Instructions to use magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF")

sentences = [
    "That is a happy person",
    "That is a happy dog",
    "That is a very happy person",
    "Today is a sunny day"
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]

Transformers

How to use magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF", dtype="auto")

Transformers.js

How to use magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('sentence-similarity', 'magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF');

llama-cpp-python

How to use magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF",
	filename="nomic-embed-text-v1.5-q8_0.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF:Q8_0

Use Docker

docker model run hf.co/magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF with Ollama:
```
ollama run hf.co/magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF:Q8_0
```

Unsloth Studio new

How to use magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF to start chatting

Docker Model Runner
How to use magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF:Q8_0
```

Lemonade

How to use magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF:Q8_0

Run and chat with the model

lemonade run user.nomic-embed-text-v1.5-Q8_0-GGUF-Q8_0

List all available models

lemonade list

nomic-embed-text-v1.5-Q8_0-GGUF / README.md

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	---
	base_model: nomic-ai/nomic-embed-text-v1.5
	language:
	- en
	library_name: sentence-transformers
	license: apache-2.0
	pipeline_tag: sentence-similarity
	tags:
	- feature-extraction
	- sentence-similarity
	- mteb
	- transformers
	- transformers.js
	- llama-cpp
	- gguf-my-repo
	model-index:
	- name: epoch_0_model
	results:
	- task:
	type: Classification
	dataset:
	name: MTEB AmazonCounterfactualClassification (en)
	type: mteb/amazon_counterfactual
	config: en
	split: test
	revision: e8379541af4e31359cca9fbcf4b00f2671dba205
	metrics:
	- type: accuracy
	value: 75.20895522388058
	- type: ap
	value: 38.57605549557802
	- type: f1
	value: 69.35586565857854
	- task:
	type: Classification
	dataset:
	name: MTEB AmazonPolarityClassification
	type: mteb/amazon_polarity
	config: default
	split: test
	revision: e2d317d38cd51312af73b3d32a06d1a08b442046
	metrics:
	- type: accuracy
	value: 91.8144
	- type: ap
	value: 88.65222882032363
	- type: f1
	value: 91.80426301643274
	- task:
	type: Classification
	dataset:
	name: MTEB AmazonReviewsClassification (en)
	type: mteb/amazon_reviews_multi
	config: en
	split: test
	revision: 1399c76144fd37290681b995c656ef9b2e06e26d
	metrics:
	- type: accuracy
	value: 47.162000000000006
	- type: f1
	value: 46.59329642263158
	- task:
	type: Retrieval
	dataset:
	name: MTEB ArguAna
	type: arguana
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 24.253
	- type: map_at_10
	value: 38.962
	- type: map_at_100
	value: 40.081
	- type: map_at_1000
	value: 40.089000000000006
	- type: map_at_3
	value: 33.499
	- type: map_at_5
	value: 36.351
	- type: mrr_at_1
	value: 24.609
	- type: mrr_at_10
	value: 39.099000000000004
	- type: mrr_at_100
	value: 40.211000000000006
	- type: mrr_at_1000
	value: 40.219
	- type: mrr_at_3
	value: 33.677
	- type: mrr_at_5
	value: 36.469
	- type: ndcg_at_1
	value: 24.253
	- type: ndcg_at_10
	value: 48.010999999999996
	- type: ndcg_at_100
	value: 52.756
	- type: ndcg_at_1000
	value: 52.964999999999996
	- type: ndcg_at_3
	value: 36.564
	- type: ndcg_at_5
	value: 41.711999999999996
	- type: precision_at_1
	value: 24.253
	- type: precision_at_10
	value: 7.738
	- type: precision_at_100
	value: 0.98
	- type: precision_at_1000
	value: 0.1
	- type: precision_at_3
	value: 15.149000000000001
	- type: precision_at_5
	value: 11.593
	- type: recall_at_1
	value: 24.253
	- type: recall_at_10
	value: 77.383
	- type: recall_at_100
	value: 98.009
	- type: recall_at_1000
	value: 99.644
	- type: recall_at_3
	value: 45.448
	- type: recall_at_5
	value: 57.965999999999994
	- task:
	type: Clustering
	dataset:
	name: MTEB ArxivClusteringP2P
	type: mteb/arxiv-clustering-p2p
	config: default
	split: test
	revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
	metrics:
	- type: v_measure
	value: 45.69069567851087
	- task:
	type: Clustering
	dataset:
	name: MTEB ArxivClusteringS2S
	type: mteb/arxiv-clustering-s2s
	config: default
	split: test
	revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
	metrics:
	- type: v_measure
	value: 36.35185490976283
	- task:
	type: Reranking
	dataset:
	name: MTEB AskUbuntuDupQuestions
	type: mteb/askubuntudupquestions-reranking
	config: default
	split: test
	revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
	metrics:
	- type: map
	value: 61.71274951450321
	- type: mrr
	value: 76.06032625423207
	- task:
	type: STS
	dataset:
	name: MTEB BIOSSES
	type: mteb/biosses-sts
	config: default
	split: test
	revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
	metrics:
	- type: cos_sim_pearson
	value: 86.73980520022269
	- type: cos_sim_spearman
	value: 84.24649792685918
	- type: euclidean_pearson
	value: 85.85197641158186
	- type: euclidean_spearman
	value: 84.24649792685918
	- type: manhattan_pearson
	value: 86.26809552711346
	- type: manhattan_spearman
	value: 84.56397504030865
	- task:
	type: Classification
	dataset:
	name: MTEB Banking77Classification
	type: mteb/banking77
	config: default
	split: test
	revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
	metrics:
	- type: accuracy
	value: 84.25324675324674
	- type: f1
	value: 84.17872280892557
	- task:
	type: Clustering
	dataset:
	name: MTEB BiorxivClusteringP2P
	type: mteb/biorxiv-clustering-p2p
	config: default
	split: test
	revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
	metrics:
	- type: v_measure
	value: 38.770253446400886
	- task:
	type: Clustering
	dataset:
	name: MTEB BiorxivClusteringS2S
	type: mteb/biorxiv-clustering-s2s
	config: default
	split: test
	revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
	metrics:
	- type: v_measure
	value: 32.94307095497281
	- task:
	type: Retrieval
	dataset:
	name: MTEB CQADupstackAndroidRetrieval
	type: BeIR/cqadupstack
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 32.164
	- type: map_at_10
	value: 42.641
	- type: map_at_100
	value: 43.947
	- type: map_at_1000
	value: 44.074999999999996
	- type: map_at_3
	value: 39.592
	- type: map_at_5
	value: 41.204
	- type: mrr_at_1
	value: 39.628
	- type: mrr_at_10
	value: 48.625
	- type: mrr_at_100
	value: 49.368
	- type: mrr_at_1000
	value: 49.413000000000004
	- type: mrr_at_3
	value: 46.400000000000006
