metadata
base_model: Snowflake/snowflake-arctic-embed-m
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1490
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: >-
What is the error message related to the blob-container for the
azure-generic subscription in ZenML?
sentences:
- >-
─────────────────────────────────────────────────┨┃ 🇦
azure-generic │ ZenML
Subscription
┃
┠───────────────────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┨
┃ 📦 blob-container │ 💥 error: connector authorization failure: the
'access-token' authentication method is not supported for blob storage
resources ┃
┠───────────────────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┨
┃ 🌀 kubernetes-cluster │
demo-zenml-demos/demo-zenml-terraform-cluster
┃
┠───────────────────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┨
┃ 🐳 docker-registry │
demozenmlcontainerregistry.azurecr.io
┃
┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
zenml service-connector describe azure-session-token
Example Command Output
Service connector 'azure-session-token' of type 'azure' with id
'94d64103-9902-4aa5-8ce4-877061af89af' is owned by user 'default' and is
'private'.
'azure-session-token' azure Service Connector Details
┏━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ PROPERTY │
VALUE
┃
┠──────────────────┼────────────────────────────────────────────────────────────────────────────────┨
┃ ID │
94d64103-9902-4aa5-8ce4-877061af89af
┃
- >-
🪆Use the Model Control Plane
A Model is simply an entity that groups pipelines, artifacts, metadata,
and other crucial business data into a unified entity. A ZenML Model is
a concept that more broadly encapsulates your ML products business
logic. You may even think of a ZenML Model as a "project" or a
"workspace"
Please note that one of the most common artifacts that is associated
with a Model in ZenML is the so-called technical model, which is the
actually model file/files that holds the weight and parameters of a
machine learning training result. However, this is not the only artifact
that is relevant; artifacts such as the training data and the
predictions this model produces in production are also linked inside a
ZenML Model.
Models are first-class citizens in ZenML and as such viewing and using
them is unified and centralized in the ZenML API, client as well as on
the ZenML Pro dashboard.
A Model captures lineage information and more. Within a Model, different
Model versions can be staged. For example, you can rely on your
predictions at a specific stage, like Production, and decide whether the
Model version should be promoted based on your business rules during
training. Plus, accessing data from other Models and their versions is
just as simple.
The Model Control Plane is how you manage your models through this
unified interface. It allows you to combine the logic of your pipelines,
artifacts and crucial business data along with the actual 'technical
model'.
To see an end-to-end example, please refer to the starter guide.
PreviousDisabling visualizations
NextRegistering a Model
Last updated 12 days ago
- >-
turns:
The Docker image repo digest or name.
"""This is a slimmed-down version of the base implementation which aims
to highlight the abstraction layer. In order to see the full
implementation and get the complete docstrings, please check the source
code on GitHub .
Build your own custom image builder
If you want to create your own custom flavor for an image builder, you
can follow the following steps:
Create a class that inherits from the BaseImageBuilder class and
implement the abstract build method. This method should use the given
build context and build a Docker image with it. If additionally a
container registry is passed to the build method, the image builder is
also responsible for pushing the image there.
If you need to provide any configuration, create a class that inherits
from the BaseImageBuilderConfig class and adds your configuration
parameters.
Bring both the implementation and the configuration together by
inheriting from the BaseImageBuilderFlavor class. Make sure that you
give a name to the flavor through its abstract property.
Once you are done with the implementation, you can register it through
the CLI. Please ensure you point to the flavor class via dot notation:
zenml image-builder flavor register <path.to.MyImageBuilderFlavor>
For example, if your flavor class MyImageBuilderFlavor is defined in
flavors/my_flavor.py, you'd register it by doing:
zenml image-builder flavor register
flavors.my_flavor.MyImageBuilderFlavor
ZenML resolves the flavor class by taking the path where you initialized
zenml (via zenml init) as the starting point of resolution. Therefore,
please ensure you follow the best practice of initializing zenml at the
root of your repository.
If ZenML does not find an initialized ZenML repository in any parent
directory, it will default to the current working directory, but usually
it's better to not have to rely on this mechanism, and initialize zenml
at the root.
Afterward, you should see the new flavor in the list of available
flavors:
- source_sentence: >-
Where can I find more information on configuring the Spark step operator
in ZenML?
sentences:
- >-
upplied a custom value while creating the cluster.Run the following
command.
aws eks update-kubeconfig --name <NAME> --region <REGION>
Get the name of the deployed cluster.
zenml stack recipe output gke-cluster-name\
Figure out the region that the cluster is deployed to. By default, the
region is set to europe-west1, which you should use in the next step if
you haven't supplied a custom value while creating the cluster.\
Figure out the project that the cluster is deployed to. You must have
passed in a project ID while creating a GCP resource for the first
time.\
Run the following command.
gcloud container clusters get-credentials <NAME> --region <REGION>
--project <PROJECT_ID>
You may already have your kubectl client configured with your cluster.
