Auto Fine-Tuning
Just tell us which domain you want your embeddings to excel in, and we automatically deliver a ready-to-use, fine-tuned embedding model for that domain.
What is Auto Fine-Tuning?
There are three ways to specify your requirement: a general instruction, a URL, or a query-document description. Choose one.
join_right
Query-document description
Describe what the query looks like and what the matched document looks like in your domain.
public
Or, webpage URL
Refer to the content from a URL for fine-tuning.
notes
Or, general instruction
Provide a detailed description of how the fine-tuned embeddings will be used.
Select a base embedding model
Fine-tuning allows you to take a pre-trained model and adapt it to a specific task or domain by training it on a new dataset. In practice, finding effective training data is not straightforward for many users. Effective training requires more than just throwing raw PDFs, HTMLs into the model; and it is hard to get it right. Auto fine-tuning solves this problem by automatically generating effective training data using an advanced LLM agent pipeline; and fine-tuning the model within a ML workflow. You can think it as a combination of synthetic data generation and AutoML, so all you need to do is describe your target domain in natural language and let our system do the rest.
Auto fine-tuning holds an auto-magical promise to deliver fine-tuned embeddings for any domain you want. But does it really work? This is a fairly reasonable doubt. We've tested it on a variety of domains and base models to find out. Check out the cherry-picked and lemon-picked results below.
Base model for fine-tuning
jinaai/jina-embeddings-v2-base-en
Avg. improvement
arrow_upward 2%
description Domain instruction
speed Performance on synthetic validation set before and after fine-tuning
NDCG
0.505 arrow_forward 0.532 arrow_upward 5%
MAP
0.352 arrow_forward 0.389 arrow_upward 10%
MRR
0.352 arrow_forward 0.389 arrow_upward 10%
speed Performance on held-out test set before and after fine-tuning
check
Tested on 50 random samples from tollefj/norwegian-nli-triplets
NDCG
0.852 arrow_forward 0.867 arrow_upward 2%
MAP
0.800 arrow_forward 0.820 arrow_upward 2%
MRR
0.800 arrow_forward 0.820 arrow_upward 2%
data_usage Synthetic data generated
Total
4648
Training
4480
Validation
168
Base model for fine-tuning
jinaai/jina-embeddings-v2-base-en
Avg. improvement
arrow_upward 6%
description Domain instruction
speed Performance on synthetic validation set before and after fine-tuning
NDCG
0.672 arrow_forward 0.755 arrow_upward 12%
MAP
0.567 arrow_forward 0.675 arrow_upward 19%
MRR
0.567 arrow_forward 0.675 arrow_upward 19%
speed Performance on held-out test set before and after fine-tuning
check
Tested on 50 random samples from mteb/askubuntudupquestions-reranking
NDCG
0.698 arrow_forward 0.722 arrow_upward 3%
MAP
0.515 arrow_forward 0.549 arrow_upward 6%
MRR
0.666 arrow_forward 0.712 arrow_upward 7%
data_usage Synthetic data generated
Total
616
Training
448
Validation
168
Base model for fine-tuning
jinaai/jina-embeddings-v2-base-en
Avg. improvement
arrow_upward 9%
description Domain instruction
speed Performance on synthetic validation set before and after fine-tuning
NDCG
0.727 arrow_forward 0.861 arrow_upward 18%
MAP
0.640 arrow_forward 0.814 arrow_upward 27%
MRR
0.640 arrow_forward 0.814 arrow_upward 27%
speed Performance on held-out test set before and after fine-tuning
check
Tested on 50 random samples from mteb/scidocs-reranking
NDCG
0.773 arrow_forward 0.822 arrow_upward 6%
MAP
0.575 arrow_forward 0.651 arrow_upward 13%
MRR
0.823 arrow_forward 0.884 arrow_upward 7%
data_usage Synthetic data generated
Total
616
Training
448
Validation
168
Base model for fine-tuning
jinaai/jina-embeddings-v2-base-zh
Avg. improvement
arrow_upward 1%
description Domain instruction
speed Performance on synthetic validation set before and after fine-tuning
NDCG
0.718 arrow_forward 0.785 arrow_upward 9%
MAP
0.629 arrow_forward 0.717 arrow_upward 14%
MRR
0.629 arrow_forward 0.717 arrow_upward 14%
speed Performance on held-out test set before and after fine-tuning
check
Tested on 50 random samples from C-MTEB/CMedQAv2-reranking
NDCG
0.938 arrow_forward 0.948 arrow_upward 1%
MAP
0.912 arrow_forward 0.926 arrow_upward 2%
MRR
0.920 arrow_forward 0.933 arrow_upward 1%
data_usage Synthetic data generated
Total
616
Training
448
Validation
168
Base model for fine-tuning
jinaai/jina-embeddings-v2-base-en
Avg. improvement
arrow_upward 6%
description Domain instruction
speed Performance on synthetic validation set before and after fine-tuning
NDCG
0.543 arrow_forward 0.579 arrow_upward 7%
MAP
0.402 arrow_forward 0.452 arrow_upward 12%
MRR
0.402 arrow_forward 0.452 arrow_upward 12%
speed Performance on held-out test set before and after fine-tuning
check
Tested on 50 random samples from nc33/triplet_sbert_law2 (machine-translated to dutch)
NDCG
0.904 arrow_forward 0.948 arrow_upward 5%
MAP
0.870 arrow_forward 0.930 arrow_upward 7%
MRR
0.870 arrow_forward 0.930 arrow_upward 7%
data_usage Synthetic data generated
Total
9128
Training
8960
Validation
168
Base model for fine-tuning
jinaai/jina-embeddings-v2-base-code
Avg. improvement
arrow_downward -4%
description Domain instruction
speed Performance on synthetic validation set before and after fine-tuning
NDCG
0.671 arrow_forward 0.640 arrow_downward -5%
MAP
