Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,1265 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:3284
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: Snowflake/snowflake-arctic-embed-m-v1.5
+widget:
+- source_sentence: Does ZenML officially support Macs running on Apple Silicon, and
+    are there any specific configurations needed?
+  sentences:
+  - 'ding ZenML to learn more!
+
+
+    Do you support Windows?ZenML officially supports Windows if you''re using WSL.
+    Much of ZenML will also work on Windows outside a WSL environment, but we don''t
+    officially support it and some features don''t work (notably anything that requires
+    spinning up a server process).
+
+
+    Do you support Macs running on Apple Silicon?
+
+
+    Yes, ZenML does support Macs running on Apple Silicon. You just need to make sure
+    that you set the following environment variable:
+
+
+    export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES
+
+
+    This is a known issue with how forking works on Macs running on Apple Silicon
+    and it will enable you to use ZenML and the server. This environment variable
+    is needed if you are working with a local server on your Mac, but if you''re just
+    using ZenML as a client / CLI and connecting to a deployed server then you don''t
+    need to set it.
+
+
+    How can I make ZenML work with my custom tool? How can I extend or build on ZenML?
+
+
+    This depends on the tool and its respective MLOps category. We have a full guide
+    on this over here!
+
+
+    How can I contribute?
+
+
+    We develop ZenML together with our community! To get involved, the best way to
+    get started is to select any issue from the good-first-issue label. If you would
+    like to contribute, please review our Contributing Guide for all relevant details.
+
+
+    How can I speak with the community?
+
+
+    The first point of the call should be our Slack group. Ask your questions about
+    bugs or specific use cases and someone from the core team will respond.
+
+
+    Which license does ZenML use?
+
+
+    ZenML is distributed under the terms of the Apache License Version 2.0. A complete
+    version of the license is available in the LICENSE.md in this repository. Any
+    contribution made to this project will be licensed under the Apache License Version
+    2.0.
+
+
+    PreviousCommunity & content
+
+
+    Last updated 3 months ago'
+  - 'Registering a Model
+
+
+    PreviousUse the Model Control PlaneNextDeleting a Model
+
+
+    Last updated 4 months ago'
+  - 'Synthetic data generation
+
+
+    Generate synthetic data with distilabel to finetune embeddings.
+
+
+    PreviousImprove retrieval by finetuning embeddingsNextFinetuning embeddings with
+    Sentence Transformers
+
+
+    Last updated 21 days ago'
+- source_sentence: How can I change the logging verbosity level in ZenML for both
+    local and remote pipeline runs?
+  sentences:
+  - 'ncepts covered in this guide to your own projects.By the end of this guide, you''ll
+    have a solid understanding of how to leverage LLMs in your MLOps workflows using
+    ZenML, enabling you to build powerful, scalable, and maintainable LLM-powered
+    applications. First up, let''s take a look at a super simple implementation of
+    the RAG paradigm to get started.
+
+
+    PreviousAn end-to-end projectNextRAG with ZenML
+
+
+    Last updated 21 days ago'
+  - 'Configuring a pipeline at runtime
+
+
+    Configuring a pipeline at runtime.
+
+
+    PreviousUse pipeline/step parametersNextReference environment variables in configurations
+
+
+    Last updated 28 days ago'
+  - "Set logging verbosity\n\nHow to set the logging verbosity in ZenML.\n\nBy default,\
+    \ ZenML sets the logging verbosity to INFO. If you wish to change this, you can\
+    \ do so by setting the following environment variable:\n\nexport ZENML_LOGGING_VERBOSITY=INFO\n\
+    \nChoose from INFO, WARN, ERROR, CRITICAL, DEBUG. This will set the logs to whichever\
+    \ level you suggest.\n\nNote that setting this on the client environment (e.g.\
+    \ your local machine which runs the pipeline) will not automatically set the same\
+    \ logging verbosity for remote pipeline runs. That means setting this variable\
+    \ locally with only effect pipelines that run locally.\n\nIf you wish to control\
+    \ for remote pipeline runs, you can set the ZENML_LOGGING_VERBOSITY environment\
+    \ variable in your pipeline runs environment as follows:\n\ndocker_settings =\
+    \ DockerSettings(environment={\"ZENML_LOGGING_VERBOSITY\": \"DEBUG\"})\n\n# Either\
+    \ add it to the decorator\n@pipeline(settings={\"docker\": docker_settings})\n\
+    def my_pipeline() -> None:\n    my_step()\n\n# Or configure the pipelines options\n\
+    my_pipeline = my_pipeline.with_options(\n    settings={\"docker\": docker_settings}\n\
+    )\n\nPreviousEnable or disable logs storageNextDisable rich traceback output\n\
+    \nLast updated 21 days ago"
+- source_sentence: How can I autogenerate a template yaml file for my specific pipeline
+    using ZenML?