Check by running kubectl get nodes before proceeding.
Get the name of the deployed cluster.
zenml stack recipe output k3d-cluster-name\
Set the KUBECONFIG env variable to the kubeconfig file from the cluster.
export KUBECONFIG=$(k3d kubeconfig get <NAME>)\
You can now use the kubectl client to talk to the cluster.
Stack Recipe Deploy
The steps for the stack recipe case should be the same as the ones
listed above. The only difference that you need to take into account is
the name of the outputs that contain your cluster name and the default
regions.
Each recipe might have its own values and here's how you can ascertain
those values.
For the cluster name, go into the outputs.tf file in the root directory
and search for the output that exposes the cluster name.
For the region, check out the variables.tf or the locals.tf file for the
default value assigned to it.
PreviousTroubleshoot the deployed server
NextCustom secret stores
Last updated 10 months ago
- >-
ettings to specify AzureML step operator settings.Difference between
stack component settings at registration-time vs real-time
For stack-component-specific settings, you might be wondering what the
difference is between these and the configuration passed in while doing
zenml stack-component register <NAME> --config1=configvalue
--config2=configvalue, etc. The answer is that the configuration passed
in at registration time is static and fixed throughout all pipeline
runs, while the settings can change.
A good example of this is the MLflow Experiment Tracker, where
configuration which remains static such as the tracking_url is sent
through at registration time, while runtime configuration such as the
experiment_name (which might change every pipeline run) is sent through
as runtime settings.
Even though settings can be overridden at runtime, you can also specify
default values for settings while configuring a stack component. For
example, you could set a default value for the nested setting of your
MLflow experiment tracker: zenml experiment-tracker register <NAME>
--flavor=mlflow --nested=True
This means that all pipelines that run using this experiment tracker use
nested MLflow runs unless overridden by specifying settings for the
pipeline at runtime.
Using the right key for Stack-component-specific settings
When specifying stack-component-specific settings, a key needs to be
passed. This key should always correspond to the pattern:
<COMPONENT_CATEGORY>.<COMPONENT_FLAVOR>
For example, the SagemakerStepOperator supports passing in
estimator_args. The way to specify this would be to use the key
step_operator.sagemaker
@step(step_operator="nameofstepoperator", settings=
{"step_operator.sagemaker": {"estimator_args": {"instance_type":
"m7g.medium"}}})
def my_step():
...
@step(step_operator="nameofstepoperator", settings=
{"step_operator.sagemaker":
SagemakerStepOperatorSettings(instance_type="m7g.medium")})
def my_step():
...
or in YAML:
steps:
my_step:
- >-
_operator
@step(step_operator=step_operator.name)def step_on_spark(...) -> ...:
...
Additional configuration
For additional configuration of the Spark step operator, you can pass
SparkStepOperatorSettings when defining or running your pipeline. Check
out the SDK docs for a full list of available attributes and this docs
page for more information on how to specify settings.
PreviousAzureML
NextDevelop a Custom Step Operator
Last updated 19 days ago
- source_sentence: >-
How can I register an Azure Service Connector for an ACR registry in ZenML
using the CLI?
sentences:
- >-
ure Container Registry to the remote ACR registry.To set up the Azure
Container Registry to authenticate to Azure and access an ACR registry,
it is recommended to leverage the many features provided by the Azure
Service Connector such as auto-configuration, local login, best security
practices regarding long-lived credentials and reusing the same
credentials across multiple stack components.
If you don't already have an Azure Service Connector configured in your
ZenML deployment, you can register one using the interactive CLI
command. You have the option to configure an Azure Service Connector
that can be used to access a ACR registry or even more than one type of
Azure resource:
zenml service-connector register --type azure -i
A non-interactive CLI example that uses Azure Service Principal
credentials to configure an Azure Service Connector targeting a single
ACR registry is:
zenml service-connector register <CONNECTOR_NAME> --type azure
--auth-method service-principal --tenant_id=<AZURE_TENANT_ID>
--client_id=<AZURE_CLIENT_ID> --client_secret=<AZURE_CLIENT_SECRET>
--resource-type docker-registry --resource-id <REGISTRY_URI>
Example Command Output
$ zenml service-connector register azure-demo --type azure --auth-method
service-principal --tenant_id=a79f3633-8f45-4a74-a42e-68871c17b7fb
--client_id=8926254a-8c3f-430a-a2fd-bdab234d491e
--client_secret=AzureSuperSecret --resource-type docker-registry
--resource-id demozenmlcontainerregistry.azurecr.io
⠸ Registering service connector 'azure-demo'...
