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:TripletLoss
widget:
- source_sentence: >-
Where is the global configuration directory located in ZenML's default
setup?
sentences:
- >-
'default' ...
Creating default user 'default' ...Creating default stack for user
'default' in workspace default...
Active workspace not set. Setting it to the default.
The active stack is not set. Setting the active stack to the default
workspace stack.
Using the default store for the global config.
Unable to find ZenML repository in your current working directory
(/tmp/folder) or any parent directories. If you want to use an existing
repository which is in a different location, set the environment
variable 'ZENML_REPOSITORY_PATH'. If you want to create a new
repository, run zenml init.
Running without an active repository root.
Using the default local database.
Running with active workspace: 'default' (global)
┏━━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━┯━━━━━━━━━┯━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━┓
┃ ACTIVE │ STACK NAME │ SHARED │ OWNER │ ARTIFACT_STORE │ ORCHESTRATOR
┃
┠────────┼────────────┼────────┼─────────┼────────────────┼──────────────┨
┃ 👉 │ default │ ❌ │ default │ default │ default
┃
┗━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━┷━━━━━━━━━┷━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━┛
The following is an example of the layout of the global config directory
immediately after initialization:
/home/stefan/.config/zenml <- Global Config Directory
├── config.yaml <- Global Configuration Settings
└── local_stores <- Every Stack component that stores
information
| locally will have its own subdirectory here.
├── a1a0d3d0-d552-4a80-be09-67e5e29be8ee <- e.g. Local Store path for
the
| `default` local Artifact
Store
└── default_zen_store
└── zenml.db <- SQLite database where ZenML data (stacks,
components, etc) are stored by default.
As shown above, the global config directory stores the following
information:
- How do you configure the network settings on a Linux server?
- >-
Reranking for better retrieval
Add reranking to your RAG inference for better retrieval performance.
Rerankers are a crucial component of retrieval systems that use LLMs.
They help improve the quality of the retrieved documents by reordering
them based on additional features or scores. In this section, we'll
explore how to add a reranker to your RAG inference pipeline in ZenML.
In previous sections, we set up the overall workflow, from data
ingestion and preprocessing to embeddings generation and retrieval. We
then set up some basic evaluation metrics to assess the performance of
our retrieval system. A reranker is a way to squeeze a bit of extra
performance out of the system by reordering the retrieved documents
based on additional features or scores.
As you can see, reranking is an optional addition we make to what we've
already set up. It's not strictly necessary, but it can help improve the
relevance and quality of the retrieved documents, which in turn can lead
to better responses from the LLM. Let's dive in!
PreviousEvaluation in practice
NextUnderstanding reranking
Last updated 1 month ago
- source_sentence: >-
Where can I find the instructions to enable CUDA for GPU-backed hardware
in ZenML SDK Docs?
sentences:
- >-
Migration guide 0.39.1 → 0.41.0
How to migrate your ZenML pipelines and steps from version <=0.39.1 to
0.41.0.
ZenML versions 0.40.0 to 0.41.0 introduced a new and more flexible
syntax to define ZenML steps and pipelines. This page contains code
samples that show you how to upgrade your steps and pipelines to the new
syntax.
Newer versions of ZenML still work with pipelines and steps defined
using the old syntax, but the old syntax is deprecated and will be
removed in the future.
Overview
from typing import Optional
from zenml.steps import BaseParameters, Output, StepContext, step
from zenml.pipelines import pipeline
class MyStepParameters(BaseParameters):
param_1: int
param_2: Optional[float] = None
@step
def my_step(
params: MyStepParameters, context: StepContext,
) -> Output(int_output=int, str_output=str):
result = int(params.param_1 * (params.param_2 or 1))
result_uri = context.get_output_artifact_uri()
return result, result_uri
my_step.entrypoint()
@pipeline
def my_pipeline(my_step):
my_step()
step_instance = my_step(params=MyStepParameters(param_1=17))
pipeline_instance = my_pipeline(my_step=step_instance)
pipeline_instance.configure(enable_cache=False)
schedule = Schedule(...)