0.569 arrow_forward 0.525 arrow_downward -8%
MRR
0.569 arrow_forward 0.525 arrow_downward -8%
speed Performance on held-out test set before and after fine-tuning
check
Tested on 50 random samples from mteb/stackoverflowdupquestions-reranking
NDCG
0.640 arrow_forward 0.621 arrow_downward -3%
MAP
0.530 arrow_forward 0.505 arrow_downward -5%
MRR
0.555 arrow_forward 0.532 arrow_downward -4%
data_usage Synthetic data generated
Total
616
Training
448
Validation
168
Base model for fine-tuning
jinaai/jina-embeddings-v2-base-code
Avg. improvement
arrow_downward -4%
description Domain instruction
speed Performance on synthetic validation set before and after fine-tuning
NDCG
0.632 arrow_forward 0.711 arrow_upward 13%
MAP
0.517 arrow_forward 0.622 arrow_upward 20%
MRR
0.517 arrow_forward 0.622 arrow_upward 20%
speed Performance on held-out test set before and after fine-tuning
check
Tested on 50 random samples from mteb/stackoverflowdupquestions-reranking
NDCG
0.640 arrow_forward 0.619 arrow_downward -3%
MAP
0.530 arrow_forward 0.504 arrow_downward -5%
MRR
0.555 arrow_forward 0.525 arrow_downward -5%
data_usage Synthetic data generated
Total
616
Training
448
Validation
168
Base model for fine-tuning
jinaai/jina-embeddings-v2-base-en
Avg. improvement
arrow_upward 1%
description Domain instruction
speed Performance on synthetic validation set before and after fine-tuning
NDCG
0.646 arrow_forward 0.729 arrow_upward 13%
MAP
0.535 arrow_forward 0.644 arrow_upward 20%
MRR
0.535 arrow_forward 0.644 arrow_upward 20%
speed Performance on held-out test set before and after fine-tuning
check
Tested on 50 random samples from mteb/askubuntudupquestions-reranking
NDCG
0.645 arrow_forward 0.650 arrow_upward 1%
MAP
0.452 arrow_forward 0.462 arrow_upward 2%
MRR
0.606 arrow_forward 0.605 arrow_downward -0%
data_usage Synthetic data generated
Total
616
Training
448
Validation
168
Auto Fine-Tuning API
Get fine-tuned embeddings for any domain you want.
Describe the domain you wish to fine-tune for.
There are three ways to specify your requirement: a general instruction, a URL, or a query-document description. Choose one.
public
Or, webpage URL
Refer to the content from a URL for fine-tuning.
notes
Or, general instruction
Provide a detailed description of how the fine-tuned embeddings will be used.
Choose a base embedding model for fine-tuning.
Enter your API key.
Please enter the email where you want to receive the download link upon completion.
Agree to the terms and begin fine-tuning.
Rate Limit
Product | API Endpoint | Description | Allowed Request | Without API Key (RPM) | With API Key (RPM) | With Premium API Key (RPM) | Average Latency (s) | Token Usage Counting | |
---|---|---|---|---|---|---|---|---|---|
Reader API | https://r.jina.ai | Convert URL to LLM-friendly text | GET/POST | 20 | 200 | 1000 | 1.6 | Count the number of tokens in the output response. | |
Reader API | https://s.jina.ai | Search the web and convert results to LLM-friendly text | GET/POST | 5 | 40 | 100 | 7.7 | Count the number of tokens in the output response. | |
Segmenter API | https://segment.jina.ai | Tokenize and segment long text | GET/POST | 20 | 200 | 1000 | 0.3 | Token is not counted as usage. | |
Embedding API | https://api.jina.ai/v1/embeddings | Convert text/images to fixed-length vectors | POST | block | 100 | 100trending_up | bolt depends on the input size | Count the number of tokens in the input request. | |
Reranker API | https://api.jina.ai/v1/rerank | Tokenize and segment long text | POST | block | 100 | 100trending_up | bolt depends on the input size | Count the number of tokens in the input request. |
Auto Fine-Tuning-related common questions
How much does the Fine-tuning API cost?
What do I need to input? Do I need to provide training data?
How long does it take to fine-tune a model?
Where are the fine-tuned models stored?
If I provide a reference URL, how does the system use it?
Can I fine-tune a model for a specific language?
Can I fine-tune non-Jina embeddings, e.g., bge-M3?
How do you ensure the quality of the fine-tuned models?
How do you generate synthetic data?
Can I keep my fine-tuned models and synthetic data private?
How can I use the fine-tuned model?
I never received the email with the evaluation results. What should I do?
API-related common questions
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Can I use the same API key for embedding, reranking, reader, fine-tuning APIs?
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Can I monitor the token usage of my API key?
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What should I do if I forget my API key?
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Do API keys expire?
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Why is the first request for some models slow?
code
Is user input data used for training your models?
Billing-related common questions
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Is billing based on the number of sentences or requests?
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Is there a free trial available for new users?
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Are tokens charged for failed requests?
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What payment methods are accepted?
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Is invoicing available for token purchases?