-instance
+    "processed_data_size": StorageSize(2500000)
 
 
-    Example Command Output
+    In this example:
 
 
-    Service connector ''aws-s3-multi-instance'' is correctly configured with valid
-    credentials and has access to the following resources:
+    Uri is used to indicate a dataset source URI.
 
 
-    ┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+    Path is used to specify the filesystem path to a preprocessing script.
 
 
-    ┃ RESOURCE TYPE │ RESOURCE NAMES                        ┃
+    DType is used to describe the data types of specific columns.
 
 
-    ┠───────────────┼───────────────────────────────────────┨
+    StorageSize is used to indicate the size of the processed data in bytes.
 
 
-    ┃ 📦 s3-bucket  │ s3://aws-ia-mwaa-715803424590         ┃
+    These special types help standardize the format of metadata and ensure that it
+    is logged in a consistent and interpretable manner.
 
 
-    ┃               │ s3://zenfiles                         ┃
+    PreviousGroup metadata
 
 
-    ┃               │ s3://zenml-demos                      ┃
+    NextFetch metadata within steps
 
 
-    ┃               │ s3://zenml-generative-chat            ┃'
-  - ' for GCS, Docker and Kubernetes Python clients. Italso allows for the configuration
-    of local Docker and Kubernetes CLIs.
+    Last updated 19 days ago'
+  - 'Configure a code repository
 
 
-    The GCP Service Connector is part of the GCP ZenML integration. You can either
+    Connect a Git repository to ZenML to track code changes and collaborate on MLOps
+    projects.
 
 
-    install the entire integration or use a pypi extra to install it independently
+    Throughout the lifecycle of a MLOps pipeline, it can get quite tiresome to always
+    wait for a Docker build every time after running a pipeline (even if the local
+    Docker cache is used). However, there is a way to just have one pipeline build
+    and keep reusing it until a change to the pipeline environment is made: by connecting
+    a code repository.
 
 
-    of the integration:
+    With ZenML, connecting to a Git repository optimizes the Docker build processes.
+    It also has the added bonus of being a better way of managing repository changes
+    and enabling better code collaboration. Here is how the flow changes when running
+    a pipeline:
 
 
-    pip install "zenml[connectors-gcp]" installs only prerequisites for the GCP
+    You trigger a pipeline run on your local machine. ZenML parses the @pipeline function
+    to determine the necessary steps.
 
 
-    Service Connector Type
+    The local client requests stack information from the ZenML server, which responds
+    with the cloud stack configuration.
 
 
-    zenml integration install gcp installs the entire GCP ZenML integration
+    The local client detects that we''re using a code repository and requests the
+    information from the git repo.
 
 
-    It is not required to install and set up the GCP CLI on your local machine to
+    Instead of building a new Docker image, the client checks if an existing image
+    can be reused based on the current Git commit hash and other environment metadata.
 
 
-    use the GCP Service Connector to link Stack Components to GCP resources and
+    The client initiates a run in the orchestrator, which sets up the execution environment
+    in the cloud, such as a VM.
 
 
-    services. However, it is recommended to do so if you are looking for a quick
+    The orchestrator downloads the code directly from the Git repository and uses
+    the existing Docker image to run the pipeline steps.
 
 
-    setup that includes using the auto-configuration Service Connector features.
+    Pipeline steps execute, storing artifacts in the cloud-based artifact store.
 
 
-    ──────────────────────────────────────────────────────────────────────────────────
+    Throughout the execution, the pipeline run status and metadata are reported back
+    to the ZenML server.
 
 
-    Fetching details about the GCP kubernetes-cluster resource type (i.e. the GKE
-    cluster):
+    By connecting a Git repository, you avoid redundant builds and make your MLOps
+    processes more efficient. Your team can work on the codebase simultaneously, with
+    ZenML handling the version tracking and ensuring that the correct code version
+    is always used for each run.
 
 
-    zenml service-connector describe-type gcp --resource-type kubernetes-cluster
+    Creating a GitHub Repository'
+  - Can you explain the process of setting up a virtual environment in Python?
+- source_sentence: What are the benefits of deploying stack components directly from
+    the ZenML CLI?
+  sentences:
+  - '─────────────────────────────────────────────────┨┃ RESOURCE TYPES   │ 🔵 gcp-generic,
+    📦 gcs-bucket, 🌀 kubernetes-cluster, 🐳 docker-registry ┃
 
 
-    Example Command Output
+    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
 
 
-    ╔══════════════════════════════════════════════════════════════════════════════╗
+    ┃ RESOURCE NAME    │ <multiple>                                                               ┃
 
 
-    ║      🌀 GCP GKE Kubernetes cluster (resource type: kubernetes-cluster)       ║
+    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
 
 
-    ╚══════════════════════════════════════════════════════════════════════════════╝
+    ┃ SECRET ID        │ 4694de65-997b-4929-8831-b49d5e067b97                                     ┃
 
 
-    Authentication methods: implicit, user-account, service-account, oauth2-token,
+    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
 
 
-    impersonation
+    ┃ SESSION DURATION │ N/A                                                                      ┃
 
 
-    Supports resource instances: True
+    ┠──────────────────┼────────────────────────────────────────────────────────────────���─────────┨
 
 
-    Authentication methods:
+    ┃ EXPIRES IN       │ 59m46s                                                                   ┃
 
 
-    🔒 implicit
+    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
 
 
-    🔒 user-account
+    ┃ OWNER            │ default                                                                  ┃
 
 
-    🔒 service-account
+    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
 