+  sentences:
+  - "Autogenerate a template yaml file\n\nTo help you figure out what you can put\
+    \ in your configuration file, simply autogenerate a template.\n\nIf you want to\
+    \ generate a template yaml file of your specific pipeline, you can do so by using\
+    \ the .write_run_configuration_template() method. This will generate a yaml file\
+    \ with all options commented out. This way you can pick and choose the settings\
+    \ that are relevant to you.\n\nfrom zenml import pipeline\n...\n\n@pipeline(enable_cache=True)\
+    \ # set cache behavior at step level\ndef simple_ml_pipeline(parameter: int):\n\
+    \    dataset = load_data(parameter=parameter)\n    train_model(dataset)\n\nsimple_ml_pipeline.write_run_configuration_template(path=\"\
+    <Insert_path_here>\")\n\nWhen you want to configure your pipeline with a certain\
+    \ stack in mind, you can do so as well: `...write_run_configuration_template(stack=<Insert_stack_here>)\n\
+    \nPreviousFind out which configuration was used for a runNextCustomize Docker\
+    \ builds\n\nLast updated 21 days ago"
+  - 'Deleting a Model
+
+
+    Learn how to delete models.
+
+
+    PreviousRegistering a ModelNextAssociate a pipeline with a Model
+
+
+    Last updated 4 months ago'
+  - 'Load artifacts into memory
+
+
+    Often ZenML pipeline steps consume artifacts produced by one another directly
+    in the pipeline code, but there are scenarios where you need to pull external
+    data into your steps. Such external data could be artifacts produced by non-ZenML
+    codes. For those cases, it is advised to use ExternalArtifact, but what if we
+    plan to exchange data created with other ZenML pipelines?
+
+
+    ZenML pipelines are first compiled and only executed at some later point. During
+    the compilation phase, all function calls are executed, and this data is fixed
+    as step input parameters. Given all this, the late materialization of dynamic
+    objects, like data artifacts, is crucial. Without late materialization, it would
+    not be possible to pass not-yet-existing artifacts as step inputs, or their metadata,
+    which is often the case in a multi-pipeline setting.
+
+
+    We identify two major use cases for exchanging artifacts between pipelines:
+
+
+    You semantically group your data products using ZenML Models
+
+
+    You prefer to use ZenML Client to bring all the pieces together
+
+
+    We recommend using models to group and access artifacts across pipelines. Find
+    out how to load an artifact from a ZenML Model here.
+
+
+    Use client methods to exchange artifacts
+
+
+    If you don''t yet use the Model Control Plane, you can still exchange data between
+    pipelines with late materialization. Let''s rework the do_predictions pipeline
+    code as follows:
+
+
+    from typing import Annotated
+
+    from zenml import step, pipeline
+
+    from zenml.client import Client
+
+    import pandas as pd
+
+    from sklearn.base import ClassifierMixin'
+- source_sentence: How can I create a Kubernetes cluster on EKS and configure it to
+    run Spark with a custom Docker image?
+  sentences:
+  - 'View logs on the dashboard
+
+
+    PreviousControl loggingNextEnable or disable logs storage
+
+
+    Last updated 21 days ago'
+  - "Datasets in ZenML\n\nModel datasets using simple abstractions.\n\nAs machine\
+    \ learning projects grow in complexity, you often need to work with various data\
+    \ sources and manage intricate data flows. This chapter explores how to use custom\
+    \ Dataset classes and Materializers in ZenML to handle these challenges efficiently.\
+    \ For strategies on scaling your data processing for larger datasets, refer to\
+    \ scaling strategies for big data.\n\nIntroduction to Custom Dataset Classes\n\
+    \nCustom Dataset classes in ZenML provide a way to encapsulate data loading, processing,\
+    \ and saving logic for different data sources. They're particularly useful when:\n\
+    \nWorking with multiple data sources (e.g., CSV files, databases, cloud storage)\n\
+    \nDealing with complex data structures that require special handling\n\nImplementing\
+    \ custom data processing or transformation logic\n\nImplementing Dataset Classes\
+    \ for Different Data Sources\n\nLet's create a base Dataset class and implement\
+    \ it for CSV and BigQuery data sources:\n\nfrom abc import ABC, abstractmethod\n\
+    import pandas as pd\nfrom google.cloud import bigquery\nfrom typing import Optional\n\
+    \nclass Dataset(ABC):\n    @abstractmethod\n    def read_data(self) -> pd.DataFrame:\n\
+    \        pass\n\nclass CSVDataset(Dataset):\n    def __init__(self, data_path:\
+    \ str, df: Optional[pd.DataFrame] = None):\n        self.data_path = data_path\n\
+    \        self.df = df\n\ndef read_data(self) -> pd.DataFrame:\n        if self.df\
+    \ is None:\n            self.df = pd.read_csv(self.data_path)\n        return\
+    \ self.df\n\nclass BigQueryDataset(Dataset):\n    def __init__(\n        self,\n\
+    \        table_id: str,\n        df: Optional[pd.DataFrame] = None,\n        project:\
+    \ Optional[str] = None,\n    ):\n        self.table_id = table_id\n        self.project\
+    \ = project\n        self.df = df\n        self.client = bigquery.Client(project=self.project)\n\
+    \ndef read_data(self) -> pd.DataFrame:\n        query = f\"SELECT * FROM `{self.table_id}`\"\
+    \n        self.df = self.client.query(query).to_dataframe()\n        return self.df"
+  - 'e the correct region is selected on the top right.Click on Add cluster and select
+    Create.
