# e5-base-v2 by Liang Wang: Open-Weight Model
Source: https://savrn.com/models/e5-base-v2
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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## Runs On

What it takes to serve e5-base-v2 (109M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
| --- | --- | --- | --- | --- | --- |
| 16-bit | 0.2 GB | 0.3 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |
| 4-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 23, 2026.

[e5-base-v2 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/e5-base-v2/gpus)

## SAVRN's Notes on e5-base-v2

Every passage this embedding model sees is capped at 512 tokens, so chunking comes before hardware. Liang Wang published it as a 109 million parameter BertModel with 12 layers and a 768-wide embedding, built for sentence similarity; it runs in 0.3 GB of memory at 16-bit and 0.1 GB at 8-bit. The cheapest setup on our Index is one MI300X with 192 GB at $1.85 per hour, so the card is bought for throughput, not weights. The 2.19 GB download across 24 files carries four formats, safetensors, ONNX, OpenVINO and PyTorch, so load one.

MIT terms allow commercial use, modification and redistribution with the notices kept, so an index built on it can ship inside a product. Note that the benchmark results in the file are publisher reported, such as 92.81 accuracy on MTEB AmazonPolarityClassification, and the method sits in arXiv:2212.03533; test retrieval on your own corpus.

SAVRN Research, 2026-09-18

## Model Card

By Liang Wang, published under mit, revision f52bf8ec8c71.

[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://savrn.com/papers/text-embeddings-by-weakly-supervised-contrastive-pre-training). Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022

This model has 12 layers and the embedding size is 768.

### Usage

Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.

[Read the full model card (682 words)](https://savrn.com/models/e5-base-v2/card)

## Configuration

Architecture

BertModel

Context length (tokens)

512

Layers

12

Hidden size

768

Feed-forward size

3,072

Attention heads

12

Vocabulary size

30,522

Stored precision

float32

Model type

bert

## Identity and Version

Repository

intfloat/e5-base-v2

Publisher

Liang Wang

Task

Sentence similarity

Modality

Text

Library

sentence-transformers

Parameters

109M parameters

Languages

en

Revision

f52bf8ec8c7124536f0efb74aca902b2995e5bcd

First published

2023-05-19

Last updated

2025-02-17

## Files and Weights

24 files, 2.2 GB in total. The weights are 7 files totalling 2.2 GB in bin, onnx, safetensors.

Weights7 files · 2.2 GB

Configuration7 files · 2.2 KB

Tokenizer6 files · 1.9 MB

Documentation1 file · 67.6 KB

Other2 files · 1.0 MB

Repository1 file · 1.5 KB

Every file

| File | Type | Size | SHA-256 |
| --- | --- | --- | --- |
| model.safetensors | Weights | 438.0 MB | d0d559c47d5f |
| onnx/model.onnx | Weights | 435.8 MB | 157f97ef1957 |
| onnx/model_O4.onnx | Weights | 217.8 MB | af23dd830162 |
| onnx/model_qint8_avx512_vnni.onnx | Weights | 110.1 MB | f2ff55f62dfc |
| openvino/openvino_model.bin | Weights | 435.6 MB | 813bf62987ce |
| openvino/openvino_model_qint8_quantized.bin | Weights | 110.0 MB | 2d6045bf41cd |
| pytorch_model.bin | Weights | 438.0 MB | 5b119cd34c66 |
| 1_Pooling/config.json | Configuration | 200 B | — |
| config.json | Configuration | 650 B | — |
| modules.json | Configuration | 387 B | — |
| onnx/config.json | Configuration | 632 B | — |
| onnx/special_tokens_map.json | Configuration | 125 B | — |
| sentence_bert_config.json | Configuration | 57 B | — |
| special_tokens_map.json | Configuration | 125 B | — |
| README.md | Documentation | 67.6 KB | — |
| openvino/openvino_model.xml | Other | 363.1 KB | — |
| openvino/openvino_model_qint8_quantized.xml | Other | 663.8 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| onnx/tokenizer.json | Tokenizer | 711.4 KB | — |
| onnx/tokenizer_config.json | Tokenizer | 314 B | — |
| onnx/vocab.txt | Tokenizer | 231.5 KB | — |
| tokenizer.json | Tokenizer | 711.4 KB | — |
| tokenizer_config.json | Tokenizer | 314 B | — |
| vocab.txt | Tokenizer | 231.5 KB | — |

## License and Download

License

mit

Access

Open weights, no gate

Download size

2.2 GB

[Download from Liang Wang](https://huggingface.co/intfloat/e5-base-v2)

Released by Liang Wang through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit).