pipeline_instance.run(schedule=schedule)
last_run = pipeline_instance.get_runs()[0]
int_output = last_run.get_step["my_step"].outputs["int_output"].read()
from typing import Annotated, Optional, Tuple
from zenml import get_step_context, pipeline, step
from zenml.client import Client
@step
def my_step(
param_1: int, param_2: Optional[float] = None
) -> Tuple[Annotated[int, "int_output"], Annotated[str, "str_output"]]:
result = int(param_1 * (param_2 or 1))
result_uri = get_step_context().get_output_artifact_uri()
return result, result_uri
my_step()
@pipeline
- >-
How do I integrate Google Cloud VertexAI into my existing Kubernetes
cluster?
- >2-
SDK Docs .
Enabling CUDA for GPU-backed hardwareNote that if you wish to use this
step operator to run steps on a GPU, you will need to follow the
instructions on this page to ensure that it works. It requires adding
some extra settings customization and is essential to enable CUDA for
the GPU to give its full acceleration.
PreviousStep Operators
NextGoogle Cloud VertexAI
Last updated 19 days ago
- source_sentence: >-
What are the special metadata types supported by ZenML and how are they
used?
sentences:
- >-
Special Metadata Types
Tracking your metadata.
ZenML supports several special metadata types to capture specific kinds
of information. Here are examples of how to use the special types Uri,
Path, DType, and StorageSize:
from zenml.metadata.metadata_types import StorageSize, DType
from zenml import log_artifact_metadata
log_artifact_metadata(
metadata={
"dataset_source": Uri("gs://my-bucket/datasets/source.csv"),
"preprocessing_script": Path("/scripts/preprocess.py"),
"column_types": {
"age": DType("int"),
"income": DType("float"),
"score": DType("int")
},
"processed_data_size": StorageSize(2500000)
In this example:
Uri is used to indicate a dataset source URI.
Path is used to specify the filesystem path to a preprocessing script.
DType is used to describe the data types of specific columns.
StorageSize is used to indicate the size of the processed data in bytes.
These special types help standardize the format of metadata and ensure
that it is logged in a consistent and interpretable manner.
PreviousGroup metadata
NextFetch metadata within steps
Last updated 19 days ago
- >-
Configure a code repository
Connect a Git repository to ZenML to track code changes and collaborate
on MLOps projects.
Throughout the lifecycle of a MLOps pipeline, it can get quite tiresome
to always wait for a Docker build every time after running a pipeline
(even if the local Docker cache is used). However, there is a way to
just have one pipeline build and keep reusing it until a change to the
pipeline environment is made: by connecting a code repository.
With ZenML, connecting to a Git repository optimizes the Docker build
processes. It also has the added bonus of being a better way of managing
repository changes and enabling better code collaboration. Here is how
the flow changes when running a pipeline:
You trigger a pipeline run on your local machine. ZenML parses the
@pipeline function to determine the necessary steps.
The local client requests stack information from the ZenML server, which
responds with the cloud stack configuration.
The local client detects that we're using a code repository and requests
the information from the git repo.
Instead of building a new Docker image, the client checks if an existing
image can be reused based on the current Git commit hash and other
environment metadata.
The client initiates a run in the orchestrator, which sets up the
execution environment in the cloud, such as a VM.
The orchestrator downloads the code directly from the Git repository and
uses the existing Docker image to run the pipeline steps.
Pipeline steps execute, storing artifacts in the cloud-based artifact
store.
Throughout the execution, the pipeline run status and metadata are
reported back to the ZenML server.
By connecting a Git repository, you avoid redundant builds and make your
MLOps processes more efficient. Your team can work on the codebase
simultaneously, with ZenML handling the version tracking and ensuring
that the correct code version is always used for each run.
