Snowflake/snowflake-arctic-embed-m-v1.5

Name: Snowflake/snowflake-arctic-embed-m-v1.5
Author: Snowflake

High-quality GGUF model

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0 📁 GGUF Files

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5 months ago 🔄 Last Updated

📋 Model Description

pipeline_tag: sentence-similarity tags:

sentence-transformers
feature-extraction
sentence-similarity
mteb
arctic
snowflake-arctic-embed
transformers.js

license: apache-2.0 model-index:

name: snowflake-arctic-embed-m-v1.5

results: - dataset: config: default name: MTEB ArguAna revision: c22ab2a51041ffd869aaddef7af8d8215647e41a split: test type: mteb/arguana metrics: - type: main_score value: 59.53000000000001 - type: mapat1 value: 34.282000000000004 - type: mapat10 value: 50.613 - type: mapat100 value: 51.269 - type: mapat1000 value: 51.271 - type: mapat20 value: 51.158 - type: mapat3 value: 45.626 - type: mapat5 value: 48.638 - type: mrrat1 value: 34.92176386913229 - type: mrrat10 value: 50.856081645555406 - type: mrrat100 value: 51.510739437069034 - type: mrrat1000 value: 51.51299498830165 - type: mrrat20 value: 51.39987941081724 - type: mrrat3 value: 45.993361782835514 - type: mrrat5 value: 48.88098624940742 - type: naucmapat1000diff1 value: 10.628675774160785 - type: naucmapat1000max value: -10.11742589992339 - type: naucmapat1000std value: -18.29277379812427 - type: naucmapat100diff1 value: 10.63250240035489 - type: naucmapat100max value: -10.112078786734363 - type: naucmapat100std value: -18.288524872706834 - 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dataset: config: default name: MTEB RedditClusteringP2P (default) revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 split: test type: mteb/reddit-clustering-p2p metrics: - type: main_score value: 61.8124222918822 - type: v_measure value: 61.8124222918822 - type: vmeasurestd value: 11.994472578100165 task: type: Clustering - dataset: config: default name: MTEB SICK-R (default) revision: 20a6d6f312dd54037fe07a32d58e5e168867909d split: test type: mteb/sickr-sts metrics: - type: cosine_pearson value: 77.63310776935984 - type: cosine_spearman value: 69.86468291111039 - type: euclidean_pearson value: 73.91537077798837 - type: euclidean_spearman value: 69.86468376650203 - type: main_score value: 69.86468291111039 - type: manhattan_pearson value: 73.68616048370464 - type: manhattan_spearman value: 69.76232036206659 - type: pearson value: 77.63310776935984 - type: spearman value: 69.86468291111039 task: type: STS - dataset: config: default name: MTEB STS12 (default) revision: a0d554a64d88156834ff5ae9920b964011b16384 split: test type: mteb/sts12-sts metrics: - type: cosine_pearson value: 57.71716838245049 - type: cosine_spearman value: 61.797855543446424 - type: euclidean_pearson value: 58.22958675325848 - type: euclidean_spearman value: 61.797855543446424 - type: main_score value: 61.797855543446424 - type: manhattan_pearson value: 57.63117544997929 - type: manhattan_spearman value: 61.3629404350085 - type: pearson value: 57.71716838245049 - type: spearman value: 61.797855543446424 task: type: STS - dataset: config: default name: MTEB STS13 (default) revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca split: test type: mteb/sts13-sts metrics: - type: cosine_pearson value: 82.30260026790903 - type: cosine_spearman value: 82.66959813070869 - type: euclidean_pearson value: 82.08383017580783 - type: euclidean_spearman value: 82.66959813070869 - type: main_score value: 82.66959813070869 - type: manhattan_pearson value: 81.77991451392153 - type: manhattan_spearman value: 82.3652534745606 - type: pearson value: 82.30260026790903 - type: spearman value: 82.66959813070869 task: type: STS - dataset: config: default name: MTEB STS14 (default) revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 split: test type: mteb/sts14-sts metrics: - type: cosine_pearson value: 71.50608384084478 - type: cosine_spearman value: 68.94968064977785 - type: euclidean_pearson value: 70.73381299949564 - type: euclidean_spearman value: 68.94968064977785 - type: main_score value: 68.94968064977785 - type: manhattan_pearson value: 70.5385486953787 - type: manhattan_spearman value: 68.82132770672365 - type: pearson value: 71.50608384084478 - type: spearman value: 68.94968064977785 task: type: STS - dataset: config: default name: MTEB STS15 (default) revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 split: test type: mteb/sts15-sts metrics: - type: cosine_pearson value: 73.66969825874907 - type: cosine_spearman value: 75.55374982088381 - type: euclidean_pearson value: 75.9339313749594 - type: euclidean_spearman value: 75.55374982088381 - type: main_score value: 75.55374982088381 - type: manhattan_pearson value: 75.88287553383817 - type: manhattan_spearman value: 75.50729812977688 - type: pearson value: 73.66969825874907 - type: spearman value: 75.55374982088381 task: type: STS - dataset: config: default name: MTEB STS16 (default) revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 split: test type: mteb/sts16-sts metrics: - type: cosine_pearson value: 74.5954724414016 - type: cosine_spearman value: 77.2688820850505 - type: euclidean_pearson value: 77.19866353971555 - type: euclidean_spearman value: 77.2688820850505 - type: main_score value: 77.2688820850505 - type: manhattan_pearson value: 77.27072603680978 - type: manhattan_spearman value: 77.29408453673607 - type: pearson value: 74.5954724414016 - type: spearman value: 77.2688820850505 task: type: STS - dataset: config: en-en name: MTEB STS17 (en-en) revision: faeb762787bd10488a50c8b5be4a3b82e411949c split: test type: mteb/sts17-crosslingual-sts metrics: - type: cosine_pearson value: 71.52588722654055 - type: cosine_spearman value: 74.97235736456061 - type: euclidean_pearson value: 74.51952528854038 - type: euclidean_spearman value: 74.97235736456061 - type: main_score value: 74.97235736456061 - type: manhattan_pearson value: 74.48272300884209 - type: manhattan_spearman value: 74.80633649415176 - type: pearson value: 71.52588722654055 - type: spearman value: 74.97235736456061 task: type: STS - dataset: config: en name: MTEB STS22 (en) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cosine_pearson value: 68.80031120401976 - type: cosine_spearman value: 69.07945196478491 - type: euclidean_pearson value: 68.99674496430792 - type: euclidean_spearman value: 69.07945196478491 - type: main_score value: 69.07945196478491 - type: manhattan_pearson value: 69.00236107775687 - type: manhattan_spearman value: 68.98064879049272 - type: pearson value: 68.80031120401976 - type: spearman value: 69.07945196478491 task: type: STS - dataset: config: default name: MTEB STSBenchmark (default) revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 split: test type: mteb/stsbenchmark-sts metrics: - type: cosine_pearson value: 65.6898007230089 - type: cosine_spearman value: 69.72386211803668 - type: euclidean_pearson value: 69.04523003701475 - type: euclidean_spearman value: 69.72386211803668 - type: main_score value: 69.72386211803668 - type: manhattan_pearson value: 68.80479743770702 - type: manhattan_spearman value: 69.43264575177459 - type: pearson value: 65.6898007230089 - type: spearman value: 69.72386211803668 task: type: STS - dataset: config: default name: MTEB SciDocsRR (default) revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab split: test type: mteb/scidocs-reranking metrics: - type: main_score value: 79.74088066874383 - type: map value: 79.74088066874383 - type: mrr value: 94.47697455050397 - type: nAUCmapdiff1 value: 8.036086256905502 - type: nAUCmapmax value: 54.88199803816819 - type: nAUCmapstd value: 69.16267942176574 - type: nAUCmrrdiff1 value: 50.020738477678115 - type: nAUCmrrmax value: 83.28922770326483 - type: nAUCmrrstd value: 83.63973501802224 task: type: Reranking - dataset: config: default name: MTEB SprintDuplicateQuestions (default) revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 split: test type: mteb/sprintduplicatequestions-pairclassification metrics: - type: cosine_accuracy value: 99.83861386138614 - type: cosineaccuracythreshold value: 74.75666999816895 - type: cosine_ap value: 96.15132792066652 - type: cosine_f1 value: 91.84890656063618 - type: cosinef1threshold value: 71.70594930648804 - type: cosine_precision value: 91.30434782608695 - type: cosine_recall value: 92.4 - type: dot_accuracy value: 99.83861386138614 - type: dotaccuracythreshold value: 74.75666999816895 - type: dot_ap value: 96.15132792066653 - type: dot_f1 value: 91.84890656063618 - type: dotf1threshold value: 71.70596122741699 - type: dot_precision value: 91.30434782608695 - type: dot_recall