embench

Benchmark embedding models on your own data.

MTEB and friends rank models on public, generic datasets. The model at the top of the leaderboard is often not the best one for your domain — your legal contracts, your support tickets, your medical notes, your code. embench lets you point a handful of candidate models (local or proprietary) at your own labeled data and see which one actually wins for your task, how confidently, and at what speed/cost.

import embench as eb

models = [
    eb.CachedModel(eb.SentenceTransformerModel("all-MiniLM-L6-v2")),
    eb.CachedModel(eb.OpenAIModel("text-embedding-3-small")),
]

retrieval = eb.RetrievalDataset.from_json("my_data.json")

results = eb.Benchmark(models, tasks=[
    eb.RetrievalTask(retrieval, k_values=[1, 5, 10]),
]).run()

print(results.to_table())
print("Winner:", results.best_model("ndcg@10"))

Install

pip install embench              # core + all API backends + PDF ingestion (docling/pymupdf)
pip install embench[local]       # + local models via sentence-transformers (pulls in PyTorch)
pip install embench[all]         # everything, including the local backend

The base install runs every API backend and the PDF ingestion pipeline out of the box. The only heavyweight left out is sentence-transformers (it pulls in PyTorch), so local models are an opt-in extra — add [local] (or [all]) when you want them.

Tasks

Task	What it measures	Metrics
RetrievalTask	RAG / semantic search quality	nDCG@k, Recall@k, MRR@k, MAP@k, Precision@k
ClassificationTask	Whether the space separates your categories (linear probe, cross-validated)	accuracy, f1_macro
ClusteringTask	Whether natural groups form clean clusters (KMeans)	V-measure, ARI, NMI

Data formats

Retrieval — JSON with queries, corpus, and qrels (relevance judgements; graded relevance supported):

{
  "queries": { "q1": "how do I reset my password" },
  "corpus":  { "d1": "click forgot password on the login page" },
  "qrels":   { "q1": { "d1": 2 } }
}

Classification / Clustering — CSV with a text column and a label column:

text,label
the stock market rallied,finance
the team won the championship,sports

eb.ClassificationDataset.from_csv("data.csv", text_col="text", label_col="label")

Ingesting documents (PDFs)

Don't have a clean JSON corpus yet? Turn a folder of PDFs into one. The pipeline is staged so each step persists its output and you never redo expensive work:

data/
  documents/          # your PDFs (input)
  extracted_text/     # stage 1 → one .md per document
  corpus.json         # stage 2 → {chunk_id: text}
  dataset.json        # stage 3 → + queries + qrels (ready to benchmark)

# ingestion ships in the base install — nothing extra to add
embench ingest extract data/documents/ -o data/extracted_text/        # stage 1
embench ingest chunk   data/extracted_text/ -o data/corpus.json       # stage 2
# …or both at once:
embench ingest run     data/documents/ -o data/corpus.json

# stage 3 — synthesise queries + qrels with an LLM, yielding a full dataset:
embench ingest queries data/corpus.json -o data/dataset.json --method openai
# now it's benchmarkable directly:
embench run -m dummy:256 --retrieval data/dataset.json

Each stage has swappable options:

Stage	Option	Notes
extract	--method docling (default)	layout/table aware, OCR for scanned pages (IBM Docling)
extract	--method pymupdf	fast, born-digital PDFs only (no OCR)
extract	--method granite	IBM Granite-Docling VLM — reads page images, best on complex/messy layouts
chunk	--method recursive (default)	packs paragraphs/sections up to --max-chars
chunk	--method sentence	never cuts mid-sentence
chunk	--method fixed	uniform sliding window with --overlap
queries	--method openai (default)	Chat Completions (OPENAI_API_KEY), default gpt-4o-mini
queries	--method google	Gemini (GOOGLE_API_KEY), default gemini-2.5-flash
queries	--n-queries N / --max-chunks N	questions per chunk / cap chunks for cost

In Python the stages are plain functions — extract once, then re-chunk freely, and synthesise the supervision with whichever model (or your own callable):

from embench.ingest import extract_text, chunk_text, generate_queries
docs    = extract_text("data/documents/", method="docling", out_dir="data/extracted_text/")
corpus  = chunk_text(docs, method="recursive", max_chars=1000, out_path="data/corpus.json")
dataset = generate_queries(corpus, method="openai", n_queries=1, out_path="data/dataset.json")

Stages 1–2 produce the corpus half of a retrieval dataset; stage 3 adds the supervision a benchmark needs — queries and relevance judgements (qrels), synthesised by asking an LLM for the questions each chunk answers. The result loads straight into RetrievalDataset.from_json and scores. (Pass your own generator=callable(text, n) -> list[str] to generate_queries to plug in any model or run offline.)