+
+
+    Enter a name and select the cluster role for Cluster service role.
+
+
+    Keep the default values for the networking and logging steps and create the cluster.
+
+
+    Note down the cluster name and the API server endpoint:
+
+
+    EKS_CLUSTER_NAME=<EKS_CLUSTER_NAME>
+
+    EKS_API_SERVER_ENDPOINT=<API_SERVER_ENDPOINT>
+
+
+    After the cluster is created, select it and click on Add node group in the Compute
+    tab.
+
+
+    Enter a name and select the node role.
+
+
+    For the instance type, we recommend t3a.xlarge, as it provides up to 4 vCPUs and
+    16 GB of memory.
+
+
+    Docker image for the Spark drivers and executors
+
+
+    When you want to run your steps on a Kubernetes cluster, Spark will require you
+    to choose a base image for the driver and executor pods. Normally, for this purpose,
+    you can either use one of the base images in Spark’s dockerhub or create an image
+    using the docker-image-tool which will use your own Spark installation and build
+    an image.
+
+
+    When using Spark in EKS, you need to use the latter and utilize the docker-image-tool.
+    However, before the build process, you also need to download the following packages
+
+
+    hadoop-aws = 3.3.1
+
+
+    aws-java-sdk-bundle = 1.12.150
+
+
+    and put them in the jars folder within your Spark installation. Once that is set
+    up, you can build the image as follows:
+
+
+    cd $SPARK_HOME # If this empty for you then you need to set the SPARK_HOME variable
+    which points to your Spark installation
+
+
+    SPARK_IMAGE_TAG=<SPARK_IMAGE_TAG>
+
+
+    ./bin/docker-image-tool.sh -t $SPARK_IMAGE_TAG -p kubernetes/dockerfiles/spark/bindings/python/Dockerfile
+    -u 0 build
+
+
+    BASE_IMAGE_NAME=spark-py:$SPARK_IMAGE_TAG
+
+
+    If you are working on an M1 Mac, you will need to build the image for the amd64
+    architecture, by using the prefix -X on the previous command. For example:
+
+
+    ./bin/docker-image-tool.sh -X -t $SPARK_IMAGE_TAG -p kubernetes/dockerfiles/spark/bindings/python/Dockerfile
+    -u 0 build
+
+
+    Configuring RBAC'
+- source_sentence: How can I configure a pipeline with a YAML file in ZenML?