Successfully registered service connector `azure-demo` with access to
the following resources:
┏━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE NAMES ┃
┠────────────────────┼───────────────────────────────────────┨
┃ 🐳 docker-registry │ demozenmlcontainerregistry.azurecr.io ┃
┗━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
- >-
Default Container Registry
Storing container images locally.
The Default container registry is a container registry flavor that comes
built-in with ZenML and allows container registry URIs of any format.
When to use it
You should use the Default container registry if you want to use a local
container registry or when using a remote container registry that is not
covered by other container registry flavors.
Local registry URI format
To specify a URI for a local container registry, use the following
format:
localhost:<PORT>
localhost:5000
localhost:8000
localhost:9999
How to use it
To use the Default container registry, we need:
Docker installed and running.
The registry URI. If you're using a local container registry, check out
the previous section on the URI format.
We can then register the container registry and use it in our active
stack:
zenml container-registry register <NAME> \
--flavor=default \
--uri=<REGISTRY_URI>
zenml stack update -c <NAME>
You may also need to set up authentication required to log in to the
container registry.
Authentication Methods
If you are using a private container registry, you will need to
configure some form of authentication to login to the registry. If
you're looking for a quick way to get started locally, you can use the
Local Authentication method. However, the recommended way to
authenticate to a remote private container registry is through a Docker
Service Connector.
If your target private container registry comes from a cloud provider
like AWS, GCP or Azure, you should use the container registry flavor
targeted at that cloud provider. For example, if you're using AWS, you
should use the AWS Container Registry flavor. These cloud provider
flavors also use specialized cloud provider Service Connectors to
authenticate to the container registry.
- >-
egister gcp-demo-multi --type gcp --auto-configureExample Command Output
```text
Successfully registered service connector `gcp-demo-multi` with access
to the following resources:
┏━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE
NAMES ┃
┠───────────────────────┼─────────────────────────────────────────────────┨
┃ 🔵 gcp-generic │
zenml-core ┃
┠───────────────────────┼─────────────────────────────────────────────────┨
┃ 📦 gcs-bucket │
gs://zenml-bucket-sl ┃
┃ │
gs://zenml-core.appspot.com ┃
┃ │
gs://zenml-core_cloudbuild ┃
┃ │
gs://zenml-datasets ┃
┠───────────────────────┼─────────────────────────────────────────────────┨
┃ 🌀 kubernetes-cluster │
zenml-test-cluster ┃
┠───────────────────────┼─────────────────────────────────────────────────┨
┃ 🐳 docker-registry │
gcr.io/zenml-core ┃
┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
```
**NOTE**: from this point forward, we don't need the local GCP CLI
credentials or the local GCP CLI at all. The steps that follow can be
run on any machine regardless of whether it has been configured and
authorized to access the GCP project.
4. find out which GCS buckets, GCR registries, and GKE Kubernetes
clusters we can gain access to. We'll use this information to configure
the Stack Components in our minimal GCP stack: a GCS Artifact Store, a
Kubernetes Orchestrator, and a GCP Container Registry.
```sh
zenml service-connector list-resources --resource-type gcs-bucket
```
Example Command Output
```text
The following 'gcs-bucket' resources can be accessed by service
connectors configured in your workspace:
- source_sentence: >-
What resources does the `gcp-demo-multi` service connector have access to
after registration?
sentences:
- >-
Find out which configuration was used for a run
Sometimes you might want to extract the used configuration from a
pipeline that has already run. You can do this simply by loading the
pipeline run and accessing its config attribute.
from zenml.client import Client
pipeline_run = Client().get_pipeline_run("<PIPELINE_RUN_NAME>")
configuration = pipeline_run.config
PreviousConfiguration hierarchy
NextAutogenerate a template yaml file
Last updated 15 days ago
- >-
onfig class and add your configuration parameters.Bring both the
implementation and the configuration together by inheriting from the
BaseModelDeployerFlavor class. Make sure that you give a name to the
flavor through its abstract property.
Create a service class that inherits from the BaseService class and
implements the abstract methods. This class will be used to represent
the deployed model server in ZenML.
Once you are done with the implementation, you can register it through
the CLI. Please ensure you point to the flavor class via dot notation:
zenml model-deployer flavor register <path.to.MyModelDeployerFlavor>
For example, if your flavor class MyModelDeployerFlavor is defined in
flavors/my_flavor.py, you'd register it by doing:
zenml model-deployer flavor register
flavors.my_flavor.MyModelDeployerFlavor
ZenML resolves the flavor class by taking the path where you initialized
zenml (via zenml init) as the starting point of resolution. Therefore,
please ensure you follow the best practice of initializing zenml at the
root of your repository.