Creating a GitHub Repository
- >-
Can you explain the process of setting up a virtual environment in
Python?
- source_sentence: >-
What are the benefits of deploying stack components directly from the
ZenML CLI?
sentences:
- >-
─────────────────────────────────────────────────┨┃ RESOURCE TYPES │
🔵 gcp-generic, 📦 gcs-bucket, 🌀 kubernetes-cluster, 🐳 docker-registry
┃
┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
┃ RESOURCE NAME │
<multiple>
┃
┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
┃ SECRET ID │
4694de65-997b-4929-8831-b49d5e067b97
┃
┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
┃ SESSION DURATION │
N/A
┃
┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
┃ EXPIRES IN │
59m46s
┃
┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
┃ OWNER │
default
┃
┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
┃ WORKSPACE │
default
┃
┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
┃ SHARED │
➖
┃
┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
┃ CREATED_AT │ 2023-05-19
09:04:33.557126 ┃
┠──────────────────┼──────────────────────────────────────────────────────────────────────────┨
┃ UPDATED_AT │ 2023-05-19
09:04:33.557127 ┃
┗━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Configuration
┏━━━━━━━━━━━━┯━━━━━━━━━━━━┓
- How do you set up a custom service account for Vertex AI?
- >-
⚒️Manage stacks
Deploying your stack components directly from the ZenML CLI
The first step in running your pipelines on remote infrastructure is to
deploy all the components that you would need, like an MLflow tracking
server, a Seldon Core model deployer, and more to your cloud.
This can bring plenty of benefits like scalability, reliability, and
collaboration. ZenML eases the path to production by providing a
seamless way for all tools to interact with others through the use of
abstractions. However, one of the most painful parts of this process,
from what we see on our Slack and in general, is the deployment of these
stack components.
Deploying and managing MLOps tools is tricky 😭😵💫
It is not trivial to set up all the different tools that you might need
for your pipeline.
🌈 Each tool comes with a certain set of requirements. For example, a
Kubeflow installation will require you to have a Kubernetes cluster, and
so would a Seldon Core deployment.
🤔 Figuring out the defaults for infra parameters is not easy. Even if
you have identified the backing infra that you need for a stack
component, setting up reasonable defaults for parameters like instance
size, CPU, memory, etc., needs a lot of experimentation to figure out.
🚧 Many times, standard tool installations don't work out of the box.
For example, to run a custom pipeline in Vertex AI, it is not enough to
just run an imported pipeline. You might also need a custom service
account that is configured to perform tasks like reading secrets from
your secret store or talking to other GCP services that your pipeline
might need.
🔐 Some tools need an additional layer of installations to enable a more
secure, production-grade setup. For example, a standard MLflow tracking
server deployment comes without an authentication frontend which might
expose all of your tracking data to the world if deployed as-is.
- source_sentence: >-
What is the expiration time for the GCP OAuth2 token in the ZenML
configuration?
sentences:
- >-
━━━━━┛
Configuration
┏━━━━━━━━━━━━┯━━━━━━━━━━━━┓┃ PROPERTY │ VALUE ┃
┠────────────┼────────────┨
┃ project_id │ zenml-core ┃
┠────────────┼────────────┨
┃ token │ [HIDDEN] ┃
┗━━━━━━━━━━━━┷━━━━━━━━━━━━┛
Note the temporary nature of the Service Connector. It will expire and
become unusable in 1 hour:
zenml service-connector list --name gcp-oauth2-token
Example Command Output
┏━━━━━━━━┯━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┯━━━━━━━━┯━━━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━┓
┃ ACTIVE │ NAME │ ID │
TYPE │ RESOURCE TYPES │ RESOURCE NAME │ SHARED │ OWNER │
EXPIRES IN │ LABELS ┃
┠────────┼──────────────────┼──────────────────────────────────────┼────────┼───────────────────────┼───────────────┼────────┼─────────┼────────────┼────────┨
┃ │ gcp-oauth2-token │ ec4d7d85-c71c-476b-aa76-95bf772c90da │ 🔵
gcp │ 🔵 gcp-generic │ <multiple> │ ➖ │ default │
59m35s │ ┃
┃ │ │
│ │ 📦 gcs-bucket │ │ │
│ │ ┃
┃ │ │
│ │ 🌀 kubernetes-cluster │ │ │
│ │ ┃
┃ │ │
│ │ 🐳 docker-registry │ │ │
│ │ ┃
┗━━━━━━━━┷━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━┷━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━┛
Auto-configuration
The GCP Service Connector allows auto-discovering and fetching
credentials and configuration set up by the GCP CLI on your local host.