+  sentences:
+  - 'atically retry steps
+
+
+    Run pipelines asynchronouslyControl execution order of steps
+
+
+    Using a custom step invocation ID
+
+
+    Name your pipeline runs
+
+
+    Use failure/success hooks
+
+
+    Hyperparameter tuning
+
+
+    Access secrets in a step
+
+
+    Run an individual step
+
+
+    Fetching pipelines
+
+
+    Get past pipeline/step runs
+
+
+    🚨Trigger a pipeline
+
+
+    Use templates: Python SDK
+
+
+    Use templates: Dashboard
+
+
+    Use templates: Rest API
+
+
+    📃Use configuration files
+
+
+    How to configure a pipeline with a YAML
+
+
+    What can be configured
+
+
+    Runtime settings for Docker, resources, and stack components
+
+
+    Configuration hierarchy
+
+
+    Find out which configuration was used for a run
+
+
+    Autogenerate a template yaml file
+
+
+    🐳Customize Docker builds
+
+
+    Docker settings on a pipeline
+
+
+    Docker settings on a step
+
+
+    Use a prebuilt image for pipeline execution
+
+
+    Specify pip dependencies and apt packages
+
+
+    Use your own Dockerfiles
+
+
+    Which files are built into the image
+
+
+    How to reuse builds
+
+
+    Define where an image is built
+
+
+    📔Run remote pipelines from notebooks
+
+
+    Limitations of defining steps in notebook cells
+
+
+    Run a single step from a notebook
+
+
+    🤹Manage your ZenML server
+
+
+    Best practices for upgrading ZenML
+
+
+    Upgrade your ZenML server
+
+
+    Using ZenML server in production
+
+
+    Troubleshoot your ZenML server
+
+
+    Migration guide
+
+
+    Migration guide 0.13.2 → 0.20.0
+
+
+    Migration guide 0.23.0 → 0.30.0
+
+
+    Migration guide 0.39.1 → 0.41.0
+
+
+    Migration guide 0.58.2 → 0.60.0
+
+
+    📍Develop locally
+
+
+    Use config files to develop locally
+
+
+    Keep your pipelines and dashboard clean
+
+
+    ⚒️Manage stacks & components
+
+
+    Deploy a cloud stack with ZenML
+
+
+    Deploy a cloud stack with Terraform
+
+
+    Register a cloud stack
+
+
+    Reference secrets in stack configuration
+
+
+    Implement a custom stack component
+
+
+    🚜Train with GPUs
+
+
+    Distributed Training with 🤗 Accelerate
+
+
+    🌲Control logging
+
+
+    View logs on the dashboard
+
+
+    Enable or disable logs storage
+
+
+    Set logging verbosity
+
+
+    Disable rich traceback output
+
+
+    Disable colorful logging
+
+
+    🗄️Handle Data/Artifacts
+
+
+    How ZenML stores data
+
+
+    Return multiple outputs from a step
+
+
+    Delete an artifact
+
+
+    Organize data with tags
+
+
+    Get arbitrary artifacts in a step'
+  - 'Security best practices
+
+
+    Best practices concerning the various authentication methods implemented by Service
+    Connectors.
+
+
+    Service Connector Types, especially those targeted at cloud providers, offer a
+    plethora of authentication methods matching those supported by remote cloud platforms.
+    While there is no single authentication standard that unifies this process, there
+    are some patterns that are easily identifiable and can be used as guidelines when
+    deciding which authentication method to use to configure a Service Connector.
+
+
+    This section explores some of those patterns and gives some advice regarding which
+    authentication methods are best suited for your needs.
+
+
+    This section may require some general knowledge about authentication and authorization
+    to be properly understood. We tried to keep it simple and limit ourselves to talking
+    about high-level concepts, but some areas may get a bit too technical.
+
+
+    Username and password
+
+
+    The key takeaway is this: you should avoid using your primary account password
+    as authentication credentials as much as possible. If there are alternative authentication
+    methods that you can use or other types of credentials (e.g. session tokens, API
+    keys, API tokens), you should always try to use those instead.
+
+
+    Ultimately, if you have no choice, be cognizant of the third parties you share
+    your passwords with. If possible, they should never leave the premises of your
+    local host or development environment.
+
+
+    This is the typical authentication method that uses a username or account name
+    plus the associated password. While this is the de facto method used to log in
+    with web consoles and local CLIs, this is the least secure of all authentication
+    methods and never something you want to share with other members of your team
+    or organization or use to authenticate automated workloads.'