	- type: mrr_at_5
	value: 47.68
	- type: ndcg_at_1
	value: 39.628
	- type: ndcg_at_10
	value: 48.564
	- type: ndcg_at_100
	value: 53.507000000000005
	- type: ndcg_at_1000
	value: 55.635999999999996
	- type: ndcg_at_3
	value: 44.471
	- type: ndcg_at_5
	value: 46.137
	- type: precision_at_1
	value: 39.628
	- type: precision_at_10
	value: 8.856
	- type: precision_at_100
	value: 1.429
	- type: precision_at_1000
	value: 0.191
	- type: precision_at_3
	value: 21.268
	- type: precision_at_5
	value: 14.649000000000001
	- type: recall_at_1
	value: 32.164
	- type: recall_at_10
	value: 59.609
	- type: recall_at_100
	value: 80.521
	- type: recall_at_1000
	value: 94.245
	- type: recall_at_3
	value: 46.521
	- type: recall_at_5
	value: 52.083999999999996
	- type: map_at_1
	value: 31.526
	- type: map_at_10
	value: 41.581
	- type: map_at_100
	value: 42.815999999999995
	- type: map_at_1000
	value: 42.936
	- type: map_at_3
	value: 38.605000000000004
	- type: map_at_5
	value: 40.351
	- type: mrr_at_1
	value: 39.489999999999995
	- type: mrr_at_10
	value: 47.829
	- type: mrr_at_100
	value: 48.512
	- type: mrr_at_1000
	value: 48.552
	- type: mrr_at_3
	value: 45.754
	- type: mrr_at_5
	value: 46.986
	- type: ndcg_at_1
	value: 39.489999999999995
	- type: ndcg_at_10
	value: 47.269
	- type: ndcg_at_100
	value: 51.564
	- type: ndcg_at_1000
	value: 53.53099999999999
	- type: ndcg_at_3
	value: 43.301
	- type: ndcg_at_5
	value: 45.239000000000004
	- type: precision_at_1
	value: 39.489999999999995
	- type: precision_at_10
	value: 8.93
	- type: precision_at_100
	value: 1.415
	- type: precision_at_1000
	value: 0.188
	- type: precision_at_3
	value: 20.892
	- type: precision_at_5
	value: 14.865999999999998
	- type: recall_at_1
	value: 31.526
	- type: recall_at_10
	value: 56.76
	- type: recall_at_100
	value: 75.029
	- type: recall_at_1000
	value: 87.491
	- type: recall_at_3
	value: 44.786
	- type: recall_at_5
	value: 50.254
	- type: map_at_1
	value: 40.987
	- type: map_at_10
	value: 52.827
	- type: map_at_100
	value: 53.751000000000005
	- type: map_at_1000
	value: 53.81
	- type: map_at_3
	value: 49.844
	- type: map_at_5
	value: 51.473
	- type: mrr_at_1
	value: 46.833999999999996
	- type: mrr_at_10
	value: 56.389
	- type: mrr_at_100
	value: 57.003
	- type: mrr_at_1000
	value: 57.034
	- type: mrr_at_3
	value: 54.17999999999999
	- type: mrr_at_5
	value: 55.486999999999995
	- type: ndcg_at_1
	value: 46.833999999999996
	- type: ndcg_at_10
	value: 58.372
	- type: ndcg_at_100
	value: 62.068
	- type: ndcg_at_1000
	value: 63.288
	- type: ndcg_at_3
	value: 53.400000000000006
	- type: ndcg_at_5
	value: 55.766000000000005
	- type: precision_at_1
	value: 46.833999999999996
	- type: precision_at_10
	value: 9.191
	- type: precision_at_100
	value: 1.192
	- type: precision_at_1000
	value: 0.134
	- type: precision_at_3
	value: 23.448
	- type: precision_at_5
	value: 15.862000000000002
	- type: recall_at_1
	value: 40.987
	- type: recall_at_10
	value: 71.146
	- type: recall_at_100
	value: 87.035
	- type: recall_at_1000
	value: 95.633
	- type: recall_at_3
	value: 58.025999999999996
	- type: recall_at_5
	value: 63.815999999999995
	- type: map_at_1
	value: 24.587
	- type: map_at_10
	value: 33.114
	- type: map_at_100
	value: 34.043
	- type: map_at_1000
	value: 34.123999999999995
	- type: map_at_3
	value: 30.45
	- type: map_at_5
	value: 31.813999999999997
	- type: mrr_at_1
	value: 26.554
	- type: mrr_at_10
	value: 35.148
	- type: mrr_at_100
	value: 35.926
	- type: mrr_at_1000
	value: 35.991
	- type: mrr_at_3
	value: 32.599000000000004
	- type: mrr_at_5
	value: 33.893
	- type: ndcg_at_1
	value: 26.554
	- type: ndcg_at_10
	value: 38.132
	- type: ndcg_at_100
	value: 42.78
	- type: ndcg_at_1000
	value: 44.919
	- type: ndcg_at_3
	value: 32.833
	- type: ndcg_at_5
	value: 35.168
	- type: precision_at_1
	value: 26.554
	- type: precision_at_10
	value: 5.921
	- type: precision_at_100
	value: 0.8659999999999999
	- type: precision_at_1000
	value: 0.109
	- type: precision_at_3
	value: 13.861
	- type: precision_at_5
	value: 9.605
	- type: recall_at_1
	value: 24.587
	- type: recall_at_10
	value: 51.690000000000005
	- type: recall_at_100
	value: 73.428
	- type: recall_at_1000
	value: 89.551
	- type: recall_at_3
	value: 37.336999999999996
	- type: recall_at_5
	value: 43.047000000000004
	- type: map_at_1
	value: 16.715
	- type: map_at_10
	value: 24.251
	- type: map_at_100
	value: 25.326999999999998
	- type: map_at_1000
	value: 25.455
	- type: map_at_3
	value: 21.912000000000003
	- type: map_at_5
	value: 23.257
	- type: mrr_at_1
	value: 20.274
	- type: mrr_at_10
	value: 28.552
	- type: mrr_at_100
	value: 29.42
	- type: mrr_at_1000
	value: 29.497
	- type: mrr_at_3
	value: 26.14
	- type: mrr_at_5
	value: 27.502
	- type: ndcg_at_1
	value: 20.274
	- type: ndcg_at_10
	value: 29.088
	- type: ndcg_at_100
	value: 34.293
	- type: ndcg_at_1000
	value: 37.271
	- type: ndcg_at_3
	value: 24.708
	- type: ndcg_at_5
	value: 26.809
	- type: precision_at_1
	value: 20.274
	- type: precision_at_10
	value: 5.361
	- type: precision_at_100
	value: 0.915
	- type: precision_at_1000
	value: 0.13
	- type: precision_at_3
	value: 11.733
	- type: precision_at_5
	value: 8.556999999999999
	- type: recall_at_1
	value: 16.715
	- type: recall_at_10
	value: 39.587
	- type: recall_at_100
	value: 62.336000000000006
	- type: recall_at_1000
	value: 83.453
	- type: recall_at_3
	value: 27.839999999999996
	- type: recall_at_5
	value: 32.952999999999996
	- type: map_at_1
	value: 28.793000000000003
	- type: map_at_10
	value: 38.582
	- type: map_at_100
	value: 39.881
	- type: map_at_1000
	value: 39.987
	- type: map_at_3
	value: 35.851
	- type: map_at_5
	value: 37.289
	- type: mrr_at_1
	value: 34.455999999999996
	- type: mrr_at_10
	value: 43.909
	- type: mrr_at_100
	value: 44.74
	- type: mrr_at_1000
	value: 44.786
	- type: mrr_at_3
	value: 41.659
	- type: mrr_at_5
	value: 43.010999999999996
	- type: ndcg_at_1
	value: 34.455999999999996
	- type: ndcg_at_10
	value: 44.266
	- type: ndcg_at_100
	value: 49.639
	- type: ndcg_at_1000
	value: 51.644
	- type: ndcg_at_3
	value: 39.865
	- type: ndcg_at_5
	value: 41.887
	- type: precision_at_1
	value: 34.455999999999996
	- type: precision_at_10
	value: 7.843999999999999
	- type: precision_at_100
	value: 1.243
	- type: precision_at_1000
	value: 0.158
	- type: precision_at_3
	value: 18.831999999999997
	- type: precision_at_5
	value: 13.147
	- type: recall_at_1
	value: 28.793000000000003
	- type: recall_at_10
	value: 55.68300000000001
	- type: recall_at_100
	value: 77.99000000000001
	- type: recall_at_1000
	value: 91.183