If ZenML does not find an initialized ZenML repository in any parent
directory, it will default to the current working directory, but
usually, it's better to not have to rely on this mechanism and
initialize zenml at the root.
Afterward, you should see the new flavor in the list of available
flavors:
zenml model-deployer flavor list
It is important to draw attention to when and how these base
abstractions are coming into play in a ZenML workflow.
The CustomModelDeployerFlavor class is imported and utilized upon the
creation of the custom flavor through the CLI.
The CustomModelDeployerConfig class is imported when someone tries to
register/update a stack component with this custom flavor. Especially,
during the registration process of the stack component, the config will
be used to validate the values given by the user. As Config objects are
inherently pydantic objects, you can also add your own custom validators
here.
- >-
egister gcp-demo-multi --type gcp --auto-configureExample Command Output
```text
Successfully registered service connector `gcp-demo-multi` with access
to the following resources:
┏━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE
NAMES ┃
┠───────────────────────┼─────────────────────────────────────────────────┨
┃ 🔵 gcp-generic │
zenml-core ┃
┠───────────────────────┼─────────────────────────────────────────────────┨
┃ 📦 gcs-bucket │
gs://zenml-bucket-sl ┃
┃ │
gs://zenml-core.appspot.com ┃
┃ │
gs://zenml-core_cloudbuild ┃
┃ │
gs://zenml-datasets ┃
┠───────────────────────┼─────────────────────────────────────────────────┨
┃ 🌀 kubernetes-cluster │
zenml-test-cluster ┃
┠───────────────────────┼─────────────────────────────────────────────────┨
┃ 🐳 docker-registry │
gcr.io/zenml-core ┃
┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
```
**NOTE**: from this point forward, we don't need the local GCP CLI
credentials or the local GCP CLI at all. The steps that follow can be
run on any machine regardless of whether it has been configured and
authorized to access the GCP project.
4. find out which GCS buckets, GCR registries, and GKE Kubernetes
clusters we can gain access to. We'll use this information to configure
the Stack Components in our minimal GCP stack: a GCS Artifact Store, a
Kubernetes Orchestrator, and a GCP Container Registry.
```sh
zenml service-connector list-resources --resource-type gcs-bucket
```
Example Command Output
```text
The following 'gcs-bucket' resources can be accessed by service
connectors configured in your workspace:
- source_sentence: What is the result of executing a Deepchecks test suite in ZenML?
sentences:
- >-
urns:
Deepchecks test suite execution result
"""# validation pre-processing (e.g. dataset preparation) can take place
here
data_validator = DeepchecksDataValidator.get_active_data_validator()
suite = data_validator.data_validation(
dataset=dataset,
check_list=[
DeepchecksDataIntegrityCheck.TABULAR_OUTLIER_SAMPLE_DETECTION,
DeepchecksDataIntegrityCheck.TABULAR_STRING_LENGTH_OUT_OF_BOUNDS,
],
# validation post-processing (e.g. interpret results, take actions) can
happen here
return suite
The arguments that the Deepchecks Data Validator methods can take in are
the same as those used for the Deepchecks standard steps.
Have a look at the complete list of methods and parameters available in
the DeepchecksDataValidator API in the SDK docs.
Call Deepchecks directly
You can use the Deepchecks library directly in your custom pipeline
steps, and only leverage ZenML's capability of serializing, versioning
and storing the SuiteResult objects in its Artifact Store, e.g.:
import pandas as pd
import deepchecks.tabular.checks as tabular_checks
from deepchecks.core.suite import SuiteResult
from deepchecks.tabular import Suite
from deepchecks.tabular import Dataset
from zenml import step
@step
def data_integrity_check(
dataset: pd.DataFrame,
) -> SuiteResult:
"""Custom data integrity check step with Deepchecks
Args:
dataset: a Pandas DataFrame
Returns:
Deepchecks test suite execution result
"""
# validation pre-processing (e.g. dataset preparation) can take place
here
train_dataset = Dataset(
dataset,
label='class',
cat_features=['country', 'state']
suite = Suite(name="custom")
check = tabular_checks.OutlierSampleDetection(
nearest_neighbors_percent=0.01,
extent_parameter=3,
check.add_condition_outlier_ratio_less_or_equal(
max_outliers_ratio=0.007,
outlier_score_threshold=0.5,
suite.add(check)
check = tabular_checks.StringLengthOutOfBounds(
num_percentiles=1000,
min_unique_values=3,
check.add_condition_number_of_outliers_less_or_equal(
max_outliers=3,
- >-
ervice-principal
```
Example Command Output
```Successfully connected orchestrator `aks-demo-cluster` to the
following resources:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ CONNECTOR ID │ CONNECTOR NAME │
CONNECTOR TYPE │ RESOURCE TYPE │ RESOURCE
NAMES ┃
┠──────────────────────────────────────┼─────────────────────────┼────────────────┼───────────────────────┼───────────────────────────────────────────────┨
┃ f2316191-d20b-4348-a68b-f5e347862196 │ azure-service-principal │ 🇦
azure │ 🌀 kubernetes-cluster │
demo-zenml-demos/demo-zenml-terraform-cluster ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
```
Register and connect an Azure Container Registry Stack Component to an
ACR container registry:Copyzenml container-registry register
acr-demo-registry --flavor azure
--uri=demozenmlcontainerregistry.azurecr.io
Example Command Output
```
Successfully registered container_registry `acr-demo-registry`.