- >-
Hugging Face
Deploying models to Huggingface Inference Endpoints with Hugging Face
:hugging_face:.
Hugging Face Inference Endpoints provides a secure production solution
to easily deploy any transformers, sentence-transformers, and diffusers
models on a dedicated and autoscaling infrastructure managed by Hugging
Face. An Inference Endpoint is built from a model from the Hub.
This service provides dedicated and autoscaling infrastructure managed
by Hugging Face, allowing you to deploy models without dealing with
containers and GPUs.
When to use it?
You should use Hugging Face Model Deployer:
if you want to deploy Transformers, Sentence-Transformers, or Diffusion
models on dedicated and secure infrastructure.
if you prefer a fully-managed production solution for inference without
the need to handle containers and GPUs.
if your goal is to turn your models into production-ready APIs with
minimal infrastructure or MLOps involvement
Cost-effectiveness is crucial, and you want to pay only for the raw
compute resources you use.
Enterprise security is a priority, and you need to deploy models into
secure offline endpoints accessible only via a direct connection to your
Virtual Private Cloud (VPCs).
If you are looking for a more easy way to deploy your models locally,
you can use the MLflow Model Deployer flavor.
How to deploy it?
The Hugging Face Model Deployer flavor is provided by the Hugging Face
ZenML integration, so you need to install it on your local machine to be
able to deploy your models. You can do this by running the following
command:
zenml integration install huggingface -y
To register the Hugging Face model deployer with ZenML you need to run
the following command:
zenml model-deployer register <MODEL_DEPLOYER_NAME> --flavor=huggingface
--token=<YOUR_HF_TOKEN> --namespace=<YOUR_HF_NAMESPACE>
Here,
token parameter is the Hugging Face authentication token. It can be
managed through Hugging Face settings.
- >-
Can you list the steps to set up a Docker registry on a Kubernetes
cluster?
model-index:
- name: zenml/finetuned-snowflake-arctic-embed-m
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 384
type: dim_384
metrics:
- type: cosine_accuracy@1
value: 0.29518072289156627
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5240963855421686
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5843373493975904
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6867469879518072
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.29518072289156627
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.17469879518072293
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11686746987951804
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0686746987951807
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.29518072289156627
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5240963855421686
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5843373493975904
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6867469879518072
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4908042072911187
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.42844234079173843
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.43576329240226386
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.25903614457831325
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5060240963855421
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5783132530120482
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6445783132530121
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.25903614457831325
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1686746987951807
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11566265060240961
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0644578313253012
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.25903614457831325
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5060240963855421
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5783132530120482
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6445783132530121
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4548319777111225
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.39346194301013593