	- type: recall_at_3
	value: 43.293
	- type: recall_at_5
	value: 48.618
	- type: map_at_1
	value: 25.907000000000004
	- type: map_at_10
	value: 35.519
	- type: map_at_100
	value: 36.806
	- type: map_at_1000
	value: 36.912
	- type: map_at_3
	value: 32.748
	- type: map_at_5
	value: 34.232
	- type: mrr_at_1
	value: 31.621
	- type: mrr_at_10
	value: 40.687
	- type: mrr_at_100
	value: 41.583
	- type: mrr_at_1000
	value: 41.638999999999996
	- type: mrr_at_3
	value: 38.527
	- type: mrr_at_5
	value: 39.612
	- type: ndcg_at_1
	value: 31.621
	- type: ndcg_at_10
	value: 41.003
	- type: ndcg_at_100
	value: 46.617999999999995
	- type: ndcg_at_1000
	value: 48.82
	- type: ndcg_at_3
	value: 36.542
	- type: ndcg_at_5
	value: 38.368
	- type: precision_at_1
	value: 31.621
	- type: precision_at_10
	value: 7.396999999999999
	- type: precision_at_100
	value: 1.191
	- type: precision_at_1000
	value: 0.153
	- type: precision_at_3
	value: 17.39
	- type: precision_at_5
	value: 12.1
	- type: recall_at_1
	value: 25.907000000000004
	- type: recall_at_10
	value: 52.115
	- type: recall_at_100
	value: 76.238
	- type: recall_at_1000
	value: 91.218
	- type: recall_at_3
	value: 39.417
	- type: recall_at_5
	value: 44.435
	- type: map_at_1
	value: 25.732166666666668
	- type: map_at_10
	value: 34.51616666666667
	- type: map_at_100
	value: 35.67241666666666
	- type: map_at_1000
	value: 35.78675
	- type: map_at_3
	value: 31.953416666666662
	- type: map_at_5
	value: 33.333
	- type: mrr_at_1
	value: 30.300166666666673
	- type: mrr_at_10
	value: 38.6255
	- type: mrr_at_100
	value: 39.46183333333334
	- type: mrr_at_1000
	value: 39.519999999999996
	- type: mrr_at_3
	value: 36.41299999999999
	- type: mrr_at_5
	value: 37.6365
	- type: ndcg_at_1
	value: 30.300166666666673
	- type: ndcg_at_10
	value: 39.61466666666667
	- type: ndcg_at_100
	value: 44.60808333333334
	- type: ndcg_at_1000
	value: 46.91708333333334
	- type: ndcg_at_3
	value: 35.26558333333333
	- type: ndcg_at_5
	value: 37.220000000000006
	- type: precision_at_1
	value: 30.300166666666673
	- type: precision_at_10
	value: 6.837416666666667
	- type: precision_at_100
	value: 1.10425
	- type: precision_at_1000
	value: 0.14875
	- type: precision_at_3
	value: 16.13716666666667
	- type: precision_at_5
	value: 11.2815
	- type: recall_at_1
	value: 25.732166666666668
	- type: recall_at_10
	value: 50.578916666666665
	- type: recall_at_100
	value: 72.42183333333334
	- type: recall_at_1000
	value: 88.48766666666667
	- type: recall_at_3
	value: 38.41325
	- type: recall_at_5
	value: 43.515750000000004
	- type: map_at_1
	value: 23.951
	- type: map_at_10
	value: 30.974
	- type: map_at_100
	value: 31.804
	- type: map_at_1000
	value: 31.900000000000002
	- type: map_at_3
	value: 28.762
	- type: map_at_5
	value: 29.94
	- type: mrr_at_1
	value: 26.534000000000002
	- type: mrr_at_10
	value: 33.553
	- type: mrr_at_100
	value: 34.297
	- type: mrr_at_1000
	value: 34.36
	- type: mrr_at_3
	value: 31.391000000000002
	- type: mrr_at_5
	value: 32.525999999999996
	- type: ndcg_at_1
	value: 26.534000000000002
	- type: ndcg_at_10
	value: 35.112
	- type: ndcg_at_100
	value: 39.28
	- type: ndcg_at_1000
	value: 41.723
	- type: ndcg_at_3
	value: 30.902
	- type: ndcg_at_5
	value: 32.759
	- type: precision_at_1
	value: 26.534000000000002
	- type: precision_at_10
	value: 5.445
	- type: precision_at_100
	value: 0.819
	- type: precision_at_1000
	value: 0.11
	- type: precision_at_3
	value: 12.986
	- type: precision_at_5
	value: 9.049
	- type: recall_at_1
	value: 23.951
	- type: recall_at_10
	value: 45.24
	- type: recall_at_100
	value: 64.12299999999999
	- type: recall_at_1000
	value: 82.28999999999999
	- type: recall_at_3
	value: 33.806000000000004
	- type: recall_at_5
	value: 38.277
	- type: map_at_1
	value: 16.829
	- type: map_at_10
	value: 23.684
	- type: map_at_100
	value: 24.683
	- type: map_at_1000
	value: 24.81
	- type: map_at_3
	value: 21.554000000000002
	- type: map_at_5
	value: 22.768
	- type: mrr_at_1
	value: 20.096
	- type: mrr_at_10
	value: 27.230999999999998
	- type: mrr_at_100
	value: 28.083999999999996
	- type: mrr_at_1000
	value: 28.166000000000004
	- type: mrr_at_3
	value: 25.212
	- type: mrr_at_5
	value: 26.32
	- type: ndcg_at_1
	value: 20.096
	- type: ndcg_at_10
	value: 27.989000000000004
	- type: ndcg_at_100
	value: 32.847
	- type: ndcg_at_1000
	value: 35.896
	- type: ndcg_at_3
	value: 24.116
	- type: ndcg_at_5
	value: 25.964
	- type: precision_at_1
	value: 20.096
	- type: precision_at_10
	value: 5
	- type: precision_at_100
	value: 0.8750000000000001
	- type: precision_at_1000
	value: 0.131
	- type: precision_at_3
	value: 11.207
	- type: precision_at_5
	value: 8.08
	- type: recall_at_1
	value: 16.829
	- type: recall_at_10
	value: 37.407000000000004
	- type: recall_at_100
	value: 59.101000000000006
	- type: recall_at_1000
	value: 81.024
	- type: recall_at_3
	value: 26.739
	- type: recall_at_5
	value: 31.524
	- type: map_at_1
	value: 24.138
	- type: map_at_10
	value: 32.275999999999996
	- type: map_at_100
	value: 33.416000000000004
	- type: map_at_1000
	value: 33.527
	- type: map_at_3
	value: 29.854000000000003
	- type: map_at_5
	value: 31.096
	- type: mrr_at_1
	value: 28.450999999999997
	- type: mrr_at_10
	value: 36.214
	- type: mrr_at_100
	value: 37.134
	- type: mrr_at_1000
	value: 37.198
	- type: mrr_at_3
	value: 34.001999999999995
	- type: mrr_at_5
	value: 35.187000000000005
	- type: ndcg_at_1
	value: 28.450999999999997
	- type: ndcg_at_10
	value: 37.166
	- type: ndcg_at_100
	value: 42.454
	- type: ndcg_at_1000
	value: 44.976
	- type: ndcg_at_3
	value: 32.796
	- type: ndcg_at_5
	value: 34.631
	- type: precision_at_1
	value: 28.450999999999997
	- type: precision_at_10
	value: 6.241
	- type: precision_at_100
	value: 0.9950000000000001
	- type: precision_at_1000
	value: 0.133
	- type: precision_at_3
	value: 14.801
	- type: precision_at_5
	value: 10.280000000000001
	- type: recall_at_1
	value: 24.138
	- type: recall_at_10
	value: 48.111
	- type: recall_at_100
	value: 71.245
	- type: recall_at_1000
	value: 88.986
	- type: recall_at_3
	value: 36.119
	- type: recall_at_5
	value: 40.846
	- type: map_at_1
	value: 23.244
	- type: map_at_10
	value: 31.227
	- type: map_at_100
	value: 33.007
	- type: map_at_1000
	value: 33.223
	- type: map_at_3
	value: 28.924
	- type: map_at_5
	value: 30.017
	- type: mrr_at_1
	value: 27.668
	- type: mrr_at_10
	value: 35.524
	- type: mrr_at_100
	value: 36.699
	- type: mrr_at_1000
	value: 36.759
	- type: mrr_at_3
	value: 33.366
	- type: mrr_at_5
	value: 34.552
	- type: ndcg_at_1
	value: 27.668
	- type: ndcg_at_10