```
```sh
zenml container-registry connect acr-demo-registry --connector
azure-service-principal
```
Example Command Output
```
Successfully connected container registry `acr-demo-registry` to the
following resources:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ CONNECTOR ID │ CONNECTOR NAME │
CONNECTOR TYPE │ RESOURCE TYPE │ RESOURCE
NAMES ┃
┠──────────────────────────────────────┼─────────────────────────┼────────────────┼────────────────────┼───────────────────────────────────────┨
┃ f2316191-d20b-4348-a68b-f5e347862196 │ azure-service-principal │ 🇦
azure │ 🐳 docker-registry │ demozenmlcontainerregistry.azurecr.io
┃
- >-
r │ zenhacks-cluster
┃┠───────────────────────┼──────────────────────────────────────────────┨
┃ 🐳 docker-registry │ 715803424590.dkr.ecr.us-east-1.amazonaws.com ┃
┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
The Service Connector configuration shows long-lived credentials were
lifted from the local environment and the AWS Session Token
authentication method was configured:
zenml service-connector describe aws-session-token
Example Command Output
Service connector 'aws-session-token' of type 'aws' with id
'3ae3e595-5cbc-446e-be64-e54e854e0e3f' is owned by user 'default' and is
'private'.
'aws-session-token' aws Service Connector Details
┏━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ PROPERTY │
VALUE
┃
┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨
┃ ID │
c0f8e857-47f9-418b-a60f-c3b03023da54
┃
┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨
┃ NAME │
aws-session-token
┃
┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨
┃ TYPE │ 🔶
aws ┃
┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨
┃ AUTH METHOD │
session-token
┃
┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨
┃ RESOURCE TYPES │ 🔶 aws-generic, 📦 s3-bucket, 🌀
kubernetes-cluster, 🐳 docker-registry ┃
┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨
model-index:
- name: zenml/finetuned-snowflake-arctic-embed-m
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 384
type: dim_384
metrics:
- type: cosine_accuracy@1
value: 0.3614457831325301
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6987951807228916
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7530120481927711
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8554216867469879
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3614457831325301
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.23293172690763048
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15060240963855417
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08554216867469877
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3614457831325301
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.6987951807228916
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7530120481927711
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8554216867469879
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6194049451779184
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5427878179384205
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5472907234693755
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.3433734939759036
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6807228915662651
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7650602409638554
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8373493975903614
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3433734939759036
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2269076305220883
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15301204819277103
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08373493975903612
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3433734939759036
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.6807228915662651
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7650602409638554
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8373493975903614
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.602546157610675
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.525891661885638
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5310273317942533
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.3132530120481928
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6265060240963856
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7168674698795181
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7891566265060241
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3132530120481928
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.20883534136546178
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1433734939759036
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0789156626506024
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3132530120481928
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.6265060240963856
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7168674698795181
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7891566265060241
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5630057581169484
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4893144004589788
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4960510164414996
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.25903614457831325