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.40343211538391555
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.2710843373493976
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.46987951807228917
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5662650602409639
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6144578313253012
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.2710843373493976
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1566265060240964
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11325301204819276
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.061445783132530116
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.2710843373493976
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.46987951807228917
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5662650602409639
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6144578313253012
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.44433019669319024
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3893574297188756
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3989315479842741
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.21686746987951808
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42168674698795183
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5180722891566265
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5843373493975904
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.21686746987951808
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.14056224899598396
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10361445783132528
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05843373493975902
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.21686746987951808
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.42168674698795183
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5180722891566265
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5843373493975904
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.39639025659520544
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3364529546758464
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.34658882510541217
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 expiration time for the GCP OAuth2 token in the ZenML configuration?',
'━━━━━┛\n\nConfiguration\n\n┏━━━━━━━━━━━━┯━━━━━━━━━━━━┓┃ PROPERTY │ VALUE ┃\n\n┠────────────┼────────────┨\n\n┃ project_id │ zenml-core ┃\n\n┠────────────┼────────────┨\n\n┃ token │ [HIDDEN] ┃\n\n┗━━━━━━━━━━━━┷━━━━━━━━━━━━┛\n\nNote the temporary nature of the Service Connector. It will expire and become unusable in 1 hour:\n\nzenml service-connector list --name gcp-oauth2-token\n\nExample Command Output\n\n┏━━━━━━━━┯━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━┯━━━━━━━━┯━━━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━┓\n\n┃ ACTIVE │ NAME │ ID │ TYPE │ RESOURCE TYPES │ RESOURCE NAME │ SHARED │ OWNER │ EXPIRES IN │ LABELS ┃\n\n┠────────┼──────────────────┼──────────────────────────────────────┼────────┼───────────────────────┼───────────────┼────────┼─────────┼────────────┼────────┨\n\n┃ │ gcp-oauth2-token │ ec4d7d85-c71c-476b-aa76-95bf772c90da │ 🔵 gcp │ 🔵 gcp-generic │ <multiple> │ ➖ │ default │ 59m35s │ ┃\n\n┃ │ │ │ │ 📦 gcs-bucket │ │ │ │ │ ┃\n\n┃ │ │ │ │ 🌀 kubernetes-cluster │ │ │ │ │ ┃\n\n┃ │ │ │ │ 🐳 docker-registry │ │ │ │ │ ┃\n\n┗━━━━━━━━┷━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━┷━━━━━━━━┷━━━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━┛\n\nAuto-configuration\n\nThe GCP Service Connector allows auto-discovering and fetching credentials and configuration set up by the GCP CLI on your local host.',
'Can you list the steps to set up a Docker registry on a Kubernetes cluster?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
Evaluation
Metrics
Information Retrieval
| Metric |
Value |
| cosine_accuracy@1 |
0.2952 |
| cosine_accuracy@3 |
0.5241 |
| cosine_accuracy@5 |
0.5843 |
| cosine_accuracy@10 |
0.6867 |
| cosine_precision@1 |
0.2952 |
| cosine_precision@3 |
0.1747 |
| cosine_precision@5 |
0.1169 |
| cosine_precision@10 |
0.0687 |