 
-    🔒 oauth2-token
+    ┃ WORKSPACE        │ default                                                                  ┃
 
 
-    🔒 impersonation
+    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
 
 
-    Allows Stack Components to access a GKE registry as a standard Kubernetes
+    ┃ SHARED           │ ➖                                                                       ┃
 
 
-    cluster resource. When used by Stack Components, they are provided a
+    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
 
 
-    pre-authenticated Python Kubernetes client instance.
+    ┃ CREATED_AT       │ 2023-05-19 09:04:33.557126                                               ┃
 
 
-    The configured credentials must have at least the following GCP permissions
+    ┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
 
 
-    associated with the GKE clusters that it can access:
+    ┃ UPDATED_AT       │ 2023-05-19 09:04:33.557127                                               ┃
 
 
-    container.clusters.list
+    ┗━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
 
 
-    container.clusters.get
+    Configuration
 
 
-    In addition to the above permissions, the credentials should include permissions'
+    ┏━━━━━━━━━━━━┯━━━━━━━━━━━━┓'
+  - How do you set up a custom service account for Vertex AI?
   - '⚒️Manage stacks
 
 
@@ -1009,162 +636,78 @@ widget:
 
     The GCP Service Connector allows auto-discovering and fetching credentials and
     configuration set up by the GCP CLI on your local host.'
-  - 'strator    │ eks_seldon     │ aws_secret_manager ┃┠────────┼──────────────────────┼──────────────────────────────────────┼────────┼─────────┼────────────────────┼──────────────────────┼───────────────────────┼────────────────┼────────────────────┨
-
-
-    ┃   👉   │ default              │ fe913bb5-e631-4d4e-8c1b-936518190ebb │        │
-    default │                    │ default              │ default               │                │                    ┃
-
-
-    ┗━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━┷━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━┛
-
-
-    Example of migrating a profile into the default project using a name prefix:
-
-
-    $ zenml profile migrate /home/stefan/.config/zenml/profiles/zenbytes --prefix
-    zenbytes_
-
-
-    No component flavors to migrate from /home/stefan/.config/zenml/profiles/zenbytes/stacks.yaml...
-
-
-    Migrating stack components from /home/stefan/.config/zenml/profiles/zenbytes/stacks.yaml...
-
-
-    Created artifact_store ''zenbytes_s3_store'' with flavor ''s3''.
-
-
-    Created container_registry ''zenbytes_ecr_registry'' with flavor ''default''.
-
-
-    Created experiment_tracker ''zenbytes_mlflow_tracker'' with flavor ''mlflow''.
-
-
-    Created experiment_tracker ''zenbytes_mlflow_tracker_local'' with flavor ''mlflow''.
-
-
-    Created model_deployer ''zenbytes_eks_seldon'' with flavor ''seldon''.
-
-
-    Created model_deployer ''zenbytes_mlflow'' with flavor ''mlflow''.
-
-
-    Created orchestrator ''zenbytes_eks_orchestrator'' with flavor ''kubeflow''.
-
-
-    Created secrets_manager ''zenbytes_aws_secret_manager'' with flavor ''aws''.
-
-
-    Migrating stacks from /home/stefan/.config/zenml/profiles/zenbytes/stacks.yaml...
-
-
-    Created stack ''zenbytes_aws_kubeflow_stack''.
-
-
-    Created stack ''zenbytes_local_with_mlflow''.
-
-
-    $ zenml stack list
-
-
-    Using the default local database.
-
-
-    Running with active project: ''default'' (global)'
-  - 'Evaluation in 65 lines of code
-
-
-    Learn how to implement evaluation for RAG in just 65 lines of code.
-
-
-    Our RAG guide included a short example for how to implement a basic RAG pipeline
-    in just 85 lines of code. In this section, we''ll build on that example to show
-    how you can evaluate the performance of your RAG pipeline in just 65 lines. For
-    the full code, please visit the project repository here. The code that follows
-    requires the functions from the earlier RAG pipeline code to work.
-
-
-    # ...previous RAG pipeline code here...
-
-
-    # see https://github.com/zenml-io/zenml-projects/blob/main/llm-complete-guide/most_basic_rag_pipeline.py
-
-
-    eval_data = [
-
-
-    "question": "What creatures inhabit the luminescent forests of ZenML World?",
-
-
-    "expected_answer": "The luminescent forests of ZenML World are inhabited by glowing
-    Zenbots.",
-
-
-    },
-
-
-    "question": "What do Fractal Fungi do in the melodic caverns of ZenML World?",
+  - 'Hugging Face
 
 
-    "expected_answer": "Fractal Fungi emit pulsating tones that resonate through the
-    crystalline structures, creating a symphony of otherworldly sounds in the melodic
-    caverns of ZenML World.",
+    Deploying models to Huggingface Inference Endpoints with Hugging Face :hugging_face:.
 
 
-    },
+    Hugging Face Inference Endpoints provides a secure production solution to easily
+    deploy any transformers, sentence-transformers, and diffusers models on a dedicated
+    and autoscaling infrastructure managed by Hugging Face. An Inference Endpoint
+    is built from a model from the Hub.
 
 
-    "question": "Where do Gravitational Geckos live in ZenML World?",
+    This service provides dedicated and autoscaling infrastructure managed by Hugging
+    Face, allowing you to deploy models without dealing with containers and GPUs.
 
 
-    "expected_answer": "Gravitational Geckos traverse the inverted cliffs of ZenML
-    World.",
+    When to use it?
 
