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finetuned-snowflake-arctic-embed-m

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:1490
loss:MatryoshkaLoss
loss:TripletLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
finetuned-snowflake-arctic-embed-m / README.md
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---
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
# Define a Step
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
# Run the Step separately
my_step.entrypoint()
# Define a Pipeline
@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)
# Configure and run the Pipeline
pipeline_instance.configure(enable_cache=False)
schedule = Schedule(...)
pipeline_instance.run(schedule=schedule)
# Fetch the Pipeline Run
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
# Define a Step
@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
# Run the Step separately
my_step()
# Define a Pipeline
@pipeline'
- How do I integrate Google Cloud VertexAI into my existing Kubernetes cluster?
- ' SDK Docs .
Enabling CUDA for GPU-backed hardwareNote that if you wish to use this step operator
to run steps on a GPU, you will need to follow the instructions on this page to
ensure that it works. It requires adding some extra settings customization and
is essential to enable CUDA for the GPU to give its full acceleration.
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](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/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](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision 71bc94c8f9ea1e54fba11167004205a65e5da2cc -->
- **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")
# Run inference
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)
# [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 | 0.2952 |
| cosine_accuracy@3 | 0.5241 |
| cosine_accuracy@5 | 0.5843 |
| cosine_accuracy@10 | 0.6867 |
| cosine_precision@1 | 0.2952 |
| cosine_precision@3 | 0.1747 |
| cosine_precision@5 | 0.1169 |
| cosine_precision@10 | 0.0687 |
| cosine_recall@1 | 0.2952 |
| cosine_recall@3 | 0.5241 |
| cosine_recall@5 | 0.5843 |
| cosine_recall@10 | 0.6867 |
| cosine_ndcg@10 | 0.4908 |
| cosine_mrr@10 | 0.4284 |
| **cosine_map@100** | **0.4358** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.259 |
| cosine_accuracy@3 | 0.506 |
| cosine_accuracy@5 | 0.5783 |
| cosine_accuracy@10 | 0.6446 |
| cosine_precision@1 | 0.259 |
| cosine_precision@3 | 0.1687 |
| cosine_precision@5 | 0.1157 |
| cosine_precision@10 | 0.0645 |
| cosine_recall@1 | 0.259 |
| cosine_recall@3 | 0.506 |
| cosine_recall@5 | 0.5783 |
| cosine_recall@10 | 0.6446 |
| cosine_ndcg@10 | 0.4548 |
| cosine_mrr@10 | 0.3935 |
| **cosine_map@100** | **0.4034** |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.2711 |
| cosine_accuracy@3 | 0.4699 |
| cosine_accuracy@5 | 0.5663 |
| cosine_accuracy@10 | 0.6145 |
| cosine_precision@1 | 0.2711 |
| cosine_precision@3 | 0.1566 |
| cosine_precision@5 | 0.1133 |
| cosine_precision@10 | 0.0614 |
| cosine_recall@1 | 0.2711 |
| cosine_recall@3 | 0.4699 |
| cosine_recall@5 | 0.5663 |
| cosine_recall@10 | 0.6145 |
| cosine_ndcg@10 | 0.4443 |
| cosine_mrr@10 | 0.3894 |
| **cosine_map@100** | **0.3989** |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.2169 |
| cosine_accuracy@3 | 0.4217 |
| cosine_accuracy@5 | 0.5181 |
| cosine_accuracy@10 | 0.5843 |
| cosine_precision@1 | 0.2169 |
| cosine_precision@3 | 0.1406 |
| cosine_precision@5 | 0.1036 |
| cosine_precision@10 | 0.0584 |
| cosine_recall@1 | 0.2169 |
| cosine_recall@3 | 0.4217 |
| cosine_recall@5 | 0.5181 |
| cosine_recall@10 | 0.5843 |
| cosine_ndcg@10 | 0.3964 |
| cosine_mrr@10 | 0.3365 |
| **cosine_map@100** | **0.3466** |
<!--
## Bias, Risks and Limitations
*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: 1,490 training samples
* Columns: <code>positive</code>, <code>anchor</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor | negative |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 21.02 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 375.16 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 17.51 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
| positive | anchor | negative |
|:-----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------|
| <code>What details can you provide about the mlflow_training_pipeline runs listed in the ZenML documentation?</code> | <code>mlflow_training_pipeline', ┃┃ │ │ │ 'zenml_pipeline_run_uuid': 'a5d4faae-ef70-48f2-9893-6e65d5e51e98', 'zenml_workspace': '10e060b3-2f7e-463d-9ec8-3a211ef4e1f6', 'epochs': '5', 'optimizer': 'Adam', 'lr': '0.005'} ┃<br><br>┠────────────────────────┼───────────────┼─────────────────────────────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┨<br><br>┃ tensorflow-mnist-model │ 2 │ Run #2 of the mlflow_training_pipeline. │ {'zenml_version': '0.34.0', 'zenml_run_name': 'mlflow_training_pipeline-2023_03_01-08_09_08_467212', 'zenml_pipeline_name': 'mlflow_training_pipeline', ┃<br><br>┃ │ │ │ 'zenml_pipeline_run_uuid': '11858dcf-3e47-4b1a-82c5-6fa25ba4e037', 'zenml_workspace': '10e060b3-2f7e-463d-9ec8-3a211ef4e1f6', 'epochs': '5', 'optimizer': 'Adam', 'lr': '0.003'} ┃<br><br>┠────────────────────────┼───────────────┼─────────────────────────────────────────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┨<br><br>┃ tensorflow-mnist-model │ 1 │ Run #1 of the mlflow_training_pipeline. │ {'zenml_version': '0.34.0', 'zenml_run_name': 'mlflow_training_pipeline-2023_03_01-08_08_52_398499', 'zenml_pipeline_name': 'mlflow_training_pipeline', ┃<br><br>┃ │ │ │ 'zenml_pipeline_run_uuid': '29fb22c1-6e0b-4431-9e04-226226506d16', 'zenml_workspace': '10e060b3-2f7e-463d-9ec8-3a211ef4e1f6', 'epochs': '5', 'optimizer': 'Adam', 'lr': '0.001'} ┃</code> | <code>Can you explain how to configure the TensorFlow settings for a different project?</code> |