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5120481927710844
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6325301204819277
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7168674698795181
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.25903614457831325
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.17068273092369476
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12650602409638553
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07168674698795179
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.25903614457831325
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5120481927710844
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6325301204819277
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7168674698795181
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.48618223058871674
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.41233027347485207
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.42094598177412385
name: Cosine Map@100
zenml/finetuned-snowflake-arctic-embed-m
This is a sentence-transformers model finetuned from Snowflake/snowflake-arctic-embed-m. 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
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
- Language: en
- License: apache-2.0
Model Sources
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:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("zenml/finetuned-snowflake-arctic-embed-m")
sentences = [
'What is the result of executing a Deepchecks test suite in ZenML?',
'urns:\n\nDeepchecks test suite execution result\n\n"""# validation pre-processing (e.g. dataset preparation) can take place here\n\ndata_validator = DeepchecksDataValidator.get_active_data_validator()\n\nsuite = data_validator.data_validation(\n\ndataset=dataset,\n\ncheck_list=[\n\nDeepchecksDataIntegrityCheck.TABULAR_OUTLIER_SAMPLE_DETECTION,\n\nDeepchecksDataIntegrityCheck.TABULAR_STRING_LENGTH_OUT_OF_BOUNDS,\n\n],\n\n# validation post-processing (e.g. interpret results, take actions) can happen here\n\nreturn suite\n\nThe arguments that the Deepchecks Data Validator methods can take in are the same as those used for the Deepchecks standard steps.\n\nHave a look at the complete list of methods and parameters available in the DeepchecksDataValidator API in the SDK docs.\n\nCall Deepchecks directly\n\nYou can use the Deepchecks library directly in your custom pipeline steps, and only leverage ZenML\'s capability of serializing, versioning and storing the SuiteResult objects in its Artifact Store, e.g.:\n\nimport pandas as pd\n\nimport deepchecks.tabular.checks as tabular_checks\n\nfrom deepchecks.core.suite import SuiteResult\n\nfrom deepchecks.tabular import Suite\n\nfrom deepchecks.tabular import Dataset\n\nfrom zenml import step\n\n@step\n\ndef data_integrity_check(\n\ndataset: pd.DataFrame,\n\n) -> SuiteResult:\n\n"""Custom data integrity check step with Deepchecks\n\nArgs:\n\ndataset: a Pandas DataFrame\n\nReturns:\n\nDeepchecks test suite execution result\n\n"""\n\n# validation pre-processing (e.g. dataset preparation) can take place here\n\ntrain_dataset = Dataset(\n\ndataset,\n\nlabel=\'class\',\n\ncat_features=[\'country\', \'state\']\n\nsuite = Suite(name="custom")\n\ncheck = tabular_checks.OutlierSampleDetection(\n\nnearest_neighbors_percent=0.01,\n\nextent_parameter=3,\n\ncheck.add_condition_outlier_ratio_less_or_equal(\n\nmax_outliers_ratio=0.007,\n\noutlier_score_threshold=0.5,\n\nsuite.add(check)\n\ncheck = tabular_checks.StringLengthOutOfBounds(\n\nnum_percentiles=1000,\n\nmin_unique_values=3,\n\ncheck.add_condition_number_of_outliers_less_or_equal(\n\nmax_outliers=3,',
"r │ zenhacks-cluster ┃┠───────────────────────┼──────────────────────────────────────────────┨\n\n┃ 🐳 docker-registry │ 715803424590.dkr.ecr.us-east-1.amazonaws.com ┃\n\n┗━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛\n\nThe Service Connector configuration shows long-lived credentials were lifted from the local environment and the AWS Session Token authentication method was configured:\n\nzenml service-connector describe aws-session-token\n\nExample Command Output\n\nService connector 'aws-session-token' of type 'aws' with id '3ae3e595-5cbc-446e-be64-e54e854e0e3f' is owned by user 'default' and is 'private'.\n\n'aws-session-token' aws Service Connector Details\n\n┏━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n\n┃ PROPERTY │ VALUE ┃\n\n┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨\n\n┃ ID │ c0f8e857-47f9-418b-a60f-c3b03023da54 ┃\n\n┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨\n\n┃ NAME │ aws-session-token ┃\n\n┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨\n\n┃ TYPE │ 🔶 aws ┃\n\n┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨\n\n┃ AUTH METHOD │ session-token ┃\n\n┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨\n\n┃ RESOURCE TYPES │ 🔶 aws-generic, 📦 s3-bucket, 🌀 kubernetes-cluster, 🐳 docker-registry ┃\n\n┠──────────────────┼─────────────────────────────────────────────────────────────────────────┨",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
Evaluation
Metrics
Information Retrieval
| Metric |
Value |
| cosine_accuracy@1 |
0.3614 |
| cosine_accuracy@3 |
0.6988 |
| cosine_accuracy@5 |
0.753 |
| cosine_accuracy@10 |
0.8554 |
| cosine_precision@1 |
0.3614 |
| cosine_precision@3 |