 
-    },
+    You should use Hugging Face Model Deployer:
 
 
-    def evaluate_retrieval(question, expected_answer, corpus, top_n=2):
+    if you want to deploy Transformers, Sentence-Transformers, or Diffusion models
+    on dedicated and secure infrastructure.
 
 
-    relevant_chunks = retrieve_relevant_chunks(question, corpus, top_n)
+    if you prefer a fully-managed production solution for inference without the need
+    to handle containers and GPUs.
 
 
-    score = any(
+    if your goal is to turn your models into production-ready APIs with minimal infrastructure
+    or MLOps involvement
 
 
-    any(word in chunk for word in tokenize(expected_answer))
+    Cost-effectiveness is crucial, and you want to pay only for the raw compute resources
+    you use.
 
 
-    for chunk in relevant_chunks
+    Enterprise security is a priority, and you need to deploy models into secure offline
+    endpoints accessible only via a direct connection to your Virtual Private Cloud
+    (VPCs).
 
 
-    return score
+    If you are looking for a more easy way to deploy your models locally, you can
+    use the MLflow Model Deployer flavor.
 
 
-    def evaluate_generation(question, expected_answer, generated_answer):
+    How to deploy it?
 
 
-    client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+    The Hugging Face Model Deployer flavor is provided by the Hugging Face ZenML integration,
+    so you need to install it on your local machine to be able to deploy your models.
+    You can do this by running the following command:
 
 
-    chat_completion = client.chat.completions.create(
+    zenml integration install huggingface -y
 
 
-    messages=[
+    To register the Hugging Face model deployer with ZenML you need to run the following
+    command:
 
 
-    "role": "system",
+    zenml model-deployer register <MODEL_DEPLOYER_NAME> --flavor=huggingface --token=<YOUR_HF_TOKEN>
+    --namespace=<YOUR_HF_NAMESPACE>
 
 
-    "content": "You are an evaluation judge. Given a question, an expected answer,
-    and a generated answer, your task is to determine if the generated answer is relevant
-    and accurate. Respond with ''YES'' if the generated answer is satisfactory, or
-    ''NO'' if it is not.",
+    Here,
 
 
-    },'
+    token parameter is the Hugging Face authentication token. It can be managed through
+    Hugging Face settings.'
+  - Can you list the steps to set up a Docker registry on a Kubernetes cluster?
 model-index:
 - name: zenml/finetuned-snowflake-arctic-embed-m
   results:
@@ -1176,49 +719,49 @@ model-index:
       type: dim_384
     metrics:
     - type: cosine_accuracy@1
-      value: 0.3493975903614458
+      value: 0.29518072289156627
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.572289156626506
+      value: 0.5240963855421686
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.6445783132530121
+      value: 0.5843373493975904
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.7530120481927711
+      value: 0.6867469879518072
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.3493975903614458
+      value: 0.29518072289156627
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.1907630522088353
+      value: 0.17469879518072293
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.1289156626506024
+      value: 0.11686746987951804
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.07530120481927709
+      value: 0.0686746987951807
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.3493975903614458
+      value: 0.29518072289156627
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.572289156626506
+      value: 0.5240963855421686
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.6445783132530121
+      value: 0.5843373493975904
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.7530120481927711
+      value: 0.6867469879518072
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.5491448856982105
+      value: 0.4908042072911187
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.48445926563396463
+      value: 0.42844234079173843
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.4935434037345097
+      value: 0.43576329240226386
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -1228,49 +771,49 @@ model-index:
       type: dim_256
     metrics:
     - type: cosine_accuracy@1
-      value: 0.3253012048192771
+      value: 0.25903614457831325
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.5783132530120482
+      value: 0.5060240963855421
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.6445783132530121
+      value: 0.5783132530120482
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.7469879518072289
+      value: 0.6445783132530121
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.3253012048192771
+      value: 0.25903614457831325
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.19277108433734935
+      value: 0.1686746987951807
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.1289156626506024
+      value: 0.11566265060240961
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.07469879518072287
+      value: 0.0644578313253012
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.3253012048192771
+      value: 0.25903614457831325
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.5783132530120482
+      value: 0.5060240963855421
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.6445783132530121
+      value: 0.5783132530120482
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.7469879518072289
+      value: 0.6445783132530121
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.5389489067913165
+      value: 0.4548319777111225
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.4727792120864409
+      value: 0.39346194301013593
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.4829032647176661
+      value: 0.40343211538391555
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -1280,49 +823,49 @@ model-index:
       type: dim_128
     metrics:
     - type: cosine_accuracy@1
-      value: 0.29518072289156627
+      value: 0.2710843373493976
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.5421686746987951
+      value: 0.46987951807228917
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.6325301204819277
+      value: 0.5662650602409639
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.7289156626506024
+      value: 0.6144578313253012
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.29518072289156627
+      value: 0.2710843373493976
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.180722891566265
+      value: 0.1566265060240964
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.12650602409638553
+      value: 0.11325301204819276
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.07289156626506023
+      value: 0.061445783132530116
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.29518072289156627
+      value: 0.2710843373493976
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.5421686746987951
+      value: 0.46987951807228917
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.6325301204819277
+      value: 0.5662650602409639
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.7289156626506024
+      value: 0.6144578313253012
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.5118201113899791
+      value: 0.44433019669319024
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.4423407917383822
+      value: 0.3893574297188756
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.45200042096222626
+      value: 0.3989315479842741
       name: Cosine Map@100
   - task:
       type: information-retrieval
@@ -1332,49 +875,49 @@ model-index:
       type: dim_64
     metrics:
     - type: cosine_accuracy@1
-      value: 0.26506024096385544
+      value: 0.21686746987951808
       name: Cosine Accuracy@1
     - type: cosine_accuracy@3
-      value: 0.5240963855421686
+      value: 0.42168674698795183
       name: Cosine Accuracy@3
     - type: cosine_accuracy@5
-      value: 0.5843373493975904
+      value: 0.5180722891566265
       name: Cosine Accuracy@5
     - type: cosine_accuracy@10
-      value: 0.7048192771084337
+      value: 0.5843373493975904
       name: Cosine Accuracy@10
     - type: cosine_precision@1
-      value: 0.26506024096385544
+      value: 0.21686746987951808
       name: Cosine Precision@1
     - type: cosine_precision@3
-      value: 0.17469879518072287
+      value: 0.14056224899598396
       name: Cosine Precision@3
     - type: cosine_precision@5
-      value: 0.11686746987951806
+      value: 0.10361445783132528
       name: Cosine Precision@5
     - type: cosine_precision@10
-      value: 0.07048192771084334
+      value: 0.05843373493975902
       name: Cosine Precision@10
     - type: cosine_recall@1
-      value: 0.26506024096385544
+      value: 0.21686746987951808
       name: Cosine Recall@1
     - type: cosine_recall@3
-      value: 0.5240963855421686
+      value: 0.42168674698795183
       name: Cosine Recall@3
     - type: cosine_recall@5
-      value: 0.5843373493975904
+      value: 0.5180722891566265
       name: Cosine Recall@5
     - type: cosine_recall@10
-      value: 0.7048192771084337
+      value: 0.5843373493975904
       name: Cosine Recall@10
     - type: cosine_ndcg@10
-      value: 0.4807789244303294
+      value: 0.39639025659520544
       name: Cosine Ndcg@10
     - type: cosine_mrr@10
-      value: 0.409741346337732
+      value: 0.3364529546758464
       name: Cosine Mrr@10
     - type: cosine_map@100
-      value: 0.41754560205770985
+      value: 0.34658882510541217
       name: Cosine Map@100
 ---
 