	value: 36.381
	- type: ndcg_at_100
	value: 43.062
	- type: ndcg_at_1000
	value: 45.656
	- type: ndcg_at_3
	value: 32.501999999999995
	- type: ndcg_at_5
	value: 34.105999999999995
	- type: precision_at_1
	value: 27.668
	- type: precision_at_10
	value: 6.798
	- type: precision_at_100
	value: 1.492
	- type: precision_at_1000
	value: 0.234
	- type: precision_at_3
	value: 15.152
	- type: precision_at_5
	value: 10.791
	- type: recall_at_1
	value: 23.244
	- type: recall_at_10
	value: 45.979
	- type: recall_at_100
	value: 74.822
	- type: recall_at_1000
	value: 91.078
	- type: recall_at_3
	value: 34.925
	- type: recall_at_5
	value: 39.126
	- type: map_at_1
	value: 19.945
	- type: map_at_10
	value: 27.517999999999997
	- type: map_at_100
	value: 28.588
	- type: map_at_1000
	value: 28.682000000000002
	- type: map_at_3
	value: 25.345000000000002
	- type: map_at_5
	value: 26.555
	- type: mrr_at_1
	value: 21.996
	- type: mrr_at_10
	value: 29.845
	- type: mrr_at_100
	value: 30.775999999999996
	- type: mrr_at_1000
	value: 30.845
	- type: mrr_at_3
	value: 27.726
	- type: mrr_at_5
	value: 28.882
	- type: ndcg_at_1
	value: 21.996
	- type: ndcg_at_10
	value: 32.034
	- type: ndcg_at_100
	value: 37.185
	- type: ndcg_at_1000
	value: 39.645
	- type: ndcg_at_3
	value: 27.750999999999998
	- type: ndcg_at_5
	value: 29.805999999999997
	- type: precision_at_1
	value: 21.996
	- type: precision_at_10
	value: 5.065
	- type: precision_at_100
	value: 0.819
	- type: precision_at_1000
	value: 0.11399999999999999
	- type: precision_at_3
	value: 12.076
	- type: precision_at_5
	value: 8.392
	- type: recall_at_1
	value: 19.945
	- type: recall_at_10
	value: 43.62
	- type: recall_at_100
	value: 67.194
	- type: recall_at_1000
	value: 85.7
	- type: recall_at_3
	value: 32.15
	- type: recall_at_5
	value: 37.208999999999996
	- task:
	type: Retrieval
	dataset:
	name: MTEB ClimateFEVER
	type: climate-fever
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 18.279
	- type: map_at_10
	value: 31.052999999999997
	- type: map_at_100
	value: 33.125
	- type: map_at_1000
	value: 33.306000000000004
	- type: map_at_3
	value: 26.208
	- type: map_at_5
	value: 28.857
	- type: mrr_at_1
	value: 42.671
	- type: mrr_at_10
	value: 54.557
	- type: mrr_at_100
	value: 55.142
	- type: mrr_at_1000
	value: 55.169000000000004
	- type: mrr_at_3
	value: 51.488
	- type: mrr_at_5
	value: 53.439
	- type: ndcg_at_1
	value: 42.671
	- type: ndcg_at_10
	value: 41.276
	- type: ndcg_at_100
	value: 48.376000000000005
	- type: ndcg_at_1000
	value: 51.318
	- type: ndcg_at_3
	value: 35.068
	- type: ndcg_at_5
	value: 37.242
	- type: precision_at_1
	value: 42.671
	- type: precision_at_10
	value: 12.638
	- type: precision_at_100
	value: 2.045
	- type: precision_at_1000
	value: 0.26
	- type: precision_at_3
	value: 26.08
	- type: precision_at_5
	value: 19.805
	- type: recall_at_1
	value: 18.279
	- type: recall_at_10
	value: 46.946
	- type: recall_at_100
	value: 70.97200000000001
	- type: recall_at_1000
	value: 87.107
	- type: recall_at_3
	value: 31.147999999999996
	- type: recall_at_5
	value: 38.099
	- task:
	type: Retrieval
	dataset:
	name: MTEB DBPedia
	type: dbpedia-entity
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 8.573
	- type: map_at_10
	value: 19.747
	- type: map_at_100
	value: 28.205000000000002
	- type: map_at_1000
	value: 29.831000000000003
	- type: map_at_3
	value: 14.109
	- type: map_at_5
	value: 16.448999999999998
	- type: mrr_at_1
	value: 71
	- type: mrr_at_10
	value: 77.68599999999999
	- type: mrr_at_100
	value: 77.995
	- type: mrr_at_1000
	value: 78.00200000000001
	- type: mrr_at_3
	value: 76.292
	- type: mrr_at_5
	value: 77.029
	- type: ndcg_at_1
	value: 59.12500000000001
	- type: ndcg_at_10
	value: 43.9
	- type: ndcg_at_100
	value: 47.863
	- type: ndcg_at_1000
	value: 54.848
	- type: ndcg_at_3
	value: 49.803999999999995
	- type: ndcg_at_5
	value: 46.317
	- type: precision_at_1
	value: 71
	- type: precision_at_10
	value: 34.4
	- type: precision_at_100
	value: 11.063
	- type: precision_at_1000
	value: 1.989
	- type: precision_at_3
	value: 52.333
	- type: precision_at_5
	value: 43.7
	- type: recall_at_1
	value: 8.573
	- type: recall_at_10
	value: 25.615
	- type: recall_at_100
	value: 53.385000000000005
	- type: recall_at_1000
	value: 75.46000000000001
	- type: recall_at_3
	value: 15.429
	- type: recall_at_5
	value: 19.357
	- task:
	type: Classification
	dataset:
	name: MTEB EmotionClassification
	type: mteb/emotion
	config: default
	split: test
	revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
	metrics:
	- type: accuracy
	value: 47.989999999999995
	- type: f1
	value: 42.776314451497555
	- task:
	type: Retrieval
	dataset:
	name: MTEB FEVER
	type: fever
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 74.13499999999999
	- type: map_at_10
	value: 82.825
	- type: map_at_100
	value: 83.096
	- type: map_at_1000
	value: 83.111
	- type: map_at_3
	value: 81.748
	- type: map_at_5
	value: 82.446
	- type: mrr_at_1
	value: 79.553
	- type: mrr_at_10
	value: 86.654
	- type: mrr_at_100
	value: 86.774
	- type: mrr_at_1000
	value: 86.778
	- type: mrr_at_3
	value: 85.981
	- type: mrr_at_5
	value: 86.462
	- type: ndcg_at_1
	value: 79.553
	- type: ndcg_at_10
	value: 86.345
	- type: ndcg_at_100
	value: 87.32
	- type: ndcg_at_1000
	value: 87.58200000000001
	- type: ndcg_at_3
	value: 84.719
	- type: ndcg_at_5
	value: 85.677
	- type: precision_at_1
	value: 79.553
	- type: precision_at_10
	value: 10.402000000000001
	- type: precision_at_100
	value: 1.1119999999999999
	- type: precision_at_1000
	value: 0.11499999999999999
	- type: precision_at_3
	value: 32.413
	- type: precision_at_5
	value: 20.138
	- type: recall_at_1
	value: 74.13499999999999
	- type: recall_at_10
	value: 93.215
	- type: recall_at_100
	value: 97.083
	- type: recall_at_1000
	value: 98.732
	- type: recall_at_3
	value: 88.79
	- type: recall_at_5
	value: 91.259
	- task:
	type: Retrieval
	dataset:
	name: MTEB FiQA2018
	type: fiqa
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 18.298000000000002
	- type: map_at_10
	value: 29.901
	- type: map_at_100
	value: 31.528
	- type: map_at_1000
	value: 31.713
	- type: map_at_3
	value: 25.740000000000002
	- type: map_at_5
	value: 28.227999999999998
	- type: mrr_at_1
	value: 36.728
	- type: mrr_at_10
	value: 45.401
	- type: mrr_at_100
	value: 46.27
	- type: mrr_at_1000
	value: 46.315
	- type: mrr_at_3
	value: 42.978
	- type: mrr_at_5
	value: 44.29
	- type: ndcg_at_1
	value: 36.728
	- type: ndcg_at_10
	value: 37.456
	- type: ndcg_at_100
	value: 43.832
	- type: ndcg_at_1000
	value: 47
	- type: ndcg_at_3