+  - "━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━┛$ zenml orchestrator connect\
+    \ <ORCHESTRATOR_NAME> --connector aws-iam-multi-us\nRunning with active stack:\
+    \ 'default' (repository)\nSuccessfully connected orchestrator `<ORCHESTRATOR_NAME>`\
+    \ to the following resources:\n┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━┓\n\
+    ┃             CONNECTOR ID             │ CONNECTOR NAME   │ CONNECTOR TYPE │ RESOURCE\
+    \ TYPE         │ RESOURCE NAMES   ┃\n┠──────────────────────────────────────┼──────────────────┼────────────────┼───────────────────────┼──────────────────┨\n\
+    ┃ ed528d5a-d6cb-4fc4-bc52-c3d2d01643e5 │ aws-iam-multi-us │ \U0001F536 aws   \
+    \      │ \U0001F300 kubernetes-cluster │ zenhacks-cluster ┃\n┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┛\n\
+    \n# Register and activate a stack with the new orchestrator\n$ zenml stack register\
+    \ <STACK_NAME> -o <ORCHESTRATOR_NAME> ... --set\n\nif you don't have a Service\
+    \ Connector on hand and you don't want to register one , the local Kubernetes\
+    \ kubectl client needs to be configured with a configuration context pointing\
+    \ to the remote cluster. The kubernetes_context stack component must also be configured\
+    \ with the value of that context:\n\nzenml orchestrator register <ORCHESTRATOR_NAME>\
+    \ \\\n    --flavor=kubernetes \\\n    --kubernetes_context=<KUBERNETES_CONTEXT>\n\
+    \n# Register and activate a stack with the new orchestrator\nzenml stack register\
+    \ <STACK_NAME> -o <ORCHESTRATOR_NAME> ... --set\n\nZenML will build a Docker image\
+    \ called <CONTAINER_REGISTRY_URI>/zenml:<PIPELINE_NAME> which includes your code\
+    \ and use it to run your pipeline steps in Kubernetes. Check out this page if\
+    \ you want to learn more about how ZenML builds these images and how you can customize\
+    \ them.\n\nYou can now run any ZenML pipeline using the Kubernetes orchestrator:\n\
+    \npython file_that_runs_a_zenml_pipeline.py"
+datasets: []
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: zenml/finetuned-snowflake-arctic-embed-m-v1.5
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 384
+      type: dim_384
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.1863013698630137
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.4794520547945205
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6602739726027397
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7972602739726027
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.1863013698630137
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.1598173515981735
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.13205479452054794
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07972602739726026
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.1863013698630137
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.4794520547945205
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6602739726027397
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7972602739726027
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.47459290361092754
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.3725994781474232
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.37953809566266083
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.18356164383561643
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.4876712328767123
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6602739726027397
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7917808219178082
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.18356164383561643
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.16255707762557076
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1320547945205479
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07917808219178081
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.18356164383561643
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.4876712328767123
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6602739726027397
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7917808219178082
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.47334554819769054
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.3724179169384647
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.37931260226095775
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.18356164383561643
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.4684931506849315
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6356164383561644
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7780821917808219
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.18356164383561643
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.1561643835616438
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.12712328767123285
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07780821917808219
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.18356164383561643
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.4684931506849315
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6356164383561644
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7780821917808219
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.46219638130094637
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.3628680147858229
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.37047490630037583
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.2054794520547945
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.4767123287671233