@@ -1430,7 +973,7 @@ model = SentenceTransformer("zenml/finetuned-snowflake-arctic-embed-m")
 sentences = [
     'What is the expiration time for the GCP OAuth2 token in the ZenML configuration?',
     '━━━━━┛\n\nConfiguration\n\n┏━━━━━━━━━━━━┯━━━━━━━━━━━━┓┃ PROPERTY   │ VALUE      ┃\n\n┠────────────┼────────────┨\n\n┃ project_id │ zenml-core ┃\n\n┠────────────┼────────────┨\n\n┃ token      │ [HIDDEN]   ┃\n\n┗━━━━━━━━━━━━┷━━━━━━━━━━━━┛\n\nNote the temporary nature of the Service Connector. It will expire and become unusable in 1 hour:\n\nzenml service-connector list --name gcp-oauth2-token\n\nExample Command Output\n\n┏━━━━━━━━┯━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┯━━━━━━━━┯━━━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━┓\n\n┃ ACTIVE │ NAME             │ ID                                   │ TYPE   │ RESOURCE TYPES        │ RESOURCE NAME │ SHARED │ OWNER   │ EXPIRES IN │ LABELS ┃\n\n┠────────┼──────────────────┼──────────────────────────────────────┼────────┼───────────────────────┼───────────────┼────────┼─────────┼────────────┼────────┨\n\n┃        │ gcp-oauth2-token │ ec4d7d85-c71c-476b-aa76-95bf772c90da │ 🔵 gcp │ 🔵 gcp-generic        │ <multiple>    │ ➖     │ default │ 59m35s     │        ┃\n\n┃        │                  │                                      │        │ 📦 gcs-bucket         │               │        │         │            │        ┃\n\n┃        │                  │                                      │        │ 🌀 kubernetes-cluster │               │        │         │            │        ┃\n\n┃        │                  │                                      │        │ 🐳 docker-registry    │               │        │         │            │        ┃\n\n┗━━━━━━━━┷━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━┷━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━┛\n\nAuto-configuration\n\nThe GCP Service Connector allows auto-discovering and fetching credentials and configuration set up by the GCP CLI on your local host.',
-    'Evaluation in 65 lines of code\n\nLearn how to implement evaluation for RAG in just 65 lines of code.\n\nOur RAG guide included a short example for how to implement a basic RAG pipeline in just 85 lines of code. In this section, we\'ll build on that example to show how you can evaluate the performance of your RAG pipeline in just 65 lines. For the full code, please visit the project repository here. The code that follows requires the functions from the earlier RAG pipeline code to work.\n\n# ...previous RAG pipeline code here...\n\n# see https://github.com/zenml-io/zenml-projects/blob/main/llm-complete-guide/most_basic_rag_pipeline.py\n\neval_data = [\n\n"question": "What creatures inhabit the luminescent forests of ZenML World?",\n\n"expected_answer": "The luminescent forests of ZenML World are inhabited by glowing Zenbots.",\n\n},\n\n"question": "What do Fractal Fungi do in the melodic caverns of ZenML World?",\n\n"expected_answer": "Fractal Fungi emit pulsating tones that resonate through the crystalline structures, creating a symphony of otherworldly sounds in the melodic caverns of ZenML World.",\n\n},\n\n"question": "Where do Gravitational Geckos live in ZenML World?",\n\n"expected_answer": "Gravitational Geckos traverse the inverted cliffs of ZenML World.",\n\n},\n\ndef evaluate_retrieval(question, expected_answer, corpus, top_n=2):\n\nrelevant_chunks = retrieve_relevant_chunks(question, corpus, top_n)\n\nscore = any(\n\nany(word in chunk for word in tokenize(expected_answer))\n\nfor chunk in relevant_chunks\n\nreturn score\n\ndef evaluate_generation(question, expected_answer, generated_answer):\n\nclient = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))\n\nchat_completion = client.chat.completions.create(\n\nmessages=[\n\n"role": "system",\n\n"content": "You are an evaluation judge. Given a question, an expected answer, and a generated answer, your task is to determine if the generated answer is relevant and accurate. Respond with \'YES\' if the generated answer is satisfactory, or \'NO\' if it is not.",\n\n},',
+    'Can you list the steps to set up a Docker registry on a Kubernetes cluster?',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -1476,21 +1019,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.3494     |
-| cosine_accuracy@3   | 0.5723     |
-| cosine_accuracy@5   | 0.6446     |
-| cosine_accuracy@10  | 0.753      |
-| cosine_precision@1  | 0.3494     |
-| cosine_precision@3  | 0.1908     |
-| cosine_precision@5  | 0.1289     |
-| cosine_precision@10 | 0.0753     |
-| cosine_recall@1     | 0.3494     |
-| cosine_recall@3     | 0.5723     |
-| cosine_recall@5     | 0.6446     |
-| cosine_recall@10    | 0.753      |
-| cosine_ndcg@10      | 0.5491     |
-| cosine_mrr@10       | 0.4845     |
-| **cosine_map@100**  | **0.4935** |
+| cosine_accuracy@1   | 0.2952     |
+| cosine_accuracy@3   | 0.5241     |
+| cosine_accuracy@5   | 0.5843     |
+| cosine_accuracy@10  | 0.6867     |
+| cosine_precision@1  | 0.2952     |
+| cosine_precision@3  | 0.1747     |
+| cosine_precision@5  | 0.1169     |
+| cosine_precision@10 | 0.0687     |
+| cosine_recall@1     | 0.2952     |
+| cosine_recall@3     | 0.5241     |
+| cosine_recall@5     | 0.5843     |
+| cosine_recall@10    | 0.6867     |
+| cosine_ndcg@10      | 0.4908     |
+| cosine_mrr@10       | 0.4284     |
+| **cosine_map@100**  | **0.4358** |
 