	value: 33.694
	- type: ndcg_at_5
	value: 35.085
	- type: precision_at_1
	value: 36.728
	- type: precision_at_10
	value: 10.386
	- type: precision_at_100
	value: 1.701
	- type: precision_at_1000
	value: 0.22599999999999998
	- type: precision_at_3
	value: 22.479
	- type: precision_at_5
	value: 16.605
	- type: recall_at_1
	value: 18.298000000000002
	- type: recall_at_10
	value: 44.369
	- type: recall_at_100
	value: 68.098
	- type: recall_at_1000
	value: 87.21900000000001
	- type: recall_at_3
	value: 30.215999999999998
	- type: recall_at_5
	value: 36.861
	- task:
	type: Retrieval
	dataset:
	name: MTEB HotpotQA
	type: hotpotqa
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 39.568
	- type: map_at_10
	value: 65.061
	- type: map_at_100
	value: 65.896
	- type: map_at_1000
	value: 65.95100000000001
	- type: map_at_3
	value: 61.831
	- type: map_at_5
	value: 63.849000000000004
	- type: mrr_at_1
	value: 79.136
	- type: mrr_at_10
	value: 84.58200000000001
	- type: mrr_at_100
	value: 84.765
	- type: mrr_at_1000
	value: 84.772
	- type: mrr_at_3
	value: 83.684
	- type: mrr_at_5
	value: 84.223
	- type: ndcg_at_1
	value: 79.136
	- type: ndcg_at_10
	value: 72.622
	- type: ndcg_at_100
	value: 75.539
	- type: ndcg_at_1000
	value: 76.613
	- type: ndcg_at_3
	value: 68.065
	- type: ndcg_at_5
	value: 70.58
	- type: precision_at_1
	value: 79.136
	- type: precision_at_10
	value: 15.215
	- type: precision_at_100
	value: 1.7500000000000002
	- type: precision_at_1000
	value: 0.189
	- type: precision_at_3
	value: 44.011
	- type: precision_at_5
	value: 28.388999999999996
	- type: recall_at_1
	value: 39.568
	- type: recall_at_10
	value: 76.077
	- type: recall_at_100
	value: 87.481
	- type: recall_at_1000
	value: 94.56400000000001
	- type: recall_at_3
	value: 66.01599999999999
	- type: recall_at_5
	value: 70.97200000000001
	- task:
	type: Classification
	dataset:
	name: MTEB ImdbClassification
	type: mteb/imdb
	config: default
	split: test
	revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
	metrics:
	- type: accuracy
	value: 85.312
	- type: ap
	value: 80.36296867333715
	- type: f1
	value: 85.26613311552218
	- task:
	type: Retrieval
	dataset:
	name: MTEB MSMARCO
	type: msmarco
	config: default
	split: dev
	revision: None
	metrics:
	- type: map_at_1
	value: 23.363999999999997
	- type: map_at_10
	value: 35.711999999999996
	- type: map_at_100
	value: 36.876999999999995
	- type: map_at_1000
	value: 36.923
	- type: map_at_3
	value: 32.034
	- type: map_at_5
	value: 34.159
	- type: mrr_at_1
	value: 24.04
	- type: mrr_at_10
	value: 36.345
	- type: mrr_at_100
	value: 37.441
	- type: mrr_at_1000
	value: 37.480000000000004
	- type: mrr_at_3
	value: 32.713
	- type: mrr_at_5
	value: 34.824
	- type: ndcg_at_1
	value: 24.026
	- type: ndcg_at_10
	value: 42.531
	- type: ndcg_at_100
	value: 48.081
	- type: ndcg_at_1000
	value: 49.213
	- type: ndcg_at_3
	value: 35.044
	- type: ndcg_at_5
	value: 38.834
	- type: precision_at_1
	value: 24.026
	- type: precision_at_10
	value: 6.622999999999999
	- type: precision_at_100
	value: 0.941
	- type: precision_at_1000
	value: 0.104
	- type: precision_at_3
	value: 14.909
	- type: precision_at_5
	value: 10.871
	- type: recall_at_1
	value: 23.363999999999997
	- type: recall_at_10
	value: 63.426
	- type: recall_at_100
	value: 88.96300000000001
	- type: recall_at_1000
	value: 97.637
	- type: recall_at_3
	value: 43.095
	- type: recall_at_5
	value: 52.178000000000004
	- task:
	type: Classification
	dataset:
	name: MTEB MTOPDomainClassification (en)
	type: mteb/mtop_domain
	config: en
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 93.0095759233926
	- type: f1
	value: 92.78387794667408
	- task:
	type: Classification
	dataset:
	name: MTEB MTOPIntentClassification (en)
	type: mteb/mtop_intent
	config: en
	split: test
	revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
	metrics:
	- type: accuracy
	value: 75.0296397628819
	- type: f1
	value: 58.45699589820874
	- task:
	type: Classification
	dataset:
	name: MTEB MassiveIntentClassification (en)
	type: mteb/amazon_massive_intent
	config: en
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 73.45662407531944
	- type: f1
	value: 71.42364781421813
	- task:
	type: Classification
	dataset:
	name: MTEB MassiveScenarioClassification (en)
	type: mteb/amazon_massive_scenario
	config: en
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 77.07800941492937
	- type: f1
	value: 77.22799045640845
	- task:
	type: Clustering
	dataset:
	name: MTEB MedrxivClusteringP2P
	type: mteb/medrxiv-clustering-p2p
	config: default
	split: test
	revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
	metrics:
	- type: v_measure
	value: 34.531234379250606
	- task:
	type: Clustering
	dataset:
	name: MTEB MedrxivClusteringS2S
	type: mteb/medrxiv-clustering-s2s
	config: default
	split: test
	revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
	metrics:
	- type: v_measure
	value: 30.941490381193802
	- task:
	type: Reranking
	dataset:
	name: MTEB MindSmallReranking
	type: mteb/mind_small
	config: default
	split: test
	revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
	metrics:
	- type: map
	value: 30.3115090856725
	- type: mrr
	value: 31.290667638675757
	- task:
	type: Retrieval
	dataset:
	name: MTEB NFCorpus
	type: nfcorpus
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 5.465
	- type: map_at_10
	value: 13.03
	- type: map_at_100
	value: 16.057
	- type: map_at_1000
	value: 17.49
	- type: map_at_3
	value: 9.553
	- type: map_at_5
	value: 11.204
	- type: mrr_at_1
	value: 43.653
	- type: mrr_at_10
	value: 53.269
	- type: mrr_at_100
	value: 53.72
	- type: mrr_at_1000
	value: 53.761
	- type: mrr_at_3
	value: 50.929
	- type: mrr_at_5
	value: 52.461
	- type: ndcg_at_1
	value: 42.26
	- type: ndcg_at_10
	value: 34.673
	- type: ndcg_at_100
	value: 30.759999999999998
	- type: ndcg_at_1000
	value: 39.728
	- type: ndcg_at_3
	value: 40.349000000000004
	- type: ndcg_at_5
	value: 37.915
	- type: precision_at_1
	value: 43.653
	- type: precision_at_10
	value: 25.789
	- type: precision_at_100
	value: 7.754999999999999
	- type: precision_at_1000
	value: 2.07
	- type: precision_at_3
	value: 38.596000000000004
	- type: precision_at_5
	value: 33.251
	- type: recall_at_1
	value: 5.465
	- type: recall_at_10
	value: 17.148
	- type: recall_at_100
	value: 29.768
	- type: recall_at_1000
	value: 62.239
	- type: recall_at_3
	value: 10.577
	- type: recall_at_5
	value: 13.315
	- task:
	type: Retrieval
	dataset:
	name: MTEB NQ
	type: nq
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 37.008
	- type: map_at_10