value: 92.4 - type: euclidean_accuracy value: 99.83861386138614 - type: euclideanaccuracythreshold value: 71.05395793914795 - type: euclidean_ap value: 96.15132792066652 - type: euclidean_f1 value: 91.84890656063618 - type: euclideanf1threshold value: 75.22505521774292 - type: euclidean_precision value: 91.30434782608695 - type: euclidean_recall value: 92.4 - type: main_score value: 96.15132792066653 - type: manhattan_accuracy value: 99.83564356435643 - type: manhattanaccuracythreshold value: 1547.6950645446777 - type: manhattan_ap value: 96.06151211452136 - type: manhattan_f1 value: 91.61676646706587 - type: manhattanf1threshold value: 1626.3608932495117 - type: manhattan_precision value: 91.43426294820716 - type: manhattan_recall value: 91.8 - type: max_ap value: 96.15132792066653 - type: max_f1 value: 91.84890656063618 - type: max_precision value: 91.43426294820716 - type: max_recall value: 92.4 - type: similarity_accuracy value: 99.83861386138614 - type: similarityaccuracythreshold value: 74.75666999816895 - type: similarity_ap value: 96.15132792066652 - type: similarity_f1 value: 91.84890656063618 - type: similarityf1threshold value: 71.70594930648804 - type: similarity_precision value: 91.30434782608695 - type: similarity_recall value: 92.4 task: type: PairClassification - dataset: config: default name: MTEB StackExchangeClustering (default) revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 split: test type: mteb/stackexchange-clustering metrics: - type: main_score value: 61.24120328328453 - type: v_measure value: 61.24120328328453 - type: vmeasurestd value: 3.9946560691100372 task: type: Clustering - dataset: config: default name: MTEB StackExchangeClusteringP2P (default) revision: 815ca46b2622cec33ccafc3735d572c266efdb44 split: test type: mteb/stackexchange-clustering-p2p metrics: - type: main_score value: 33.808268374864745 - type: v_measure value: 33.808268374864745 - type: vmeasurestd value: 1.2212188701887239 task: type: Clustering - dataset: config: default name: MTEB StackOverflowDupQuestions (default) revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 split: test type: mteb/stackoverflowdupquestions-reranking metrics: - type: main_score value: 52.19806018468037 - type: map value: 52.19806018468037 - type: mrr value: 52.98921462524404 - type: nAUCmapdiff1 value: 37.41443156995912 - type: nAUCmapmax value: 9.410262727675603 - type: nAUCmapstd value: 8.7094185014992 - type: nAUCmrrdiff1 value: 37.78202772392581 - type: nAUCmrrmax value: 10.517635536565816 - type: nAUCmrrstd value: 8.509423813772491 task: type: Reranking - dataset: config: default name: MTEB SummEval (default) revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c split: test type: mteb/summeval metrics: - type: cosine_pearson value: 30.48413700430812 - type: cosine_spearman value: 30.357162200875816 - type: dot_pearson value: 30.484140144824938 - type: dot_spearman value: 30.357162200875816 - type: main_score value: 30.357162200875816 - type: pearson value: 30.48413700430812 - type: spearman value: 30.357162200875816 task: type: Summarization - dataset: config: default name: MTEB ToxicConversationsClassification (default) revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de split: test type: mteb/toxicconversations50k metrics: - type: accuracy value: 66.8359375 - type: ap value: 12.482653786025985 - type: ap_weighted value: 12.482653786025985 - type: f1 value: 51.328608527332385 - type: f1_weighted value: 74.07974463955398 - type: main_score value: 66.8359375 task: type: Classification - dataset: config: default name: MTEB TweetSentimentExtractionClassification (default) revision: d604517c81ca91fe16a244d1248fc021f9ecee7a split: test type: mteb/tweetsentimentextraction metrics: - type: accuracy value: 53.907753254103 - type: f1 value: 54.22707647269581 - type: f1_weighted value: 53.611822984407695 - type: main_score value: 53.907753254103 task: type: Classification - dataset: config: default name: MTEB TwentyNewsgroupsClustering (default) revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 split: test type: mteb/twentynewsgroups-clustering metrics: - type: main_score value: 38.1364789307295 - type: v_measure value: 38.1364789307295 - type: vmeasurestd value: 2.0731634966352077 task: type: Clustering - dataset: config: default name: MTEB TwitterSemEval2015 (default) revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 split: test type: mteb/twittersemeval2015-pairclassification metrics: - type: cosine_accuracy value: 82.66674614054956 - type: cosineaccuracythreshold value: 79.80123162269592 - type: cosine_ap value: 63.28209719072804 - type: cosine_f1 value: 60.16389710903711 - type: cosinef1threshold value: 72.22893834114075 - type: cosine_precision value: 52.90232185748599 - type: cosine_recall value: 69.73614775725594 - type: dot_accuracy value: 82.66674614054956 - type: dotaccuracythreshold value: 79.8012375831604 - type: dot_ap value: 63.282103870645166 - type: dot_f1 value: 60.16389710903711 - type: dotf1threshold value: 72.22894430160522 - type: dot_precision value: 52.90232185748599 - type: dot_recall value: 69.73614775725594 - type: euclidean_accuracy value: 82.66674614054956 - type: euclideanaccuracythreshold value: 63.55905532836914 - type: euclidean_ap value: 63.282095399953164 - type: euclidean_f1 value: 60.16389710903711 - type: euclideanf1threshold value: 74.5265781879425 - type: euclidean_precision value: 52.90232185748599 - type: euclidean_recall value: 69.73614775725594 - type: main_score value: 63.282103870645166 - type: manhattan_accuracy value: 82.74423317637242 - type: manhattanaccuracythreshold value: 1415.380859375 - type: manhattan_ap value: 63.26931757839598 - type: manhattan_f1 value: 60.11014948859166 - type: manhattanf1threshold value: 1632.522201538086 - type: manhattan_precision value: 52.359506559624045 - type: manhattan_recall value: 70.55408970976254 - type: max_ap value: 63.282103870645166 - type: max_f1 value: 60.16389710903711 - type: max_precision value: 52.90232185748599 - type: max_recall value: 70.55408970976254 - type: similarity_accuracy value: 82.66674614054956 - type: similarityaccuracythreshold value: 79.80123162269592 - type: similarity_ap value: 63.28209719072804 - type: similarity_f1 value: 60.16389710903711 - type: similarityf1threshold value: 72.22893834114075 - type: similarity_precision value: 52.90232185748599 - type: similarity_recall value: 69.73614775725594 task: type: PairClassification - dataset: config: default name: MTEB TwitterURLCorpus (default) revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf split: test type: mteb/twitterurlcorpus-pairclassification metrics: - type: cosine_accuracy value: 88.10105949470253 - type: cosineaccuracythreshold value: 68.95147562026978 - type: cosine_ap value: 84.65516103854583 - type: cosine_f1 value: 76.54581123301605 - type: cosinef1threshold value: 63.92929553985596 - type: cosine_precision value: 72.46526344751685 - type: cosine_recall value: 81.11333538651063 - type: dot_accuracy value: 88.10105949470253 - type: dotaccuracythreshold value: 68.95147562026978 - type: dot_ap value: 84.65516301437592 - type: dot_f1 value: 76.54581123301605 - type: dotf1threshold value: 63.92928957939148 - type: dot_precision value: 72.46526344751685 - type: dot_recall value: 81.11333538651063 - type: euclidean_accuracy value: 88.10105949470253 - type: euclideanaccuracythreshold value: 78.80169153213501 - type: euclidean_ap value: 84.65517268264233 - type: euclidean_f1 value: 76.54581123301605 - type: euclideanf1threshold value: 84.93610620498657 - type: euclidean_precision value: 72.46526344751685 - type: euclidean_recall value: 81.11333538651063 - type: main_score value: 84.65517268264233 - type: manhattan_accuracy value: 88.08941669577366 - type: manhattanaccuracythreshold value: 1739.3169403076172 - type: manhattan_ap value: 84.64592398855694 - type: manhattan_f1 value: 76.62890540443034 - type: manhattanf1threshold value: 1861.344337463379 - type: manhattan_precision value: 72.09775967413442 - type: manhattan_recall value: 81.76778564829073 - type: max_ap value: 84.65517268264233 - type: max_f1 value: 76.62890540443034 - type: max_precision value: 72.46526344751685 - type: max_recall value: 81.76778564829073 - type: similarity_accuracy value: 88.10105949470253 - type: similarityaccuracythreshold value: 68.95147562026978 - type: similarity_ap value: 84.65516103854583 - type: similarity_f1 value: 76.54581123301605 - type: similarityf1threshold value: 63.92929553985596 - type: similarity_precision value: 72.46526344751685 - type: similarity_recall value: 81.11333538651063 task: type: PairClassification