| cosine_recall@1 |
0.2952 |
| cosine_recall@3 |
0.5241 |
| cosine_recall@5 |
0.5843 |
| cosine_recall@10 |
0.6867 |
| cosine_ndcg@10 |
0.4908 |
| cosine_mrr@10 |
0.4284 |
| cosine_map@100 |
0.4358 |
Information Retrieval
| Metric |
Value |
| cosine_accuracy@1 |
0.259 |
| cosine_accuracy@3 |
0.506 |
| cosine_accuracy@5 |
0.5783 |
| cosine_accuracy@10 |
0.6446 |
| cosine_precision@1 |
0.259 |
| cosine_precision@3 |
0.1687 |
| cosine_precision@5 |
0.1157 |
| cosine_precision@10 |
0.0645 |
| cosine_recall@1 |
0.259 |
| cosine_recall@3 |
0.506 |
| cosine_recall@5 |
0.5783 |
| cosine_recall@10 |
0.6446 |
| cosine_ndcg@10 |
0.4548 |
| cosine_mrr@10 |
0.3935 |
| cosine_map@100 |
0.4034 |
Information Retrieval
| Metric |
Value |
| cosine_accuracy@1 |
0.2711 |
| cosine_accuracy@3 |
0.4699 |
| cosine_accuracy@5 |
0.5663 |
| cosine_accuracy@10 |
0.6145 |
| cosine_precision@1 |
0.2711 |
| cosine_precision@3 |
0.1566 |
| cosine_precision@5 |
0.1133 |
| cosine_precision@10 |
0.0614 |
| cosine_recall@1 |
0.2711 |
| cosine_recall@3 |
0.4699 |
| cosine_recall@5 |
0.5663 |
| cosine_recall@10 |
0.6145 |
| cosine_ndcg@10 |
0.4443 |
| cosine_mrr@10 |
0.3894 |
| cosine_map@100 |
0.3989 |
Information Retrieval
| Metric |
Value |
| cosine_accuracy@1 |
0.2169 |
| cosine_accuracy@3 |
0.4217 |
| cosine_accuracy@5 |
0.5181 |
| cosine_accuracy@10 |
0.5843 |
| cosine_precision@1 |
0.2169 |
| cosine_precision@3 |
0.1406 |
| cosine_precision@5 |
0.1036 |
| cosine_precision@10 |
0.0584 |
| cosine_recall@1 |
0.2169 |
| cosine_recall@3 |
0.4217 |
| cosine_recall@5 |
0.5181 |
| cosine_recall@10 |
0.5843 |
| cosine_ndcg@10 |
0.3964 |
| cosine_mrr@10 |
0.3365 |
| cosine_map@100 |
0.3466 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 1,490 training samples
- Columns:
positive, anchor, and negative
- Approximate statistics based on the first 1000 samples:
|
positive |
anchor |
negative |
| type |
string |
string |
string |
| details |
- min: 9 tokens
- mean: 21.02 tokens
- max: 64 tokens
|
- min: 23 tokens
- mean: 375.16 tokens
- max: 512 tokens
|
- min: 10 tokens
- mean: 17.51 tokens
- max: 31 tokens
|
- Samples:
| positive |
anchor |
negative |
What details can you provide about the mlflow_training_pipeline runs listed in the ZenML documentation? |
mlflow_training_pipeline', ┃┃ │ │ │ 'zenml_pipeline_run_uuid': 'a5d4faae-ef70-48f2-9893-6e65d5e51e98', 'zenml_workspace': '10e060b3-2f7e-463d-9ec8-3a211ef4e1f6', 'epochs': '5', 'optimizer': 'Adam', 'lr': '0.005'} ┃
┠────────────────────────┼───────────────┼─────────────────────────────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┨
┃ tensorflow-mnist-model │ 2 │ Run #2 of the mlflow_training_pipeline. │ {'zenml_version': '0.34.0', 'zenml_run_name': 'mlflow_training_pipeline-2023_03_01-08_09_08_467212', 'zenml_pipeline_name': 'mlflow_training_pipeline', ┃
┃ │ │ │ 'zenml_pipeline_run_uuid': '11858dcf-3e47-4b1a-82c5-6fa25ba4e037', 'zenml_workspace': '10e060b3-2f7e-463d-9ec8-3a211ef4e1f6', 'epochs': '5', 'optimizer': 'Adam', 'lr': '0.003'} ┃
┠────────────────────────┼───────────────┼─────────────────────────────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┨
┃ tensorflow-mnist-model │ 1 │ Run #1 of the mlflow_training_pipeline. │ {'zenml_version': '0.34.0', 'zenml_run_name': 'mlflow_training_pipeline-2023_03_01-08_08_52_398499', 'zenml_pipeline_name': 'mlflow_training_pipeline', ┃
┃ │ │ │ 'zenml_pipeline_run_uuid': '29fb22c1-6e0b-4431-9e04-226226506d16', 'zenml_workspace': '10e060b3-2f7e-463d-9ec8-3a211ef4e1f6', 'epochs': '5', 'optimizer': 'Adam', 'lr': '0.001'} ┃ |
Can you explain how to configure the TensorFlow settings for a different project? |
How do you register a GCP Service Connector that uses account impersonation to access the zenml-bucket-sl GCS bucket? |
esource-id zenml-bucket-sl
Example Command OutputError: Service connector 'gcp-empty-sa' verification failed: connector authorization failure: failed to fetch GCS bucket
zenml-bucket-sl: 403 GET https://storage.googleapis.com/storage/v1/b/zenml-bucket-sl?projection=noAcl&prettyPrint=false:
empty-connectors@zenml-core.iam.gserviceaccount.com does not have storage.buckets.get access to the Google Cloud Storage bucket.