	value: 52.467
	- type: map_at_100
	value: 53.342999999999996
	- type: map_at_1000
	value: 53.366
	- type: map_at_3
	value: 48.412
	- type: map_at_5
	value: 50.875
	- type: mrr_at_1
	value: 41.541
	- type: mrr_at_10
	value: 54.967
	- type: mrr_at_100
	value: 55.611
	- type: mrr_at_1000
	value: 55.627
	- type: mrr_at_3
	value: 51.824999999999996
	- type: mrr_at_5
	value: 53.763000000000005
	- type: ndcg_at_1
	value: 41.541
	- type: ndcg_at_10
	value: 59.724999999999994
	- type: ndcg_at_100
	value: 63.38700000000001
	- type: ndcg_at_1000
	value: 63.883
	- type: ndcg_at_3
	value: 52.331
	- type: ndcg_at_5
	value: 56.327000000000005
	- type: precision_at_1
	value: 41.541
	- type: precision_at_10
	value: 9.447
	- type: precision_at_100
	value: 1.1520000000000001
	- type: precision_at_1000
	value: 0.12
	- type: precision_at_3
	value: 23.262
	- type: precision_at_5
	value: 16.314999999999998
	- type: recall_at_1
	value: 37.008
	- type: recall_at_10
	value: 79.145
	- type: recall_at_100
	value: 94.986
	- type: recall_at_1000
	value: 98.607
	- type: recall_at_3
	value: 60.277
	- type: recall_at_5
	value: 69.407
	- task:
	type: Retrieval
	dataset:
	name: MTEB QuoraRetrieval
	type: quora
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 70.402
	- type: map_at_10
	value: 84.181
	- type: map_at_100
	value: 84.796
	- type: map_at_1000
	value: 84.81400000000001
	- type: map_at_3
	value: 81.209
	- type: map_at_5
	value: 83.085
	- type: mrr_at_1
	value: 81.02000000000001
	- type: mrr_at_10
	value: 87.263
	- type: mrr_at_100
	value: 87.36
	- type: mrr_at_1000
	value: 87.36
	- type: mrr_at_3
	value: 86.235
	- type: mrr_at_5
	value: 86.945
	- type: ndcg_at_1
	value: 81.01
	- type: ndcg_at_10
	value: 87.99900000000001
	- type: ndcg_at_100
	value: 89.217
	- type: ndcg_at_1000
	value: 89.33
	- type: ndcg_at_3
	value: 85.053
	- type: ndcg_at_5
	value: 86.703
	- type: precision_at_1
	value: 81.01
	- type: precision_at_10
	value: 13.336
	- type: precision_at_100
	value: 1.52
	- type: precision_at_1000
	value: 0.156
	- type: precision_at_3
	value: 37.14
	- type: precision_at_5
	value: 24.44
	- type: recall_at_1
	value: 70.402
	- type: recall_at_10
	value: 95.214
	- type: recall_at_100
	value: 99.438
	- type: recall_at_1000
	value: 99.928
	- type: recall_at_3
	value: 86.75699999999999
	- type: recall_at_5
	value: 91.44099999999999
	- task:
	type: Clustering
	dataset:
	name: MTEB RedditClustering
	type: mteb/reddit-clustering
	config: default
	split: test
	revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
	metrics:
	- type: v_measure
	value: 56.51721502758904
	- task:
	type: Clustering
	dataset:
	name: MTEB RedditClusteringP2P
	type: mteb/reddit-clustering-p2p
	config: default
	split: test
	revision: 282350215ef01743dc01b456c7f5241fa8937f16
	metrics:
	- type: v_measure
	value: 61.054808572333016
	- task:
	type: Retrieval
	dataset:
	name: MTEB SCIDOCS
	type: scidocs
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 4.578
	- type: map_at_10
	value: 11.036999999999999
	- type: map_at_100
	value: 12.879999999999999
	- type: map_at_1000
	value: 13.150999999999998
	- type: map_at_3
	value: 8.133
	- type: map_at_5
	value: 9.559
	- type: mrr_at_1
	value: 22.6
	- type: mrr_at_10
	value: 32.68
	- type: mrr_at_100
	value: 33.789
	- type: mrr_at_1000
	value: 33.854
	- type: mrr_at_3
	value: 29.7
	- type: mrr_at_5
	value: 31.480000000000004
	- type: ndcg_at_1
	value: 22.6
	- type: ndcg_at_10
	value: 18.616
	- type: ndcg_at_100
	value: 25.883
	- type: ndcg_at_1000
	value: 30.944
	- type: ndcg_at_3
	value: 18.136
	- type: ndcg_at_5
	value: 15.625
	- type: precision_at_1
	value: 22.6
	- type: precision_at_10
	value: 9.48
	- type: precision_at_100
	value: 1.991
	- type: precision_at_1000
	value: 0.321
	- type: precision_at_3
	value: 16.8
	- type: precision_at_5
	value: 13.54
	- type: recall_at_1
	value: 4.578
	- type: recall_at_10
	value: 19.213
	- type: recall_at_100
	value: 40.397
	- type: recall_at_1000
	value: 65.2
	- type: recall_at_3
	value: 10.208
	- type: recall_at_5
	value: 13.718
	- task:
	type: STS
	dataset:
	name: MTEB SICK-R
	type: mteb/sickr-sts
	config: default
	split: test
	revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
	metrics:
	- type: cos_sim_pearson
	value: 83.44288351714071
	- type: cos_sim_spearman
	value: 79.37995604564952
	- type: euclidean_pearson
	value: 81.1078874670718
	- type: euclidean_spearman
	value: 79.37995905980499
	- type: manhattan_pearson
	value: 81.03697527288986
	- type: manhattan_spearman
	value: 79.33490235296236
	- task:
	type: STS
	dataset:
	name: MTEB STS12
	type: mteb/sts12-sts
	config: default
	split: test
	revision: a0d554a64d88156834ff5ae9920b964011b16384
	metrics:
	- type: cos_sim_pearson
	value: 84.95557650436523
	- type: cos_sim_spearman
	value: 78.5190672399868
	- type: euclidean_pearson
	value: 81.58064025904707
	- type: euclidean_spearman
	value: 78.5190672399868
	- type: manhattan_pearson
	value: 81.52857930619889
	- type: manhattan_spearman
	value: 78.50421361308034
	- task:
	type: STS
	dataset:
	name: MTEB STS13
	type: mteb/sts13-sts
	config: default
	split: test
	revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
	metrics:
	- type: cos_sim_pearson
	value: 84.79128416228737
	- type: cos_sim_spearman
	value: 86.05402451477147
	- type: euclidean_pearson
	value: 85.46280267054289
	- type: euclidean_spearman
	value: 86.05402451477147
	- type: manhattan_pearson
	value: 85.46278563858236
	- type: manhattan_spearman
	value: 86.08079590861004
	- task:
	type: STS
	dataset:
	name: MTEB STS14
	type: mteb/sts14-sts
	config: default
	split: test
	revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
	metrics:
	- type: cos_sim_pearson
	value: 83.20623089568763
	- type: cos_sim_spearman
	value: 81.53786907061009
	- type: euclidean_pearson
	value: 82.82272250091494
	- type: euclidean_spearman
	value: 81.53786907061009
	- type: manhattan_pearson
	value: 82.78850494027013
	- type: manhattan_spearman
	value: 81.5135618083407
	- task:
	type: STS
	dataset:
	name: MTEB STS15
	type: mteb/sts15-sts
	config: default
	split: test
	revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
	metrics:
	- type: cos_sim_pearson
	value: 85.46366618397936
	- type: cos_sim_spearman
	value: 86.96566013336908
	- type: euclidean_pearson
	value: 86.62651697548931
	- type: euclidean_spearman
	value: 86.96565526364454
	- type: manhattan_pearson
	value: 86.58812160258009
	- type: manhattan_spearman
	value: 86.9336484321288
	- task:
	type: STS
	dataset:
	name: MTEB STS16
	type: mteb/sts16-sts
	config: default
	split: test
	revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
	metrics:
	- type: cos_sim_pearson
	value: 82.51858358641559
	- type: cos_sim_spearman
	value: 84.7652527954999
	- type: euclidean_pearson
	value: 84.23914783766861
	- type: euclidean_spearman
	value: 84.7652527954999
	- type: manhattan_pearson
	value: 84.22749648503171
	- type: manhattan_spearman
	value: 84.74527996746386
	- task:
	type: STS
	dataset:
	name: MTEB STS17 (en-en)
	type: mteb/sts17-crosslingual-sts
	config: en-en
	split: test
	revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
	metrics:
	- type: cos_sim_pearson
	value: 87.28026563313065
	- type: cos_sim_spearman
	value: 87.46928143824915
	- type: euclidean_pearson
	value: 88.30558762000372
	- type: euclidean_spearman
	value: 87.46928143824915
	- type: manhattan_pearson
	value: 88.10513330809331
	- type: manhattan_spearman
	value: 87.21069787834173
	- task:
	type: STS
	dataset:
	name: MTEB STS22 (en)
	type: mteb/sts22-crosslingual-sts
	config: en
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 62.376497134587375
	- type: cos_sim_spearman
	value: 65.0159550112516
	- type: euclidean_pearson
	value: 65.64572120879598
	- type: euclidean_spearman
	value: 65.0159550112516
	- type: manhattan_pearson
	value: 65.88143604989976
	- type: manhattan_spearman
	value: 65.17547297222434
	- task:
	type: STS
	dataset:
	name: MTEB STSBenchmark
	type: mteb/stsbenchmark-sts
	config: default
	split: test
	revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
	metrics:
	- type: cos_sim_pearson
	value: 84.22876368947644
	- type: cos_sim_spearman
	value: 85.46935577445318
	- type: euclidean_pearson
	value: 85.32830231392005
	- type: euclidean_spearman
	value: 85.46935577445318
	- type: manhattan_pearson
	value: 85.30353211758495
	- type: manhattan_spearman
	value: 85.42821085956945
	- task:
	type: Reranking
	dataset:
	name: MTEB SciDocsRR
	type: mteb/scidocs-reranking
	config: default
	split: test
	revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
	metrics:
	- type: map
	value: 80.60986667767133
	- type: mrr
	value: 94.29432314236236
	- task:
	type: Retrieval
	dataset:
	name: MTEB SciFact
	type: scifact
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 54.528
	- type: map_at_10
	value: 65.187
	- type: map_at_100
	value: 65.62599999999999
	- type: map_at_1000
	value: 65.657
	- type: map_at_3
	value: 62.352
	- type: map_at_5
	value: 64.025
	- type: mrr_at_1
	value: 57.333
	- type: mrr_at_10
	value: 66.577
	- type: mrr_at_100
	value: 66.88
	- type: mrr_at_1000
	value: 66.908
	- type: mrr_at_3
	value: 64.556
	- type: mrr_at_5
	value: 65.739
	- type: ndcg_at_1
	value: 57.333
	- type: ndcg_at_10
	value: 70.275
	- type: ndcg_at_100
	value: 72.136
	- type: ndcg_at_1000
	value: 72.963
	- type: ndcg_at_3
	value: 65.414
	- type: ndcg_at_5
	value: 67.831
	- type: precision_at_1
	value: 57.333
	- type: precision_at_10
	value: 9.5
	- type: precision_at_100
	value: 1.057
	- type: precision_at_1000
	value: 0.11199999999999999
	- type: precision_at_3
	value: 25.778000000000002
	- type: precision_at_5
	value: 17.2
	- type: recall_at_1
	value: 54.528
	- type: recall_at_10
	value: 84.356
	- type: recall_at_100
	value: 92.833
	- type: recall_at_1000
	value: 99.333
	- type: recall_at_3
	value: 71.283
	- type: recall_at_5
	value: 77.14999999999999
	- task:
	type: PairClassification
	dataset:
	name: MTEB SprintDuplicateQuestions
	type: mteb/sprintduplicatequestions-pairclassification
	config: default
	split: test
	revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
	metrics:
	- type: cos_sim_accuracy
	value: 99.74158415841585
	- type: cos_sim_ap
	value: 92.90048959850317
	- type: cos_sim_f1
	value: 86.35650810245687
	- type: cos_sim_precision
	value: 90.4709748083242
	- type: cos_sim_recall
	value: 82.6
	- type: dot_accuracy
	value: 99.74158415841585
	- type: dot_ap
	value: 92.90048959850317
	- type: dot_f1
	value: 86.35650810245687
	- type: dot_precision
	value: 90.4709748083242
	- type: dot_recall
	value: 82.6
	- type: euclidean_accuracy
	value: 99.74158415841585
	- type: euclidean_ap
	value: 92.90048959850317
	- type: euclidean_f1
	value: 86.35650810245687
	- type: euclidean_precision
	value: 90.4709748083242
	- type: euclidean_recall
	value: 82.6
	- type: manhattan_accuracy
	value: 99.74158415841585
	- type: manhattan_ap
	value: 92.87344692947894
	- type: manhattan_f1
	value: 86.38497652582159
	- type: manhattan_precision
	value: 90.29443838604145
	- type: manhattan_recall
	value: 82.8
	- type: max_accuracy
	value: 99.74158415841585
	- type: max_ap
	value: 92.90048959850317
	- type: max_f1
	value: 86.38497652582159
	- task:
	type: Clustering
	dataset:
	name: MTEB StackExchangeClustering
	type: mteb/stackexchange-clustering
	config: default
	split: test
	revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
	metrics:
	- type: v_measure
	value: 63.191648770424216
	- task:
	type: Clustering
	dataset:
	name: MTEB StackExchangeClusteringP2P
	type: mteb/stackexchange-clustering-p2p
	config: default
	split: test
	revision: 815ca46b2622cec33ccafc3735d572c266efdb44
	metrics:
	- type: v_measure
	value: 34.02944668730218
	- task:
	type: Reranking
	dataset:
	name: MTEB StackOverflowDupQuestions
	type: mteb/stackoverflowdupquestions-reranking
	config: default
	split: test
	revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
	metrics:
	- type: map
	value: 50.466386167525265
	- type: mrr
	value: 51.19071492233257
	- task:
	type: Summarization
	dataset:
	name: MTEB SummEval
	type: mteb/summeval
	config: default
	split: test
	revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
	metrics:
	- type: cos_sim_pearson
	value: 30.198022505886435
	- type: cos_sim_spearman
	value: 30.40170257939193
	- type: dot_pearson
	value: 30.198015316402614
	- type: dot_spearman
	value: 30.40170257939193
	- task:
	type: Retrieval
	dataset:
	name: MTEB TRECCOVID
	type: trec-covid
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 0.242
	- type: map_at_10
	value: 2.17
	- type: map_at_100
	value: 12.221
	- type: map_at_1000
	value: 28.63
	- type: map_at_3
	value: 0.728
	- type: map_at_5
	value: 1.185
	- type: mrr_at_1
	value: 94
	- type: mrr_at_10
	value: 97
	- type: mrr_at_100
	value: 97
	- type: mrr_at_1000
	value: 97
	- type: mrr_at_3
	value: 97
	- type: mrr_at_5
	value: 97
	- type: ndcg_at_1
	value: 89
	- type: ndcg_at_10
	value: 82.30499999999999
	- type: ndcg_at_100
	value: 61.839999999999996
	- type: ndcg_at_1000
	value: 53.381
	- type: ndcg_at_3
	value: 88.877
	- type: ndcg_at_5
	value: 86.05199999999999
	- type: precision_at_1
	value: 94