Snowflake's Arctic-embed-m-v1.5

News | This Model | Usage | FAQ | Contact | License | Acknowledgement

News

12/11/2024: Release of Technical Report for 2.0 model

12/04/2024: Release of L-2.0 and M-2.0

07/26/2024: Release preprint [[2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining](https://arxiv.org/abs/2407.18887) on arXiv.

07/18/2024: Release of snowflake-arctic-embed-m-v1.5, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector. Details about the development of this model are available in the launch post on the Snowflake engineering blog.

05/10/2024: Release of the technical report on Arctic Embed

04/16/2024: Original release the snowflake-arctic-embed family of text embedding models.

This Model

This model is an updated version of snowflake-arctic-embed-m designed to improve embedding vector compressibility. This model achieves a slightly higher performance overall without compression, and it is additionally capable of retaining most of its retrieval quality even down to 128 byte embedding vectors through a combination of Matryoshka Representation Learning (MRL) and uniform scalar quanitization.

Model Name	MTEB Retrieval Score (NDCG @ 10)
snowflake-arctic-embed-m-v1.5	55.14
snowflake-arctic-embed-m	54.91

Compared to several other models trained with MRL to produce 256-dimensional embedding vectors, snowflake-arctic-embed-m-v1.5 retains a higher degree of original model quality and delivers better retrieval quality on the MTEB Retrieval benchmark.

Model	Model Parameters	MTEB Retrieval Score at 256 Dimensions (fraction of arctic-embed-m-v1.5)
Snowflake arctic-embed-m-v1.5	109M	54.2 (100%)
Google gecko	1200M	52.4 (97%)
OpenAI text-embedding-3-large	Not Published	51.7 (95%)
Nomic nomic-embed-text-v1.5	138M	50.8 (94%)

Additionally, this model was designed to pair well with a corpus-independent scalar quantization scheme to achieve great performance even in as little as 128 bytes per vector (24x compression compared to 768 dimensional vectors stored in float32).

Model Version	Dimensionality	Scalar Quantization	Bytes Per Vector (fraction of baseline)	MTEB Retrieval Score (fraction of baseline)	Vectors Per GB (improvement over baseline)
v1	768	None (float32)	3072 (100%)	54.9 (100%)	0.33M (1.0x)
v1	768	int8	768 (25%)	54.9 (100%)	1.3M (4x)
v1.5	768	int8	768 (25%)	55.1 (100%)	1.3M (4x)
v1.5	256	int8	256 (8.3%)	54.2 (99%)	3.9M (12x)
v1.5	256	int4	128 (4.2%)	53.7 (98%)	7.8M (24x)

NOTE: Good uniform scalar quantization ranges to use with this model (and which were used in the eval above), are -0.18 to +0.18 for 4bit and -0.3 to +0.3 for 8bit. For a detailed walkthrough of using integer quantization with snowflake-arctic-embed-m-v1.5, check out our example notebook on GitHub.