0.2329 |
| cosine_precision@5 |
0.1506 |
| cosine_precision@10 |
0.0855 |
| cosine_recall@1 |
0.3614 |
| cosine_recall@3 |
0.6988 |
| cosine_recall@5 |
0.753 |
| cosine_recall@10 |
0.8554 |
| cosine_ndcg@10 |
0.6194 |
| cosine_mrr@10 |
0.5428 |
| cosine_map@100 |
0.5473 |
Information Retrieval
| Metric |
Value |
| cosine_accuracy@1 |
0.3434 |
| cosine_accuracy@3 |
0.6807 |
| cosine_accuracy@5 |
0.7651 |
| cosine_accuracy@10 |
0.8373 |
| cosine_precision@1 |
0.3434 |
| cosine_precision@3 |
0.2269 |
| cosine_precision@5 |
0.153 |
| cosine_precision@10 |
0.0837 |
| cosine_recall@1 |
0.3434 |
| cosine_recall@3 |
0.6807 |
| cosine_recall@5 |
0.7651 |
| cosine_recall@10 |
0.8373 |
| cosine_ndcg@10 |
0.6025 |
| cosine_mrr@10 |
0.5259 |
| cosine_map@100 |
0.531 |
Information Retrieval
| Metric |
Value |
| cosine_accuracy@1 |
0.3133 |
| cosine_accuracy@3 |
0.6265 |
| cosine_accuracy@5 |
0.7169 |
| cosine_accuracy@10 |
0.7892 |
| cosine_precision@1 |
0.3133 |
| cosine_precision@3 |
0.2088 |
| cosine_precision@5 |
0.1434 |
| cosine_precision@10 |
0.0789 |
| cosine_recall@1 |
0.3133 |
| cosine_recall@3 |
0.6265 |
| cosine_recall@5 |
0.7169 |
| cosine_recall@10 |
0.7892 |
| cosine_ndcg@10 |
0.563 |
| cosine_mrr@10 |
0.4893 |
| cosine_map@100 |
0.4961 |
Information Retrieval
| Metric |
Value |
| cosine_accuracy@1 |
0.259 |
| cosine_accuracy@3 |
0.512 |
| cosine_accuracy@5 |
0.6325 |
| cosine_accuracy@10 |
0.7169 |
| cosine_precision@1 |
0.259 |
| cosine_precision@3 |
0.1707 |
| cosine_precision@5 |
0.1265 |
| cosine_precision@10 |
0.0717 |
| cosine_recall@1 |
0.259 |
| cosine_recall@3 |
0.512 |
| cosine_recall@5 |
0.6325 |
| cosine_recall@10 |
0.7169 |
| cosine_ndcg@10 |
0.4862 |
| cosine_mrr@10 |
0.4123 |
| cosine_map@100 |
0.4209 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 1,490 training samples
- Columns:
positive and anchor
- Approximate statistics based on the first 1000 samples:
|
positive |
anchor |
| type |
string |
string |
| details |
- min: 9 tokens
- mean: 21.15 tokens
- max: 48 tokens
|
- min: 21 tokens
- mean: 373.39 tokens
- max: 512 tokens
|
- Samples:
| positive |
anchor |
How do I configure the IAM role for the ZenML AWS CLI profile? |
ication method with a configured IAM role instead.The connector needs to be configured with the IAM role to be assumed accompanied by an AWS secret key associated with an IAM user or an STS token associated with another IAM role. The IAM user or IAM role must have permission to assume the target IAM role. The connector will generate temporary STS tokens upon request by calling the AssumeRole STS API.
The best practice implemented with this authentication scheme is to keep the set of permissions associated with the primary IAM user or IAM role down to the bare minimum and grant permissions to the privilege-bearing IAM role instead.
An AWS region is required and the connector may only be used to access AWS resources in the specified region.
One or more optional IAM session policies may also be configured to further restrict the permissions of the generated STS tokens. If not specified, IAM session policies are automatically configured for the generated STS tokens to restrict them to the minimum set of permissions required to access the target resource. Refer to the documentation for each supported Resource Type for the complete list of AWS permissions automatically granted to the generated STS tokens.
The default expiration period for generated STS tokens is 1 hour with a minimum of 15 minutes up to the maximum session duration setting configured for the IAM role (default is 1 hour). If you need longer-lived tokens, you can configure the IAM role to use a higher maximum expiration value (up to 12 hours) or use the AWS Federation Token or AWS Session Token authentication methods.
For more information on IAM roles and the AssumeRole AWS API, see the official AWS documentation on the subject.
For more information about the difference between this method and the AWS Federation Token authentication method, consult this AWS documentation page.
The following assumes the local AWS CLI has a zenml AWS CLI profile already configured with an AWS Secret Key and an IAM role to be assumed: |
What command should be used to list all available alerter flavors after initializing ZenML at the root of the repository? |
initializing zenml at the root of your repository.If ZenML does not find an initialized ZenML repository in any parent directory, it will default to the current working directory, but usually, it's better to not have to rely on this mechanism and initialize zenml at the root.
Afterward, you should see the new custom alerter flavor in the list of available alerter flavors:
zenml alerter flavor list
It is important to draw attention to when and how these abstractions are coming into play in a ZenML workflow.
The MyAlerterFlavor class is imported and utilized upon the creation of the custom flavor through the CLI.
The MyAlerterConfig class is imported when someone tries to register/update a stack component with the my_alerter flavor. Especially, during the registration process of the stack component, the config will be used to validate the values given by the user. As Config objects are inherently pydantic objects, you can also add your own custom validators here.