Permission 'storage.buckets.get' denied on resource (or it may not exist).
Next, we'll register a GCP Service Connector that actually uses account impersonation to access the zenml-bucket-sl GCS bucket and verify that it can actually access the bucket:
zenml service-connector register gcp-impersonate-sa --type gcp --auth-method impersonation --service_account_json=@empty-connectors@zenml-core.json --project_id=zenml-core --target_principal=zenml-bucket-sl@zenml-core.iam.gserviceaccount.com --resource-type gcs-bucket --resource-id gs://zenml-bucket-sl
Example Command Output
Expanding argument value service_account_json to contents of file /home/stefan/aspyre/src/zenml/empty-connectors@zenml-core.json.
Successfully registered service connector gcp-impersonate-sa with access to the following resources:
┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━┓
┃ RESOURCE TYPE │ RESOURCE NAMES ┃
┠───────────────┼──────────────────────┨
┃ 📦 gcs-bucket │ gs://zenml-bucket-sl ┃
┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━┛
External Account (GCP Workload Identity)
Use GCP workload identity federation to authenticate to GCP services using AWS IAM credentials, Azure Active Directory credentials or generic OIDC tokens. |
What is the process for setting up a ZenML pipeline using AWS IAM credentials? |
Can you explain how data validation helps in detecting data drift and model drift in ZenML pipelines? |
of your models at different stages of development.if you have pipelines that regularly ingest new data, you should use data validation to run regular data integrity checks to signal problems before they are propagated downstream.
in continuous training pipelines, you should use data validation techniques to compare new training data against a data reference and to compare the performance of newly trained models against previous ones.
when you have pipelines that automate batch inference or if you regularly collect data used as input in online inference, you should use data validation to run data drift analyses and detect training-serving skew, data drift and model drift.
Data Validator Flavors
Data Validator are optional stack components provided by integrations. The following table lists the currently available Data Validators and summarizes their features and the data types and model types that they can be used with in ZenML pipelines:
Data Validator Validation Features Data Types Model Types Notes Flavor/Integration Deepchecks data quality
data drift
model drift
model performance tabular: pandas.DataFrame CV: torch.utils.data.dataloader.DataLoader tabular: sklearn.base.ClassifierMixin CV: torch.nn.Module Add Deepchecks data and model validation tests to your pipelines deepchecks Evidently data quality
data drift
model drift
model performance tabular: pandas.DataFrame N/A Use Evidently to generate a variety of data quality and data/model drift reports and visualizations evidently Great Expectations data profiling
data quality tabular: pandas.DataFrame N/A Perform data testing, documentation and profiling with Great Expectations great_expectations Whylogs/WhyLabs data drift tabular: pandas.DataFrame N/A Generate data profiles with whylogs and upload them to WhyLabs whylogs
If you would like to see the available flavors of Data Validator, you can use the command:
zenml data-validator flavor list
How to use it |
What are the best practices for deploying web applications using Docker and Kubernetes? |
- Loss:
MatryoshkaLoss with these parameters:{
"loss": "TripletLoss",
"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 |
0.3884 |
0.4332 |
0.4464 |
0.3140 |
| 2.0 |
3 |
0.4064 |
0.4195 |
0.4431 |
0.3553 |
| 2.6667 |
4 |
0.3989 |
0.4034 |
0.4358 |
0.3466 |
- The bold row denotes the saved checkpoint.
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}
}
TripletLoss
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