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.6273972602739726
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.7534246575342466
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.2054794520547945
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.15890410958904108
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.12547945205479452
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.07534246575342465
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.2054794520547945
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.4767123287671233
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.6273972602739726
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.7534246575342466
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.46250756548591326
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.37069906501413347
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.37874559284369463
+      name: Cosine Map@100
+---
+
+# zenml/finetuned-snowflake-arctic-embed-m-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [Snowflake/snowflake-arctic-embed-m-v1.5](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v1.5) <!-- at revision 3b5a16eaf17e47bd997da998988dce5877a57092 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+- **Language:** en
+- **License:** apache-2.0
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("zenml/finetuned-snowflake-arctic-embed-m-v1.5")
+# Run inference
+sentences = [
+    'How can I configure a pipeline with a YAML file in ZenML?',
+    'atically retry steps\n\nRun pipelines asynchronouslyControl execution order of steps\n\nUsing a custom step invocation ID\n\nName your pipeline runs\n\nUse failure/success hooks\n\nHyperparameter tuning\n\nAccess secrets in a step\n\nRun an individual step\n\nFetching pipelines\n\nGet past pipeline/step runs\n\n🚨Trigger a pipeline\n\nUse templates: Python SDK\n\nUse templates: Dashboard\n\nUse templates: Rest API\n\n📃Use configuration files\n\nHow to configure a pipeline with a YAML\n\nWhat can be configured\n\nRuntime settings for Docker, resources, and stack components\n\nConfiguration hierarchy\n\nFind out which configuration was used for a run\n\nAutogenerate a template yaml file\n\n🐳Customize Docker builds\n\nDocker settings on a pipeline\n\nDocker settings on a step\n\nUse a prebuilt image for pipeline execution\n\nSpecify pip dependencies and apt packages\n\nUse your own Dockerfiles\n\nWhich files are built into the image\n\nHow to reuse builds\n\nDefine where an image is built\n\n📔Run remote pipelines from notebooks\n\nLimitations of defining steps in notebook cells\n\nRun a single step from a notebook\n\n🤹Manage your ZenML server\n\nBest practices for upgrading ZenML\n\nUpgrade your ZenML server\n\nUsing ZenML server in production\n\nTroubleshoot your ZenML server\n\nMigration guide\n\nMigration guide 0.13.2 → 0.20.0\n\nMigration guide 0.23.0 → 0.30.0\n\nMigration guide 0.39.1 → 0.41.0\n\nMigration guide 0.58.2 → 0.60.0\n\n📍Develop locally\n\nUse config files to develop locally\n\nKeep your pipelines and dashboard clean\n\n⚒️Manage stacks & components\n\nDeploy a cloud stack with ZenML\n\nDeploy a cloud stack with Terraform\n\nRegister a cloud stack\n\nReference secrets in stack configuration\n\nImplement a custom stack component\n\n🚜Train with GPUs\n\nDistributed Training with 🤗 Accelerate\n\n🌲Control logging\n\nView logs on the dashboard\n\nEnable or disable logs storage\n\nSet logging verbosity\n\nDisable rich traceback output\n\nDisable colorful logging\n\n🗄️Handle Data/Artifacts\n\nHow ZenML stores data\n\nReturn multiple outputs from a step\n\nDelete an artifact\n\nOrganize data with tags\n\nGet arbitrary artifacts in a step',
+    "━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━┛$ zenml orchestrator connect <ORCHESTRATOR_NAME> --connector aws-iam-multi-us\nRunning with active stack: 'default' (repository)\nSuccessfully connected orchestrator `<ORCHESTRATOR_NAME>` to the following resources:\n┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━┓\n┃             CONNECTOR ID             │ CONNECTOR NAME   │ CONNECTOR TYPE │ RESOURCE TYPE         │ RESOURCE NAMES   ┃\n┠──────────────────────────────────────┼──────────────────┼────────────────┼───────────────────────┼──────────────────┨\n┃ ed528d5a-d6cb-4fc4-bc52-c3d2d01643e5 │ aws-iam-multi-us │ 🔶 aws         │ 🌀 kubernetes-cluster │ zenhacks-cluster ┃\n┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━┛\n\n# Register and activate a stack with the new orchestrator\n$ zenml stack register <STACK_NAME> -o <ORCHESTRATOR_NAME> ... --set\n\nif you don't have a Service Connector on hand and you don't want to register one , the local Kubernetes kubectl client needs to be configured with a configuration context pointing to the remote cluster. The kubernetes_context stack component must also be configured with the value of that context:\n\nzenml orchestrator register <ORCHESTRATOR_NAME> \\\n    --flavor=kubernetes \\\n    --kubernetes_context=<KUBERNETES_CONTEXT>\n\n# Register and activate a stack with the new orchestrator\nzenml stack register <STACK_NAME> -o <ORCHESTRATOR_NAME> ... --set\n\nZenML will build a Docker image called <CONTAINER_REGISTRY_URI>/zenml:<PIPELINE_NAME> which includes your code and use it to run your pipeline steps in Kubernetes. Check out this page if you want to learn more about how ZenML builds these images and how you can customize them.\n\nYou can now run any ZenML pipeline using the Kubernetes orchestrator:\n\npython file_that_runs_a_zenml_pipeline.py",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+* Dataset: `dim_384`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.1863     |