The MyAlerter only comes into play when the component is ultimately in use.
The design behind this interaction lets us separate the configuration of the flavor from its implementation. This way we can register flavors and components even when the major dependencies behind their implementation are not installed in our local setting (assuming the MyAlerterFlavor and the MyAlerterConfig are implemented in a different module/path than the actual MyAlerter).
PreviousSlack Alerter
NextImage Builders
Last updated 15 days ago |
Where can I find the URL to the UI of a remote orchestrator for a pipeline run in ZenML? |
g, and cached.
status = run.status
ConfigurationThe pipeline_configuration is an object that contains all configurations of the pipeline and pipeline run, including the pipeline-level settings, which we will learn more about later:
pipeline_config = run.config
pipeline_settings = run.config.settings
Component-Specific metadata
Depending on the stack components you use, you might have additional component-specific metadata associated with your run, such as the URL to the UI of a remote orchestrator. You can access this component-specific metadata via the run_metadata attribute:
run_metadata = run.run_metadata
# The following only works for runs on certain remote orchestrators
orchestrator_url = run_metadata["orchestrator_url"].value
## Steps
Within a given pipeline run you can now further zoom in on individual steps using the steps attribute:
```python
# get all steps of a pipeline for a given run
steps = run.steps
# get a specific step by its invocation ID
step = run.steps["first_step"]
If you're only calling each step once inside your pipeline, the invocation ID will be the same as the name of your step. For more complex pipelines, check out this page to learn more about the invocation ID.
Inspect pipeline runs with our VS Code extension
If you are using our VS Code extension, you can easily view your pipeline runs by opening the sidebar (click on the ZenML icon). You can then click on any particular pipeline run to see its status and some other metadata. If you want to delete a run, you can also do so from the same sidebar view.
Step information
Similar to the run, you can use the step object to access a variety of useful information:
The parameters used to run the step via step.config.parameters,
The step-level settings via step.config.settings,
Component-specific step metadata, such as the URL of an experiment tracker or model deployer, via step.run_metadata
See the StepRunResponse definition for a comprehensive list of available information.
Artifacts |
- Loss:
MatryoshkaLoss with these parameters:{
"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: epochper_device_train_batch_size: 32per_device_eval_batch_size: 16gradient_accumulation_steps: 16learning_rate: 2e-05num_train_epochs: 4lr_scheduler_type: cosinewarmup_ratio: 0.1bf16: Truetf32: Trueload_best_model_at_end: Trueoptim: adamw_torch_fusedbatch_sampler: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir: Falsedo_predict: Falseeval_strategy: epochprediction_loss_only: Trueper_device_train_batch_size: 32per_device_eval_batch_size: 16per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 16eval_accumulation_steps: Nonelearning_rate: 2e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1.0num_train_epochs: 4max_steps: -1lr_scheduler_type: cosinelr_scheduler_kwargs: {}warmup_ratio: 0.1warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseno_cuda: Falseuse_cpu: Falseuse_mps_device: Falseseed: 42data_seed: Nonejit_mode_eval: Falseuse_ipex: Falsebf16: Truefp16: Falsefp16_opt_level: O1half_precision_backend: autobf16_full_eval: Falsefp16_full_eval: Falsetf32: Truelocal_rank: 0ddp_backend: Nonetpu_num_cores: Nonetpu_metrics_debug: Falsedebug: []dataloader_drop_last: Falsedataloader_num_workers: 0dataloader_prefetch_factor: Nonepast_index: -1disable_tqdm: Trueremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Trueignore_data_skip: Falsefsdp: []fsdp_min_num_params: 0fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap: Noneaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torch_fusedoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Trueuse_legacy_prediction_loop: Falsepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Falsehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseeval_do_concat_batches: Truefp16_backend: autopush_to_hub_model_id: Nonepush_to_hub_organization: Nonemp_parameters: auto_find_batch_size: Falsefull_determinism: Falsetorchdynamo: Noneray_scope: lastddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Nonedispatch_batches: Nonesplit_batches: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falsebatch_sampler: no_duplicatesmulti_dataset_batch_sampler: proportional
Training Logs
| Epoch |
Step |
dim_128_cosine_map@100 |
dim_256_cosine_map@100 |
dim_384_cosine_map@100 |
dim_64_cosine_map@100 |
| 0.6667 |
1 |
0.4215 |
0.4509 |
0.4878 |
0.3203 |
| 2.0 |
3 |
0.4835 |
0.5278 |
0.5582 |
0.4141 |
| 2.6667 |
4 |
0.4961 |
0.531 |
0.5473 |
0.4209 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.1+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1
Citation
BibTeX
Sentence Transformers
@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
@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
@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}
}