## Built From

- Described by [arXiv:2104.08663](https://savrn.com/papers/beir-a-heterogenous-benchmark-for-zero-shot-evaluation-of-information-retrieval-models)
- Described by [arXiv:2210.07316](https://savrn.com/papers/mteb-massive-text-embedding-benchmark)
- Described by [arXiv:2212.03533](https://savrn.com/papers/text-embeddings-by-weakly-supervised-contrastive-pre-training)

## Evaluations

Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical.

| Benchmark | Conditions | Result | Reported by | Revision | Date |
| --- | --- | --- | --- | --- | --- |
| MTEB AmazonCounterfactualClassification (en) | Configuration enTask ClassificationMetric accuracyComparison conditions not established | 77.7761 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonCounterfactualClassification (en) | Configuration enTask ClassificationMetric apComparison conditions not established | 42.0527 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonCounterfactualClassification (en) | Configuration enTask ClassificationMetric f1Comparison conditions not established | 72.1204 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonPolarityClassification | Configuration defaultTask ClassificationMetric accuracyComparison conditions not established | 92.8101 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonPolarityClassification | Configuration defaultTask ClassificationMetric apComparison conditions not established | 89.4214 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonPolarityClassification | Configuration defaultTask ClassificationMetric f1Comparison conditions not established | 92.8039 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonReviewsClassification (en) | Configuration enTask ClassificationMetric accuracyComparison conditions not established | 46.712 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonReviewsClassification (en) | Configuration enTask ClassificationMetric f1Comparison conditions not established | 46.1154 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_1Comparison conditions not established | 23.186 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_10Comparison conditions not established | 36.633 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_100Comparison conditions not established | 37.842 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_1000Comparison conditions not established | 37.865 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_3Comparison conditions not established | 32.278 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_5Comparison conditions not established | 34.761 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_1Comparison conditions not established | 23.4 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_10Comparison conditions not established | 36.721 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_100Comparison conditions not established | 37.937 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_1000Comparison conditions not established | 37.96 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_3Comparison conditions not established | 32.302 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_5Comparison conditions not established | 34.894 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_1Comparison conditions not established | 23.186 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_10Comparison conditions not established | 44.49 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_100Comparison conditions not established | 50.065 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_1000Comparison conditions not established | 50.63 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_3Comparison conditions not established | 35.461 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_5Comparison conditions not established | 39.969 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_1Comparison conditions not established | 23.186 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_10Comparison conditions not established | 6.97 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_100Comparison conditions not established | 0.951 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_1000Comparison conditions not established | 0.099 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_3Comparison conditions not established | 14.912 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_5Comparison conditions not established | 11.152 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_1Comparison conditions not established | 23.186 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_10Comparison conditions not established | 69.701 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_100Comparison conditions not established | 95.092 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_1000Comparison conditions not established | 99.431 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_3Comparison conditions not established | 44.737 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_5Comparison conditions not established | 55.761 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArxivClusteringP2P | Configuration defaultTask ClusteringMetric v_measureComparison conditions not established | 46.1031 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |
| MTEB ArxivClusteringS2S | Configuration defaultTask ClusteringMetric v_measureComparison conditions not established | 39.6728 | [intfloat](https://huggingface.co/intfloat/e5-base-v2) Publisher reported | Evaluated revision not stated | — |

## Memory Requirements

| Precision | Weights in memory |
| --- | --- |
| As published | 2.2 GB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.1 GB |

Weights only, from the published parameter count; the key-value cache and runtime add to this.

## Compare e5-base-v2

- [all-distilroberta-v1 vs e5-base-v2](https://savrn.com/models/compare/all-distilroberta-v1-vs-e5-base-v2)
- [bge-base-en-v1.5-course-recommender-v5 vs e5-base-v2](https://savrn.com/models/compare/bge-base-en-v1-5-course-recommender-v5-vs-e5-base-v2)

## Questions About e5-base-v2

### How much GPU memory does e5-base-v2 need?

About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (109M parameters) plus a working margin. A long context needs more.

### What is the cheapest GPU to run e5-base-v2 on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

### Can I use e5-base-v2 commercially?

Yes. e5-base-v2 is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

### What is e5-base-v2's context length?

512 tokens, from the maximum position embeddings in its published configuration.

## Similar Models

Model · Sentence similarity

### [e5-base](https://savrn.com/models/e5-base)

[Liang Wang](https://savrn.com/model-publishers/intfloat)

News (May 2023): please switch to e5-base-v2, which has better performance and same method of usage. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 This model has 12 layers and the embedding size is 768. Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. Please refer to our paper at https://arxiv.org/pdf/2212.03533.pdf. Check out unilm/e5 to reproduce evaluation results on the BEIR and MTEB benchmark. Below is an example for usage with sentencetransformers. Package requirements pip install sentencetransformers~=2.2.2 1. Do I need to add the prefix "query: " and "passage: " to…

Open weights mit 109M parameters 512 tokens sentence-transformers

[View model](https://savrn.com/models/e5-base)

Model · Sentence similarity

### [bge-base-en-v1.5-course-recommender-v5](https://savrn.com/models/bge-base-en-v1-5-course-recommender-v5)

[Data Society](https://savrn.com/model-publishers/datasocietyco)