Usage

Using Sentence Transformers

You can use the sentence-transformers package to use any of the snowflake-arctic-embed models. Here's an example for snowflake-arctic-embed-m-v1.5.

import torch
from sentence_transformers import SentenceTransformer
from torch.nn.functional import normalize

Model constant.
MODEL_ID = "Snowflake/snowflake-arctic-embed-m-v1.5"

Your queries and docs.
queries = ['what is snowflake?', 'Where can I get the best tacos?']
documents = ['The Data Cloud!', 'Mexico City of Course!']

Load the model.
model = SentenceTransformer(MODEL_ID)

Generate text embeddings.
queryembeddings = model.encode(queries, promptname="query")
document_embeddings = model.encode(documents)

Scores via dotproduct.
scores = queryembeddings @ documentembeddings.T

Pretty-print the results.
for query, query_scores in zip(queries, scores):
    docscorepairs = list(zip(documents, query_scores))
    docscorepairs = sorted(docscorepairs, key=lambda x: x[1], reverse=True)
    print(f'Query: "{query}"')
    for document, score in docscorepairs:
        print(f'Score: {score:.4f} | Document: "{document}"')
    print()

#### OUTPUT ####
Query: "what is snowflake?"

Score: 0.3521 | Document: "The Data Cloud!"

Score: 0.2358 | Document: "Mexico City of Course!"


Query: "Where can I get the best tacos?"
Score: 0.3884 | Document: "Mexico City of Course!"
Score: 0.2389 | Document: "The Data Cloud!"
#

#### Variation: Truncated Embeddings ####
queryembeddings256 = normalize(torch.fromnumpy(queryembeddings)[:, :256])
documentembeddings256 = normalize(torch.fromnumpy(documentembeddings)[:, :256])
scores256 = queryembeddings256 @ documentembeddings_256.T

Pretty-print the results.
for query, queryscores in zip(queries, scores256):
    docscorepairs = sorted(zip(documents, query_scores), key=lambda x: x[1], reverse=True)
    print(f'Query: "{query}"')
    for document, score in docscorepairs:
        print(f'Score: {score:.4f} | Document: "{document}"')
    print()

#### OUTPUT ####
Query: "what is snowflake?"

Score: 0.3852 | Document: "The Data Cloud!"

Score: 0.2721 | Document: "Mexico City of Course!"


Query: "Where can I get the best tacos?"
Score: 0.4337 | Document: "Mexico City of Course!"
Score: 0.2886 | Document: "The Data Cloud!"
#

Using Huggingface transformers

You can use the transformers package to use an snowflake-arctic-embed model, too. For optimal retrieval quality, remember to use the CLS token for embeddings and to use the query prefix below (just on the query).

import torch
from torch.nn.functional import normalize
from transformers import AutoModel, AutoTokenizer

Model constants.
MODEL_ID = "Snowflake/snowflake-arctic-embed-m-v1.5"
QUERY_PREFIX = 'Represent this sentence for searching relevant passages: '

Your queries and docs.
queries  = ['what is snowflake?', 'Where can I get the best tacos?']
documents = ['The Data Cloud!', 'Mexico City of Course!']

Load the model and tokenizer.
tokenizer = AutoTokenizer.frompretrained(MODELID)
model = AutoModel.frompretrained(MODELID, addpoolinglayer=False)
model.eval()

Add query prefix and tokenize queries and docs.
querieswithprefix = [f"{QUERY_PREFIX}{q}" for q in queries]
querytokens = tokenizer(querieswithprefix, padding=True, truncation=True, returntensors='pt', max_length=512)
documenttokens =  tokenizer(documents, padding=True, truncation=True, returntensors='pt', max_length=512)

Use the model to generate text embeddings.
with torch.inference_mode():
    queryembeddings = model(querytokens)[0][:, 0]
    documentembeddings = model(documenttokens)[0][:, 0]

Remember to normalize embeddings.
queryembeddings = normalize(queryembeddings)
documentembeddings = normalize(documentembeddings)

Scores via dotproduct.
scores = queryembeddings @ documentembeddings.T

Pretty-print the results.
for query, query_scores in zip(queries, scores):
    docscorepairs = list(zip(documents, query_scores))
    docscorepairs = sorted(docscorepairs, key=lambda x: x[1], reverse=True)
    print(f'Query: "{query}"')
    for document, score in docscorepairs:
        print(f'Score: {score:.4f} | Document: "{document}"')
    print()

#### OUTPUT ####
Query: "what is snowflake?"

Score: 0.3521 | Document: "The Data Cloud!"