| <code>How do you register a GCP Service Connector that uses account impersonation to access the zenml-bucket-sl GCS bucket?</code> | <code>esource-id zenml-bucket-sl<br><br>Example Command OutputError: Service connector 'gcp-empty-sa' verification failed: connector authorization failure: failed to fetch GCS bucket<br><br>zenml-bucket-sl: 403 GET https://storage.googleapis.com/storage/v1/b/zenml-bucket-sl?projection=noAcl&prettyPrint=false:<br><br>[email protected] does not have storage.buckets.get access to the Google Cloud Storage bucket.<br><br>Permission 'storage.buckets.get' denied on resource (or it may not exist).<br><br>Next, we'll register a GCP Service Connector that actually uses account impersonation to access the zenml-bucket-sl GCS bucket and verify that it can actually access the bucket:<br><br>zenml service-connector register gcp-impersonate-sa --type gcp --auth-method impersonation --service_account_json=@[email protected] --project_id=zenml-core --target[email protected] --resource-type gcs-bucket --resource-id gs://zenml-bucket-sl<br><br>Example Command Output<br><br>Expanding argument value service_account_json to contents of file /home/stefan/aspyre/src/zenml/[email protected].<br><br>Successfully registered service connector `gcp-impersonate-sa` with access to the following resources:<br><br>┏━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━┓<br><br>┃ RESOURCE TYPE │ RESOURCE NAMES ┃<br><br>┠───────────────┼──────────────────────┨<br><br>┃ 📦 gcs-bucket │ gs://zenml-bucket-sl ┃<br><br>┗━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━┛<br><br>External Account (GCP Workload Identity)<br><br>Use GCP workload identity federation to authenticate to GCP services using AWS IAM credentials, Azure Active Directory credentials or generic OIDC tokens.</code> | <code>What is the process for setting up a ZenML pipeline using AWS IAM credentials?</code> |
| <code>Can you explain how data validation helps in detecting data drift and model drift in ZenML pipelines?</code> | <code>of your models at different stages of development.if you have pipelines that regularly ingest new data, you should use data validation to run regular data integrity checks to signal problems before they are propagated downstream.<br><br>in continuous training pipelines, you should use data validation techniques to compare new training data against a data reference and to compare the performance of newly trained models against previous ones.<br><br>when you have pipelines that automate batch inference or if you regularly collect data used as input in online inference, you should use data validation to run data drift analyses and detect training-serving skew, data drift and model drift.<br><br>Data Validator Flavors<br><br>Data Validator are optional stack components provided by integrations. The following table lists the currently available Data Validators and summarizes their features and the data types and model types that they can be used with in ZenML pipelines:<br><br>Data Validator Validation Features Data Types Model Types Notes Flavor/Integration Deepchecks data quality<br>data drift<br>model drift<br>model performance tabular: pandas.DataFrame CV: torch.utils.data.dataloader.DataLoader tabular: sklearn.base.ClassifierMixin CV: torch.nn.Module Add Deepchecks data and model validation tests to your pipelines deepchecks Evidently data quality<br>data drift<br>model drift<br>model performance tabular: pandas.DataFrame N/A Use Evidently to generate a variety of data quality and data/model drift reports and visualizations evidently Great Expectations data profiling<br>data quality tabular: pandas.DataFrame N/A Perform data testing, documentation and profiling with Great Expectations great_expectations Whylogs/WhyLabs data drift tabular: pandas.DataFrame N/A Generate data profiles with whylogs and upload them to WhyLabs whylogs<br><br>If you would like to see the available flavors of Data Validator, you can use the command:<br><br>zenml data-validator flavor list<br><br>How to use it</code> | <code>What are the best practices for deploying web applications using Docker and Kubernetes?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "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`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<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`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: True
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_384_cosine_map@100 | dim_64_cosine_map@100 |
|:-------:|:-----:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.6667 | 1 | 0.3884 | 0.4332 | 0.4464 | 0.3140 |
| **2.0** | **3** | **0.4064** | **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
```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}
}
```
#### TripletLoss
```bibtex
@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}
}
```
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