Caching

Wrap any model in CachedModel so re-running a comparison never re-encodes (or re-pays for) the same text. Embeddings are keyed by model name + text and stored on disk.

model = eb.CachedModel(eb.OpenAIModel("text-embedding-3-small"))

API keys

Hosted backends read their key from the environment (OPENAI_API_KEY, COHERE_API_KEY, VOYAGE_API_KEY, GOOGLE_API_KEY, HUGGINGFACE_API_KEY). Copy .env.example to .env, fill it in, and load it:

import embench as eb
eb.load_env()            # reads .env into the environment (no-op if absent)

models = [eb.GoogleModel(), eb.HuggingFaceModel("sentence-transformers/all-MiniLM-L6-v2")]

load_env() uses python-dotenv if installed and otherwise a small built-in parser, so it works with just the core install.

Reading results

results.to_table()                       # pretty comparison string (quality only)
results.to_table(std=True)               # cells as "mean ± std"
results.to_dataframe()                    # wide: models x quality metrics
results.performance()                     # wide: models x speed/cost metrics
results.best_model("ndcg@10")            # single best model for a metric
results.ranking("accuracy")              # all models ranked by one metric
results.aggregate_ranking()              # one overall score: mean across quality metrics
results.to_csv("out.csv")                # export

Each metric also carries a spread (accuracy_std over CV folds, retrieval metric std over queries). It's hidden from the default table — use to_table(std=True) (or the CLI --std flag) to see whether one model really beats another or the gap is within noise.

Is the difference real? (significance testing)

A higher number isn't always a real win — it can be noise on your particular queries. embench answers this directly with a paired randomization (Fisher) test on the per-query scores, the same idea ranx uses, but with no extra dependency:

results.significance("ndcg@10")   # tidy table: every model pair, delta, p-value, significant?
results.win_tie_loss("ndcg@10")   # per-model record: wins / ties / losses (p < 0.05)

A win is a higher mean that clears p < max_p; anything within noise is a tie. This needs per-query data, so it applies to retrieval metrics. From the CLI: embench run ... --significance ndcg@10 --rank.

Speed and cost

Every run also records how long each model spent encoding and how many texts it actually encoded (cache hits don't count, so this tracks real API cost). These are kept out of the quality table and exposed separately:

results.performance()          # encode_seconds, texts_encoded, texts_per_sec per model
results.ranking("texts_per_sec")  # fastest model first
results.to_dataframe(include_perf=True)  # quality + perf in one table

Choosing a model is a quality/speed/cost trade-off — embench shows all three.

Extending it

A new model backend: subclass BaseEmbeddingModel, implement _encode. Batching and progress are handled for you.

class MyModel(eb.BaseEmbeddingModel):
    def _encode(self, texts: list[str]) -> np.ndarray:
        return my_embedding_call(texts)

A new task type: subclass Task, set task_type and dataset_class, implement evaluate(self, model) -> dict[str, float]. The runner and reporting need no changes — that is the whole design.

Command line

Run a benchmark from the shell — no Python needed. Models are backend:model_id specs (repeat -m); tasks are file paths. A local .env is loaded automatically, so API keys are picked up.

embench run \
  -m dummy:256 \
  -m openai:text-embedding-3-small \
  -m local:all-MiniLM-L6-v2 \
  --retrieval my_data.json \
  --classification labeled.csv \
  -k 1,5,10 \
  -o results.csv

Backends: dummy, openai, cohere, voyage, google, hf:<id> (Inference API), local:<id> (sentence-transformers). Caching is on by default (--no-cache to disable); add --perf to fold speed/cost into the table. See embench run -h for all options.

Try it without any keys

embench run -m dummy:128 -m dummy:512 \
  --retrieval sample_data/retrieval.json \
  --classification sample_data/labeled.csv \
  --clustering sample_data/labeled.csv
# or the scripted equivalent:
python examples/quickstart.py

Uses a dependency-free DummyModel (a hashing-trick embedder) so the whole pipeline runs offline. Use it as a baseline and a sanity check.

Development

After cloning, set up an isolated environment in one command:

# Windows (PowerShell)
./setup.ps1

# macOS / Linux
./setup.sh

This creates a local .venv, installs embench (editable, with the API backends + dev tools), and seeds a .env from .env.example for your API keys. Then:

# activate: .venv\Scripts\Activate.ps1   (Windows)  |  source .venv/bin/activate  (Unix)
pytest                      # run the test suite

To also work on the local backend (PyTorch), add it after setup: pip install -e ".[all]".

.venv, .env, and the embedding cache are git-ignored; never commit them. Do commit .env.example.

License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License - Feel free to use, modify, and distribute
Academic use encouraged - Please cite our work
Commercial use welcome - Attribution appreciated

Support

For questions, suggestions, or collaboration opportunities, feel free to reach out.