	- type: precision_at_10
	value: 87
	- type: precision_at_100
	value: 63.38
	- type: precision_at_1000
	value: 23.498
	- type: precision_at_3
	value: 94
	- type: precision_at_5
	value: 92
	- type: recall_at_1
	value: 0.242
	- type: recall_at_10
	value: 2.302
	- type: recall_at_100
	value: 14.979000000000001
	- type: recall_at_1000
	value: 49.638
	- type: recall_at_3
	value: 0.753
	- type: recall_at_5
	value: 1.226
	- task:
	type: Retrieval
	dataset:
	name: MTEB Touche2020
	type: webis-touche2020
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 3.006
	- type: map_at_10
	value: 11.805
	- type: map_at_100
	value: 18.146
	- type: map_at_1000
	value: 19.788
	- type: map_at_3
	value: 5.914
	- type: map_at_5
	value: 8.801
	- type: mrr_at_1
	value: 40.816
	- type: mrr_at_10
	value: 56.36600000000001
	- type: mrr_at_100
	value: 56.721999999999994
	- type: mrr_at_1000
	value: 56.721999999999994
	- type: mrr_at_3
	value: 52.041000000000004
	- type: mrr_at_5
	value: 54.796
	- type: ndcg_at_1
	value: 37.755
	- type: ndcg_at_10
	value: 29.863
	- type: ndcg_at_100
	value: 39.571
	- type: ndcg_at_1000
	value: 51.385999999999996
	- type: ndcg_at_3
	value: 32.578
	- type: ndcg_at_5
	value: 32.351
	- type: precision_at_1
	value: 40.816
	- type: precision_at_10
	value: 26.531
	- type: precision_at_100
	value: 7.796
	- type: precision_at_1000
	value: 1.555
	- type: precision_at_3
	value: 32.653
	- type: precision_at_5
	value: 33.061
	- type: recall_at_1
	value: 3.006
	- type: recall_at_10
	value: 18.738
	- type: recall_at_100
	value: 48.058
	- type: recall_at_1000
	value: 83.41300000000001
	- type: recall_at_3
	value: 7.166
	- type: recall_at_5
	value: 12.102
	- task:
	type: Classification
	dataset:
	name: MTEB ToxicConversationsClassification
	type: mteb/toxic_conversations_50k
	config: default
	split: test
	revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
	metrics:
	- type: accuracy
	value: 71.4178
	- type: ap
	value: 14.648781342150446
	- type: f1
	value: 55.07299194946378
	- task:
	type: Classification
	dataset:
	name: MTEB TweetSentimentExtractionClassification
	type: mteb/tweet_sentiment_extraction
	config: default
	split: test
	revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
	metrics:
	- type: accuracy
	value: 60.919637804187886
	- type: f1
	value: 61.24122013967399
	- task:
	type: Clustering
	dataset:
	name: MTEB TwentyNewsgroupsClustering
	type: mteb/twentynewsgroups-clustering
	config: default
	split: test
	revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
	metrics:
	- type: v_measure
	value: 49.207896583685695
	- task:
	type: PairClassification
	dataset:
	name: MTEB TwitterSemEval2015
	type: mteb/twittersemeval2015-pairclassification
	config: default
	split: test
	revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
	metrics:
	- type: cos_sim_accuracy
	value: 86.23114978840078
	- type: cos_sim_ap
	value: 74.26624727825818
	- type: cos_sim_f1
	value: 68.72377190817083
	- type: cos_sim_precision
	value: 64.56400742115028
	- type: cos_sim_recall
	value: 73.45646437994723
	- type: dot_accuracy
	value: 86.23114978840078
	- type: dot_ap
	value: 74.26624032659652
	- type: dot_f1
	value: 68.72377190817083
	- type: dot_precision
	value: 64.56400742115028
	- type: dot_recall
	value: 73.45646437994723
	- type: euclidean_accuracy
	value: 86.23114978840078
	- type: euclidean_ap
	value: 74.26624714480556
	- type: euclidean_f1
	value: 68.72377190817083
	- type: euclidean_precision
	value: 64.56400742115028
	- type: euclidean_recall
	value: 73.45646437994723
	- type: manhattan_accuracy
	value: 86.16558383501221
	- type: manhattan_ap
	value: 74.2091943976357
	- type: manhattan_f1
	value: 68.64221520524654
	- type: manhattan_precision
	value: 63.59135913591359
	- type: manhattan_recall
	value: 74.5646437994723
	- type: max_accuracy
	value: 86.23114978840078
	- type: max_ap
	value: 74.26624727825818
	- type: max_f1
	value: 68.72377190817083
	- task:
	type: PairClassification
	dataset:
	name: MTEB TwitterURLCorpus
	type: mteb/twitterurlcorpus-pairclassification
	config: default
	split: test
	revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
	metrics:
	- type: cos_sim_accuracy
	value: 89.3681841114604
	- type: cos_sim_ap
	value: 86.65166387498546
	- type: cos_sim_f1
	value: 79.02581944698774
	- type: cos_sim_precision
	value: 75.35796605434099
	- type: cos_sim_recall
	value: 83.06898675700647
	- type: dot_accuracy
	value: 89.3681841114604
	- type: dot_ap
	value: 86.65166019802056
	- type: dot_f1
	value: 79.02581944698774
	- type: dot_precision
	value: 75.35796605434099
	- type: dot_recall
	value: 83.06898675700647
	- type: euclidean_accuracy
	value: 89.3681841114604
	- type: euclidean_ap
	value: 86.65166462876266
	- type: euclidean_f1
	value: 79.02581944698774
	- type: euclidean_precision
	value: 75.35796605434099
	- type: euclidean_recall
	value: 83.06898675700647
	- type: manhattan_accuracy
	value: 89.36624364497226
	- type: manhattan_ap
	value: 86.65076471274106
	- type: manhattan_f1
	value: 79.07408783532733
	- type: manhattan_precision
	value: 76.41102972856527
	- type: manhattan_recall
	value: 81.92947336002464
	- type: max_accuracy
	value: 89.3681841114604
	- type: max_ap
	value: 86.65166462876266
	- type: max_f1
	value: 79.07408783532733
	---

	# magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF
	This model was converted to GGUF format from [`nomic-ai/nomic-embed-text-v1.5`](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
	Refer to the [original model card](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) for more details on the model.

	## Use with llama.cpp
	Install llama.cpp through brew (works on Mac and Linux)

	```bash
	brew install llama.cpp

	```
	Invoke the llama.cpp server or the CLI.

	### CLI:
	```bash
	llama-cli --hf-repo magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -p "The meaning to life and the universe is"
	```

	### Server:
	```bash
	llama-server --hf-repo magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -c 2048
	```

	Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

	Step 1: Clone llama.cpp from GitHub.
	```
	git clone https://github.com/ggerganov/llama.cpp
	```

	Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
	```
	cd llama.cpp && LLAMA_CURL=1 make
	```

	Step 3: Run inference through the main binary.
	```
	./llama-cli --hf-repo magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -p "The meaning to life and the universe is"
	```
	or
	```
	./llama-server --hf-repo magicunicorn/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -c 2048
	```