 #### Information Retrieval
 * Dataset: `dim_256`
@@ -1498,43 +1041,43 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.3253     |
-| cosine_accuracy@3   | 0.5783     |
-| cosine_accuracy@5   | 0.6446     |
-| cosine_accuracy@10  | 0.747      |
-| cosine_precision@1  | 0.3253     |
-| cosine_precision@3  | 0.1928     |
-| cosine_precision@5  | 0.1289     |
-| cosine_precision@10 | 0.0747     |
-| cosine_recall@1     | 0.3253     |
-| cosine_recall@3     | 0.5783     |
-| cosine_recall@5     | 0.6446     |
-| cosine_recall@10    | 0.747      |
-| cosine_ndcg@10      | 0.5389     |
-| cosine_mrr@10       | 0.4728     |
-| **cosine_map@100**  | **0.4829** |
+| cosine_accuracy@1   | 0.259      |
+| cosine_accuracy@3   | 0.506      |
+| cosine_accuracy@5   | 0.5783     |
+| cosine_accuracy@10  | 0.6446     |
+| cosine_precision@1  | 0.259      |
+| cosine_precision@3  | 0.1687     |
+| cosine_precision@5  | 0.1157     |
+| cosine_precision@10 | 0.0645     |
+| cosine_recall@1     | 0.259      |
+| cosine_recall@3     | 0.506      |
+| cosine_recall@5     | 0.5783     |
+| cosine_recall@10    | 0.6446     |
+| cosine_ndcg@10      | 0.4548     |
+| cosine_mrr@10       | 0.3935     |
+| **cosine_map@100**  | **0.4034** |
 
 #### Information Retrieval
 * Dataset: `dim_128`
 * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
 
-| Metric              | Value     |
-|:--------------------|:----------|
-| cosine_accuracy@1   | 0.2952    |
-| cosine_accuracy@3   | 0.5422    |
-| cosine_accuracy@5   | 0.6325    |
-| cosine_accuracy@10  | 0.7289    |
-| cosine_precision@1  | 0.2952    |
-| cosine_precision@3  | 0.1807    |
-| cosine_precision@5  | 0.1265    |
-| cosine_precision@10 | 0.0729    |
-| cosine_recall@1     | 0.2952    |
-| cosine_recall@3     | 0.5422    |
-| cosine_recall@5     | 0.6325    |
-| cosine_recall@10    | 0.7289    |
-| cosine_ndcg@10      | 0.5118    |
-| cosine_mrr@10       | 0.4423    |
-| **cosine_map@100**  | **0.452** |
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.2711     |
+| cosine_accuracy@3   | 0.4699     |
+| cosine_accuracy@5   | 0.5663     |
+| cosine_accuracy@10  | 0.6145     |
+| cosine_precision@1  | 0.2711     |
+| cosine_precision@3  | 0.1566     |
+| cosine_precision@5  | 0.1133     |
+| cosine_precision@10 | 0.0614     |
+| cosine_recall@1     | 0.2711     |
+| cosine_recall@3     | 0.4699     |
+| cosine_recall@5     | 0.5663     |
+| cosine_recall@10    | 0.6145     |
+| cosine_ndcg@10      | 0.4443     |
+| cosine_mrr@10       | 0.3894     |
+| **cosine_map@100**  | **0.3989** |
 