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. First install the Sentence Transformers library: Then you can load this model and run inference. Approximate statistics based on the first 45 samples: Approximate statistics based on the first 5 samples: - evalstrategy: steps - perdevicetrainbatchsize: 16 - perdeviceevalbatchsize: 16 - learningrate: 3e-06 - maxsteps: 24 - warmupratio: 0.1 - batchsampler: noduplicates - overwriteoutputdir: False - dopredict: False…

Open weights 109M parameters 512 tokens sentence-transformers

[View model](https://savrn.com/models/bge-base-en-v1-5-course-recommender-v5)

Model · Sentence similarity

### [pubmedbert-base-embeddings](https://savrn.com/models/pubmedbert-base-embeddings)

[NeuML](https://savrn.com/model-publishers/neuml)

This is a PubMedBERT-base model fined-tuned using sentence-transformers. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The training dataset was generated using a random sample of PubMed title-abstract pairs along with similar title pairs. PubMedBERT Embeddings produces higher quality embeddings than generalized models for medical literature. Further fine-tuning for a medical subdomain will result in even better performance. This model can be used to build embeddings databases with txtai for semantic search and/or as a knowledge source for retrieval augmented generation (RAG). Alternatively, the model can…

Open weights apache-2.0 109M parameters 512 tokens sentence-transformers

[View model](https://savrn.com/models/pubmedbert-base-embeddings)

Model · Sentence similarity

### [vandf-rxnorm-biencoder](https://savrn.com/models/vandf-rxnorm-biencoder)

[Kevin Venanzi](https://savrn.com/model-publishers/kvenanzi)

Maps a VA National Drug File (VANDF) drug string to the RxNorm clinical drug confidence so you can auto-accept the sure cases and route the rest to review. Published tools such as RxMap normalize to the ingredient level (IN/MIN). This model targets the full clinical drug, where a miss on strength or dose form is a wrong answer. Method, hypotheses, the full sweep, and the error Mapper loads the encoder, trainconfig.json (input preprocessing), calibration.json (score → probability), and candidates.parquet (the 27,287 active RxNorm SCD/SBD names it searches) from this repo. The default acceptance threshold (0.92) was chosen on validation for 99% precision on a population that includes real…

Open weights apache-2.0 109M parameters 512 tokens sentence-transformers

[View model](https://savrn.com/models/vandf-rxnorm-biencoder)

Model · Sentence similarity

### [vandf-rxnorm-biencoder-all](https://savrn.com/models/vandf-rxnorm-biencoder-all)

[Kevin Venanzi](https://savrn.com/model-publishers/kvenanzi)

The recipe of kvenanzi/vandf-rxnorm-biencoder, trained on all 14,369 VA National Drug File (VANDF) strings that have a clinical drug in RxNorm, so that every ingredient family in the file is in training. It maps a VANDF drug string to the RxNorm clinical drug it names (SCD or SBD: ingredient, strength, and dose form), with a calibrated confidence for routing uncertain strings to review. This model has no test set of its own: every VA string was used for training. Its expected accuracy comes from seven-fold cross-validation of the recipe, and a comparison with the first model on strings neither model trained on (below). Write-up: https://withinnoise.dev/blog/posts/vandf-rxnorm-interventions/…

Open weights apache-2.0 109M parameters 512 tokens sentence-transformers

[View model](https://savrn.com/models/vandf-rxnorm-biencoder-all)

Model · Sentence similarity

### [finetuned-medical-bert](https://savrn.com/models/finetuned-medical-bert)

[Fico Filbert](https://savrn.com/model-publishers/ficofilbert)

This is a sentence-transformers model finetuned from google-bert/bert-base-uncased. It maps inputs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more. First install the Sentence Transformers library: Then you can load this model and run inference. Approximate statistics based on the first 100 samples: - perdevicetrainbatchsize: 16 - perdeviceevalbatchsize: 16 - multidatasetbatchsampler: roundrobin - dopredict: False - predictionlossonly: True - perdevicetrainbatchsize: 16 - perdeviceevalbatchsize: 16 - gradientaccumulationsteps: 1 - evalaccumulationsteps: None…

Open weights 109M parameters 512 tokens sentence-transformers

[View model](https://savrn.com/models/finetuned-medical-bert)

## Liang Wang

[All models and datasets](https://savrn.com/model-publishers/intfloat)

## Versions

- [f52bf8ec8c71](https://savrn.com/models/e5-base-v2/versions/f52bf8ec8c71) · current 2026-09-18

## Explore More

- [All sentence similarity models](https://savrn.com/models/tasks/sentence-similarity)
- [All models under mit](https://savrn.com/models/licenses/mit)
- [Model comparisons](https://savrn.com/models/comparisons)
- [The model directory](https://savrn.com/models)
- [Open model prices by host](https://savrn.com/ai-index/pricing/open-models)

## Source

- Repository metadata, read 2026-10-19.
- [Liang Wang website](https://github.com/microsoft/unilm/tree/master/e5)
- [Hugging Face record](https://huggingface.co/intfloat/e5-base-v2)
- [How the hub is built](https://savrn.com/model-hub/methodology)