+| cosine_accuracy@3   | 0.4795     |
+| cosine_accuracy@5   | 0.6603     |
+| cosine_accuracy@10  | 0.7973     |
+| cosine_precision@1  | 0.1863     |
+| cosine_precision@3  | 0.1598     |
+| cosine_precision@5  | 0.1321     |
+| cosine_precision@10 | 0.0797     |
+| cosine_recall@1     | 0.1863     |
+| cosine_recall@3     | 0.4795     |
+| cosine_recall@5     | 0.6603     |
+| cosine_recall@10    | 0.7973     |
+| cosine_ndcg@10      | 0.4746     |
+| cosine_mrr@10       | 0.3726     |
+| **cosine_map@100**  | **0.3795** |
+
+#### Information Retrieval
+* Dataset: `dim_256`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.1836     |
+| cosine_accuracy@3   | 0.4877     |
+| cosine_accuracy@5   | 0.6603     |
+| cosine_accuracy@10  | 0.7918     |
+| cosine_precision@1  | 0.1836     |
+| cosine_precision@3  | 0.1626     |
+| cosine_precision@5  | 0.1321     |
+| cosine_precision@10 | 0.0792     |
+| cosine_recall@1     | 0.1836     |
+| cosine_recall@3     | 0.4877     |
+| cosine_recall@5     | 0.6603     |
+| cosine_recall@10    | 0.7918     |
+| cosine_ndcg@10      | 0.4733     |
+| cosine_mrr@10       | 0.3724     |
+| **cosine_map@100**  | **0.3793** |
+
+#### 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   | 0.1836     |
+| cosine_accuracy@3   | 0.4685     |
+| cosine_accuracy@5   | 0.6356     |
+| cosine_accuracy@10  | 0.7781     |
+| cosine_precision@1  | 0.1836     |
+| cosine_precision@3  | 0.1562     |
+| cosine_precision@5  | 0.1271     |
+| cosine_precision@10 | 0.0778     |
+| cosine_recall@1     | 0.1836     |
+| cosine_recall@3     | 0.4685     |
+| cosine_recall@5     | 0.6356     |
+| cosine_recall@10    | 0.7781     |
+| cosine_ndcg@10      | 0.4622     |
+| cosine_mrr@10       | 0.3629     |
+| **cosine_map@100**  | **0.3705** |
+
+#### Information Retrieval
+* Dataset: `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.2055     |
+| cosine_accuracy@3   | 0.4767     |
+| cosine_accuracy@5   | 0.6274     |
+| cosine_accuracy@10  | 0.7534     |
+| cosine_precision@1  | 0.2055     |
+| cosine_precision@3  | 0.1589     |
+| cosine_precision@5  | 0.1255     |
+| cosine_precision@10 | 0.0753     |
+| cosine_recall@1     | 0.2055     |
+| cosine_recall@3     | 0.4767     |
+| cosine_recall@5     | 0.6274     |
+| cosine_recall@10    | 0.7534     |
+| cosine_ndcg@10      | 0.4625     |
+| cosine_mrr@10       | 0.3707     |
+| **cosine_map@100**  | **0.3787** |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 3,284 training samples
+* Columns: <code>positive</code> and <code>anchor</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | positive                                                                          | anchor                                                                              |
+  |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                              |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 22.7 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 316.5 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                                        | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>How does ZenML help in integrating machine learning with operational processes?</code>                                                                                    | <code>ZenML - Bridging the gap between ML & Ops<br><br>Legacy Docs<br><br>Bleeding EdgeLegacy Docs0.67.0<br><br>🧙‍♂️Find older version our docs<br><br>Powered by GitBook</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+  | <code>How can I configure a data integrity check step in ZenML to perform outlier sample detection and string length verification on a dataset with specific conditions?</code> | <code>ks. For example, the following step configuration:deepchecks_data_integrity_check_step(<br>    check_list=[<br>        DeepchecksDataIntegrityCheck.TABULAR_OUTLIER_SAMPLE_DETECTION,<br>        DeepchecksDataIntegrityCheck.TABULAR_STRING_LENGTH_OUT_OF_BOUNDS,<br>    ],<br>    dataset_kwargs=dict(label='class', cat_features=['country', 'state']),<br>    check_kwargs={<br>        DeepchecksDataIntegrityCheck.TABULAR_OUTLIER_SAMPLE_DETECTION: dict(<br>            nearest_neighbors_percent=0.01,<br>            extent_parameter=3,<br>            condition_outlier_ratio_less_or_equal=dict(<br>                max_outliers_ratio=0.007,<br>                outlier_score_threshold=0.5,<br>            ),<br>            condition_no_outliers=dict(<br>                outlier_score_threshold=0.6,<br>            )<br>        ),<br>        DeepchecksDataIntegrityCheck.TABULAR_STRING_LENGTH_OUT_OF_BOUNDS: dict(<br>            num_percentiles=1000,<br>            min_unique_values=3,<br>            condition_number_of_outliers_less_or_equal=dict(<br>                max_outliers=3,<br>            )<br>        ),<br>    },<br>    ...<br>)<br><br>is equivalent to running the following Deepchecks tests:<br><br>import deepchecks.tabular.checks as tabular_checks<br>from deepchecks.tabular import Suite<br>from deepchecks.tabular import Dataset<br><br>train_dataset = Dataset(<br>    reference_dataset,<br>    label='class',<br>    cat_features=['country', 'state']<br>)<br><br>suite = Suite(name="custom")<br>check = tabular_checks.OutlierSampleDetection(<br>    nearest_neighbors_percent=0.01,<br>    extent_parameter=3,<br>)<br>check.add_condition_outlier_ratio_less_or_equal(<br>    max_outliers_ratio=0.007,<br>    outlier_score_threshold=0.5,<br>)<br>check.add_condition_no_outliers(<br>    outlier_score_threshold=0.6,<br>)<br>suite.add(check)<br>check = tabular_checks.StringLengthOutOfBounds(<br>    num_percentiles=1000,<br>    min_unique_values=3,<br>)<br>check.add_condition_number_of_outliers_less_or_equal(<br>    max_outliers=3,<br>)<br>suite.run(train_dataset=train_dataset)<br><br>The Deepchecks Data Validator</code> |