 #### Information Retrieval
 * Dataset: `dim_64`
@@ -1542,21 +1085,21 @@ You can finetune this model on your own dataset.
 
 | Metric              | Value      |
 |:--------------------|:-----------|
-| cosine_accuracy@1   | 0.2651     |
-| cosine_accuracy@3   | 0.5241     |
-| cosine_accuracy@5   | 0.5843     |
-| cosine_accuracy@10  | 0.7048     |
-| cosine_precision@1  | 0.2651     |
-| cosine_precision@3  | 0.1747     |
-| cosine_precision@5  | 0.1169     |
-| cosine_precision@10 | 0.0705     |
-| cosine_recall@1     | 0.2651     |
-| cosine_recall@3     | 0.5241     |
-| cosine_recall@5     | 0.5843     |
-| cosine_recall@10    | 0.7048     |
-| cosine_ndcg@10      | 0.4808     |
-| cosine_mrr@10       | 0.4097     |
-| **cosine_map@100**  | **0.4175** |
+| cosine_accuracy@1   | 0.2169     |
+| cosine_accuracy@3   | 0.4217     |
+| cosine_accuracy@5   | 0.5181     |
+| cosine_accuracy@10  | 0.5843     |
+| cosine_precision@1  | 0.2169     |
+| cosine_precision@3  | 0.1406     |
+| cosine_precision@5  | 0.1036     |
+| cosine_precision@10 | 0.0584     |
+| cosine_recall@1     | 0.2169     |
+| cosine_recall@3     | 0.4217     |
+| cosine_recall@5     | 0.5181     |
+| cosine_recall@10    | 0.5843     |
+| cosine_ndcg@10      | 0.3964     |
+| cosine_mrr@10       | 0.3365     |
+| **cosine_map@100**  | **0.3466** |
 
 <!--
 ## Bias, Risks and Limitations
@@ -1578,22 +1121,22 @@ You can finetune this model on your own dataset.
 