Score: 0.2358 | Document: "Mexico City of Course!"


Query: "Where can I get the best tacos?"
Score: 0.3884 | Document: "Mexico City of Course!"
Score: 0.2389 | Document: "The Data Cloud!"
#

#### Variation: Truncated Embeddings ####
queryembeddings256 = normalize(query_embeddings[:, :256])
documentembeddings256 = normalize(document_embeddings[:, :256])
scores256 = queryembeddings256 @ documentembeddings_256.T

Pretty-print the results.
for query, queryscores in zip(queries, scores256):
    docscorepairs = sorted(zip(documents, query_scores), key=lambda x: x[1], reverse=True)
    print(f'Query: "{query}"')
    for document, score in docscorepairs:
        print(f'Score: {score:.4f} | Document: "{document}"')
    print()

#### OUTPUT ####
Query: "what is snowflake?"

Score: 0.3852 | Document: "The Data Cloud!"

Score: 0.2721 | Document: "Mexico City of Course!"


Query: "Where can I get the best tacos?"
Score: 0.4337 | Document: "Mexico City of Course!"
Score: 0.2886 | Document: "The Data Cloud!"
#

Using Transformers.js

If you haven't already, you can install the Transformers.js JavaScript library from NPM by running:

npm i @xenova/transformers

You can then use the model to compute embeddings as follows:

import { pipeline, dot } from '@xenova/transformers';

// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-m-v1.5', {
    quantized: false, // Comment out this line to use the quantized version
});

// Generate sentence embeddings
const sentences = [
    'Represent this sentence for searching relevant passages: Where can I get the best tacos?',
    'The Data Cloud!',
    'Mexico City of Course!',
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });

// Compute similarity scores
const [sourceembeddings, ...documentembeddings ] = output.tolist();
const similarities = documentembeddings.map(x => dot(sourceembeddings, x));
console.log(similarities); // [0.15664823859882132, 0.24481869975470627]

Compressing to 128 bytes

This model is designed to generate embeddings which compress well down to 128 bytes via a two-part compression scheme:

Truncation and renormalization to 256 dimensions (a la Matryoskha Representation Learning, see the original paper for reference).
4-bit uniform scalar quantization of all 256 values to the same range (-0.18 to +0.18).

- For 8-bit uniform scalar quantization, the slightly wider range -0.3 to +0.3 tends to work slightly better given how much more granular 8-bit quantization is.

For an in-depth examples, check out our arctic-embed GitHub repositiory.

FAQ

TBD

Contact

Feel free to open an issue or pull request if you have any questions or suggestions about this project.
You also can email Daniel Campos([email protected]).

License

Arctic is licensed under the Apache-2. The released models can be used for commercial purposes free of charge.

Acknowledgement

We want to thank the open-source community, which has provided the great building blocks upon which we could make our models.
We thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Daniel Campos, for making these great models possible.
We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work.
We also thank the open-source community for producing the great models we could build on top of and making these releases possible.
Finally, we thank the researchers who created BEIR and MTEB benchmarks.
It is largely thanks to their tireless work to define what better looks like that we could improve model performance.

📂 GGUF File List

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📊 Model Information

🆔 Model ID: Snowflake/snowflake-arctic-embed-m-v1.5

📅 Created: 2 years ago

🔄 Last Updated: 5 months ago

📥 Downloads: 88.9K

❤️ Likes: 60

🎯 Difficulty: Beginner

🏷️ Tags

sentence-transformersonnxsafetensorsggufbertfeature-extractionsentence-similaritymtebarcticsnowflake-arctic-embedtransformers.jsarxiv:2412.04506arxiv:2407.18887arxiv:2405.05374arxiv:2205.13147license:apache-2.0model-indexautotrain_compatibletext-embeddings-inferenceendpoints_compatibleregion:us

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