+  | <code>How can I develop a custom data validator in ZenML?</code>                                                                                                                | <code>custom data validator<br><br>📈Experiment Trackers<br><br>CometMLflow<br><br>Neptune<br><br>Weights & Biases<br><br>Develop a custom experiment tracker<br><br>🏃‍♀️Model Deployers<br><br>MLflow<br><br>Seldon<br><br>BentoML<br><br>Hugging Face<br><br>Databricks<br><br>Develop a Custom Model Deployer<br><br>👣Step Operators<br><br>Amazon SageMaker<br><br>Google Cloud VertexAI<br><br>AzureML<br><br>Kubernetes<br><br>Spark<br><br>Develop a Custom Step Operator<br><br>❗Alerters<br><br>Discord Alerter<br><br>Slack Alerter<br><br>Develop a Custom Alerter<br><br>🖼️Image Builders<br><br>Local Image Builder<br><br>Kaniko Image Builder<br><br>Google Cloud Image Builder<br><br>Develop a Custom Image Builder<br><br>🏷️Annotators<br><br>Argilla<br><br>Label Studio<br><br>Pigeon<br><br>Prodigy<br><br>Develop a Custom Annotator<br><br>📓Model Registries<br><br>MLflow Model Registry<br><br>Develop a Custom Model Registry<br><br>📊Feature Stores<br><br>Feast<br><br>Develop a Custom Feature Store<br><br>Examples<br><br>🚀Quickstart<br><br>🔏End-to-End Batch Inference<br><br>📚Basic NLP with BERT<br><br>👁️Computer Vision with YoloV8<br><br>📖LLM Finetuning<br><br>🧩More Projects...<br><br>Reference<br><br>🐍Python Client<br><br>📼Global settings<br><br>🌎Environment Variables<br><br>👀API reference<br><br>🤷SDK & CLI reference<br><br>📚How do I...?<br><br>♻️Migration guide<br><br>Migration guide 0.13.2 → 0.20.0<br><br>Migration guide 0.23.0 → 0.30.0<br><br>Migration guide 0.39.1 → 0.41.0<br><br>Migration guide 0.58.2 → 0.60.0<br><br>💜Community & content<br><br>❓FAQ<br><br>Powered by GitBook</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          384,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 16
+- `gradient_accumulation_steps`: 16
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 4
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `tf32`: False
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 4
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 16
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 4
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: False
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: 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
+- `eval_on_start`: False
+- `eval_use_gather_object`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch      | Step   | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_384_cosine_map@100 | dim_64_cosine_map@100 |
+|:----------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
+| 0.3893     | 10     | 1.7142        | -                      | -                      | -                      | -                     |
+| 0.7786     | 20     | 0.4461        | -                      | -                      | -                      | -                     |
+| 0.9732     | 25     | -             | 0.3544                 | 0.3592                 | 0.3674                 | 0.3523                |
+| 1.1655     | 30     | 0.1889        | -                      | -                      | -                      | -                     |
+| 1.5547     | 40     | 0.1196        | -                      | -                      | -                      | -                     |
+| 1.9440     | 50     | 0.0717        | -                      | -                      | -                      | -                     |
+| 1.9830     | 51     | -             | 0.3672                 | 0.3727                 | 0.3728                 | 0.3797                |
+| 2.3309     | 60     | 0.0474        | -                      | -                      | -                      | -                     |
+| 2.7202     | 70     | 0.0418        | -                      | -                      | -                      | -                     |
+| **2.9927** | **77** | **-**         | **0.3722**             | **0.3772**             | **0.3798**             | **0.3783**            |
+| 3.1071     | 80     | 0.0355        | -                      | -                      | -                      | -                     |
+| 3.4964     | 90     | 0.0351        | -                      | -                      | -                      | -                     |
+| 3.8856     | 100    | 0.0276        | 0.3705                 | 0.3793                 | 0.3795                 | 0.3787                |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.12.3
+- Sentence Transformers: 3.0.1
+- Transformers: 4.44.0
+- PyTorch: 2.5.0+cu124
+- Accelerate: 0.33.0
+- Datasets: 2.20.0
+- 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "Snowflake/snowflake-arctic-embed-m-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,12 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.44.0",
+    "pytorch": "2.5.0+cu124"
+  },
+  "prompts": {
+    "query": "Represent this sentence for searching relevant passages: "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b8c58ad12b1547c240e2803ab66e9f6a8fa7baaa022ce3c0336bc103cdbb96f
+size 435588776

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,62 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}