 
 * Size: 1,490 training samples
-* Columns: <code>positive</code> and <code>anchor</code>
+* Columns: <code>positive</code>, <code>anchor</code>, and <code>negative</code>
 * Approximate statistics based on the first 1000 samples:
-  |         | positive                                                                          | anchor                                                                               |
-  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
-  | type    | string                                                                            | string                                                                               |
-  | details | <ul><li>min: 9 tokens</li><li>mean: 21.02 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 375.16 tokens</li><li>max: 512 tokens</li></ul> |
+  |         | positive                                                                          | anchor                                                                               | negative                                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               | string                                                                             |
+  | details | <ul><li>min: 9 tokens</li><li>mean: 21.02 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 375.16 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 17.51 tokens</li><li>max: 31 tokens</li></ul> |
 * Samples:
-  | positive                                                                                                                           | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
-  |:-----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | <code>What details can you provide about the mlflow_training_pipeline runs listed in the ZenML documentation?</code>               | <code>mlflow_training_pipeline',                       ┃┃                        │               │                                         │ 'zenml_pipeline_run_uuid': 'a5d4faae-ef70-48f2-9893-6e65d5e51e98', 'zenml_workspace': '10e060b3-2f7e-463d-9ec8-3a211ef4e1f6', 'epochs': '5', 'optimizer': 'Adam', 'lr': '0.005'}     ┃<br><br>┠────────────────────────┼───────────────┼─────────────────────────────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┨<br><br>┃ tensorflow-mnist-model │ 2             │ Run #2 of the mlflow_training_pipeline. │ {'zenml_version': '0.34.0', 'zenml_run_name': 'mlflow_training_pipeline-2023_03_01-08_09_08_467212', 'zenml_pipeline_name': 'mlflow_training_pipeline',                       ┃<br><br>┃                        │               │                                         │ 'zenml_pipeline_run_uuid': '11858dcf-3e47-4b1a-82c5-6fa25ba4e037', 'zenml_workspace': '10e060b3-2f7e-463d-9ec8-3a211ef4e1f6', 'epochs': '5', 'optimizer': 'Adam', 'lr': '0.003'}     ┃<br><br>┠────────────────────────┼───────────────┼─────────────────────────────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┨<br><br>┃ tensorflow-mnist-model │ 1             │ Run #1 of the mlflow_training_pipeline. │ {'zenml_version': '0.34.0', 'zenml_run_name': 'mlflow_training_pipeline-2023_03_01-08_08_52_398499', 'zenml_pipeline_name': 'mlflow_training_pipeline',                       ┃<br><br>┃                        │               │                                         │ 'zenml_pipeline_run_uuid': '29fb22c1-6e0b-4431-9e04-226226506d16', 'zenml_workspace': '10e060b3-2f7e-463d-9ec8-3a211ef4e1f6', 'epochs': '5', 'optimizer': 'Adam', 'lr': '0.001'}     ┃</code>                                                                   |
-  | <code>How do you register a GCP Service Connector that uses account impersonation to access the zenml-bucket-sl GCS bucket?</code> | <code>esource-id zenml-bucket-sl<br><br>Example Command OutputError: Service connector 'gcp-empty-sa' verification failed: connector authorization failure: failed to fetch GCS bucket<br><br>zenml-bucket-sl: 403 GET https://storage.googleapis.com/storage/v1/b/zenml-bucket-sl?projection=noAcl&prettyPrint=false:<br><br>[email protected] does not have storage.buckets.get access to the Google Cloud Storage bucket.<br><br>Permission 'storage.buckets.get' denied on resource (or it may not exist).<br><br>Next, we'll register a GCP Service Connector that actually uses account impersonation to access the zenml-bucket-sl GCS bucket and verify that it can actually access the bucket:<br><br>zenml service-connector register gcp-impersonate-sa --type gcp --auth-method impersonation --service_account_json=@[email protected]  --project_id=zenml-core --target_principal=zenml-bucket-sl@zenml-core.iam.gserviceaccount.com --resource-type gcs-bucket --resource-id gs://zenml-bucket-sl<br><br>Example Command Output<br><br>Expanding argument value service_account_json to contents of file /home/stefan/aspyre/src/zenml/[email protected].<br><br>Successfully registered service connector `gcp-impersonate-sa` with access to the following resources:<br><br>┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━┓<br><br>┃ RESOURCE TYPE │ RESOURCE NAMES       ┃<br><br>┠───────────────┼──────────────────────┨<br><br>┃ 📦 gcs-bucket │ gs://zenml-bucket-sl ┃<br><br>┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━┛<br><br>External Account (GCP Workload Identity)<br><br>Use GCP workload identity federation to authenticate to GCP services using AWS IAM credentials, Azure Active Directory credentials or generic OIDC tokens.</code>                                                                                                                                                                                                                                                                                   |
-  | <code>Can you explain how data validation helps in detecting data drift and model drift in ZenML pipelines?</code>                 | <code>of your models at different stages of development.if you have pipelines that regularly ingest new data, you should use data validation to run regular data integrity checks to signal problems before they are propagated downstream.<br><br>in continuous training pipelines, you should use data validation techniques to compare new training data against a data reference and to compare the performance of newly trained models against previous ones.<br><br>when you have pipelines that automate batch inference or if you regularly collect data used as input in online inference, you should use data validation to run data drift analyses and detect training-serving skew, data drift and model drift.<br><br>Data Validator Flavors<br><br>Data Validator are optional stack components provided by integrations. The following table lists the currently available Data Validators and summarizes their features and the data types and model types that they can be used with in ZenML pipelines:<br><br>Data Validator Validation Features Data Types Model Types Notes Flavor/Integration Deepchecks data quality<br>data drift<br>model drift<br>model performance tabular: pandas.DataFrame CV: torch.utils.data.dataloader.DataLoader tabular: sklearn.base.ClassifierMixin CV: torch.nn.Module Add Deepchecks data and model validation tests to your pipelines deepchecks Evidently data quality<br>data drift<br>model drift<br>model performance tabular: pandas.DataFrame N/A Use Evidently to generate a variety of data quality and data/model drift reports and visualizations evidently Great Expectations data profiling<br>data quality tabular: pandas.DataFrame N/A Perform data testing, documentation and profiling with Great Expectations great_expectations Whylogs/WhyLabs data drift tabular: pandas.DataFrame N/A Generate data profiles with whylogs and upload them to WhyLabs whylogs<br><br>If you would like to see the available flavors of Data Validator, you can use the command:<br><br>zenml data-validator flavor list<br><br>How to use it</code> |
+  | positive                                                                                                                           | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | negative                                                                                             |
+  |:-----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------|
+  | <code>What details can you provide about the mlflow_training_pipeline runs listed in the ZenML documentation?</code>               | <code>mlflow_training_pipeline',                       ┃┃                        │               │                                         │ 'zenml_pipeline_run_uuid': 'a5d4faae-ef70-48f2-9893-6e65d5e51e98', 'zenml_workspace': '10e060b3-2f7e-463d-9ec8-3a211ef4e1f6', 'epochs': '5', 'optimizer': 'Adam', 'lr': '0.005'}     ┃<br><br>┠────────────────────────┼───────────────┼─────────────────────────────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┨<br><br>┃ tensorflow-mnist-model │ 2             │ Run #2 of the mlflow_training_pipeline. │ {'zenml_version': '0.34.0', 'zenml_run_name': 'mlflow_training_pipeline-2023_03_01-08_09_08_467212', 'zenml_pipeline_name': 'mlflow_training_pipeline',                       ┃<br><br>┃                        │               │                                         │ 'zenml_pipeline_run_uuid': '11858dcf-3e47-4b1a-82c5-6fa25ba4e037', 'zenml_workspace': '10e060b3-2f7e-463d-9ec8-3a211ef4e1f6', 'epochs': '5', 'optimizer': 'Adam', 'lr': '0.003'}     ┃<br><br>┠────────────────────────┼───────────────┼───────────────────────────────────────��─┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┨<br><br>┃ tensorflow-mnist-model │ 1             │ Run #1 of the mlflow_training_pipeline. │ {'zenml_version': '0.34.0', 'zenml_run_name': 'mlflow_training_pipeline-2023_03_01-08_08_52_398499', 'zenml_pipeline_name': 'mlflow_training_pipeline',                       ┃<br><br>┃                        │               │                                         │ 'zenml_pipeline_run_uuid': '29fb22c1-6e0b-4431-9e04-226226506d16', 'zenml_workspace': '10e060b3-2f7e-463d-9ec8-3a211ef4e1f6', 'epochs': '5', 'optimizer': 'Adam', 'lr': '0.001'}     ┃</code>                                                                   | <code>Can you explain how to configure the TensorFlow settings for a different project?</code>       |
+  | <code>How do you register a GCP Service Connector that uses account impersonation to access the zenml-bucket-sl GCS bucket?</code> | <code>esource-id zenml-bucket-sl<br><br>Example Command OutputError: Service connector 'gcp-empty-sa' verification failed: connector authorization failure: failed to fetch GCS bucket<br><br>zenml-bucket-sl: 403 GET https://storage.googleapis.com/storage/v1/b/zenml-bucket-sl?projection=noAcl&prettyPrint=false:<br><br>[email protected] does not have storage.buckets.get access to the Google Cloud Storage bucket.<br><br>Permission 'storage.buckets.get' denied on resource (or it may not exist).<br><br>Next, we'll register a GCP Service Connector that actually uses account impersonation to access the zenml-bucket-sl GCS bucket and verify that it can actually access the bucket:<br><br>zenml service-connector register gcp-impersonate-sa --type gcp --auth-method impersonation --service_account_json=@[email protected]  --project_id=zenml-core --target_principal=zenml-bucket-sl@zenml-core.iam.gserviceaccount.com --resource-type gcs-bucket --resource-id gs://zenml-bucket-sl<br><br>Example Command Output<br><br>Expanding argument value service_account_json to contents of file /home/stefan/aspyre/src/zenml/[email protected].<br><br>Successfully registered service connector `gcp-impersonate-sa` with access to the following resources:<br><br>┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━┓<br><br>┃ RESOURCE TYPE │ RESOURCE NAMES       ┃<br><br>┠───────────────┼──────────────────────┨<br><br>┃ 📦 gcs-bucket │ gs://zenml-bucket-sl ┃<br><br>┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━┛<br><br>External Account (GCP Workload Identity)<br><br>Use GCP workload identity federation to authenticate to GCP services using AWS IAM credentials, Azure Active Directory credentials or generic OIDC tokens.</code>                                                                                                                                                                                                                                                                                   | <code>What is the process for setting up a ZenML pipeline using AWS IAM credentials?</code>          |
+  | <code>Can you explain how data validation helps in detecting data drift and model drift in ZenML pipelines?</code>                 | <code>of your models at different stages of development.if you have pipelines that regularly ingest new data, you should use data validation to run regular data integrity checks to signal problems before they are propagated downstream.<br><br>in continuous training pipelines, you should use data validation techniques to compare new training data against a data reference and to compare the performance of newly trained models against previous ones.<br><br>when you have pipelines that automate batch inference or if you regularly collect data used as input in online inference, you should use data validation to run data drift analyses and detect training-serving skew, data drift and model drift.<br><br>Data Validator Flavors<br><br>Data Validator are optional stack components provided by integrations. The following table lists the currently available Data Validators and summarizes their features and the data types and model types that they can be used with in ZenML pipelines:<br><br>Data Validator Validation Features Data Types Model Types Notes Flavor/Integration Deepchecks data quality<br>data drift<br>model drift<br>model performance tabular: pandas.DataFrame CV: torch.utils.data.dataloader.DataLoader tabular: sklearn.base.ClassifierMixin CV: torch.nn.Module Add Deepchecks data and model validation tests to your pipelines deepchecks Evidently data quality<br>data drift<br>model drift<br>model performance tabular: pandas.DataFrame N/A Use Evidently to generate a variety of data quality and data/model drift reports and visualizations evidently Great Expectations data profiling<br>data quality tabular: pandas.DataFrame N/A Perform data testing, documentation and profiling with Great Expectations great_expectations Whylogs/WhyLabs data drift tabular: pandas.DataFrame N/A Generate data profiles with whylogs and upload them to WhyLabs whylogs<br><br>If you would like to see the available flavors of Data Validator, you can use the command:<br><br>zenml data-validator flavor list<br><br>How to use it</code> | <code>What are the best practices for deploying web applications using Docker and Kubernetes?</code> |
 * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
   ```json
   {
-      "loss": "MultipleNegativesRankingLoss",
+      "loss": "TripletLoss",
       "matryoshka_dims": [
           384,
           256,
@@ -1742,11 +1285,11 @@ You can finetune this model on your own dataset.
 </details>
 
 ### 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     | 0.3884                 | 0.4332                 | 0.4464                 | 0.3140                |
-| 2.0        | 3     | 0.4430                 | 0.4689                 | 0.4962                 | 0.4019                |
-| **2.6667** | **4** | **0.452**              | **0.4829**             | **0.4935**             | **0.4175**            |
+| 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     | 0.3884                 | 0.4332                 | 0.4464                 | 0.3140                |
+| **2.0** | **3** | **0.4064**             | **0.4195**             | **0.4431**             | **0.3553**            |
+| 2.6667  | 4     | 0.3989                 | 0.4034                 | 0.4358                 | 0.3466                |
 
 * The bold row denotes the saved checkpoint.
 
@@ -1788,15 +1331,15 @@ You can finetune this model on your own dataset.
 }
 ```
 
-#### MultipleNegativesRankingLoss
+#### TripletLoss
 ```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},
+@misc{hermans2017defense,
+    title={In Defense of the Triplet Loss for Person Re-Identification}, 
+    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
     year={2017},
-    eprint={1705.00652},
+    eprint={1703.07737},
     archivePrefix={arXiv},
-    primaryClass={cs.CL}
+    primaryClass={cs.CV}
 }
 ```
 

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:5f6c582bbe82af64b23557e72963c469a2155751ff7c68eeedf3125475224864
 size 435588776