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EN
Giga Embeddings Instruct
G
Giga Embeddings Instruct
Developed by ai-sage
Giga Embeddings Instruct is a high-performance embedding model focused on text classification and retrieval tasks, excelling in multiple benchmark tests.
Downloads 11.97k
Release Time : 12/11/2024
Model Overview
This model is primarily used for text embedding and classification tasks, capable of efficiently processing text data such as Amazon product reviews, supporting multiple languages and complex query scenarios.
Model Features
High-accuracy classification
Achieves 94.5% accuracy in Amazon counterfactual classification tasks
Powerful retrieval capability
Performs excellently in ArguAna retrieval tasks, supporting complex query scenarios
Multi-metric optimization
Simultaneously optimizes multiple performance metrics including accuracy, F1 score, and average precision
Model Capabilities
Text classification
Information retrieval
Sentiment analysis
Text embedding
Use Cases
E-commerce
Product review classification
Classify Amazon product reviews as positive or negative
Achieves 94.3% accuracy in Amazon polarity classification tasks
Counterfactual review detection
Identify counterfactual reviews on Amazon platform
Achieves 94.5% accuracy in en-ext configuration
Information retrieval
Argument retrieval
Perform argument relevance retrieval on ArguAna dataset
NDCG@10 reaches 53.23
🚀 giga-embeddings-instruct
This document presents the performance metrics of the giga-embeddings-instruct model on multiple datasets in different tasks such as Classification, Retrieval, Reranking, and STS.
📚 Documentation
Model Performance Metrics
| Dataset Name | Task Type | Metrics | Value |
|---|---|---|---|
| MTEB AmazonCounterfactualClassification (en-ext) | Classification | accuracy | 94.5352323838081 |
| MTEB AmazonCounterfactualClassification (en-ext) | Classification | ap | 62.422648408367344 |
| MTEB AmazonCounterfactualClassification (en-ext) | Classification | ap_weighted | 62.422648408367344 |
| MTEB AmazonCounterfactualClassification (en-ext) | Classification | f1 | 87.13103677336655 |
| MTEB AmazonCounterfactualClassification (en-ext) | Classification | f1_weighted | 94.85637995412655 |
| MTEB AmazonCounterfactualClassification (en-ext) | Classification | main_score | 94.5352323838081 |
| MTEB AmazonCounterfactualClassification (en) | Classification | accuracy | 90.31343283582089 |
| MTEB AmazonCounterfactualClassification (en) | Classification | ap | 63.42364739316405 |
| MTEB AmazonCounterfactualClassification (en) | Classification | ap_weighted | 63.42364739316405 |
| MTEB AmazonCounterfactualClassification (en) | Classification | f1 | 85.54214552412623 |
| MTEB AmazonCounterfactualClassification (en) | Classification | f1_weighted | 90.59539168268289 |
| MTEB AmazonCounterfactualClassification (en) | Classification | main_score | 90.31343283582089 |
| MTEB AmazonPolarityClassification (default) | Classification | accuracy | 94.29605000000001 |
| MTEB AmazonPolarityClassification (default) | Classification | ap | 91.30887530384256 |
| MTEB AmazonPolarityClassification (default) | Classification | ap_weighted | 91.30887530384256 |
| MTEB AmazonPolarityClassification (default) | Classification | f1 | 94.29070662237378 |
| MTEB AmazonPolarityClassification (default) | Classification | f1_weighted | 94.29070662237378 |
| MTEB AmazonPolarityClassification (default) | Classification | main_score | 94.29605000000001 |
| MTEB ArguAna (default) | Retrieval | main_score | 53.227999999999994 |
| MTEB ArguAna (default) | Retrieval | map_at_1 | 27.595999999999997 |
| MTEB ArguAna (default) | Retrieval | map_at_10 | 43.756 |
| MTEB ArguAna (default) | Retrieval | map_at_100 | 44.674 |
| MTEB ArguAna (default) | Retrieval | map_at_1000 | 44.675 |
| MTEB ArguAna (default) | Retrieval | map_at_20 | 44.511 |
| MTEB ArguAna (default) | Retrieval | map_at_3 | 38.312000000000005 |
| MTEB ArguAna (default) | Retrieval | map_at_5 | 41.271 |
| MTEB ArguAna (default) | Retrieval | mrr_at_1 | 27.951635846372692 |
| MTEB ArguAna (default) | Retrieval | mrr_at_10 | 43.8683138025244 |
| MTEB ArguAna (default) | Retrieval | mrr_at_100 | 44.79916793634115 |
| MTEB ArguAna (default) | Retrieval | mrr_at_1000 | 44.800641832434614 |
| MTEB ArguAna (default) | Retrieval | mrr_at_20 | 44.63636850959653 |
| MTEB ArguAna (default) | Retrieval | mrr_at_3 | 38.383119962067305 |
| MTEB ArguAna (default) | Retrieval | mrr_at_5 | 41.41299193930774 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_1000_diff1 | 6.936710279308449 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_1000_max | -16.426102328143827 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_1000_std | -18.408713623781154 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_100_diff1 | 6.936900325690782 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_100_max | -16.424599448813982 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_100_std | -18.41002427519262 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_10_diff1 | 6.686089466049945 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_10_max | -16.277854443721235 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_10_std | -18.533367246025183 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_1_diff1 | 10.048892770421086 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_1_max | -18.88033774058785 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_1_std | -18.950654138263662 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_20_diff1 | 6.896257398324564 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_20_max | -16.28720522758851 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_20_std | -18.463554340157874 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_3_diff1 | 6.996349008138944 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_3_max | -16.895326699141894 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_3_std | -18.550696483491105 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_5_diff1 | 6.652257808997529 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_5_max | -16.616340120756664 |
| MTEB ArguAna (default) | Retrieval | nauc_map_at_5_std | -18.750380766744815 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_1000_diff1 | 5.675242976111991 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_1000_max | -16.992812047837067 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_1000_std | -18.32929497132872 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_100_diff1 | 5.6754937777142835 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_100_max | -16.991287123334946 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_100_std | -18.330604638796043 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_10_diff1 | 5.392768177635316 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_10_max | -16.891663162548255 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_10_std | -18.471864534496945 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_1_diff1 | 8.923777873913467 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_1_max | -18.81665268664494 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_1_std | -18.819665466571674 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_20_diff1 | 5.641752338928701 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_20_max | -16.85136568990159 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_20_std | -18.384362648232546 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_3_diff1 | 5.524316132813568 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_3_max | -17.723568343459988 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_3_std | -18.372688451025656 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_5_diff1 | 5.414405183203325 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_5_max | -17.288127460794154 |
| MTEB ArguAna (default) | Retrieval | nauc_mrr_at_5_std | -18.71123050851349 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_1000_diff1 | 6.487802962417493 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_1000_max | -15.76159401306176 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_1000_std | -18.15838595665605 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_100_diff1 | 6.48323468898899 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_100_max | -15.728467477722477 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_100_std | -18.197384218078643 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_10_diff1 | 5.423448018411026 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_10_max | -14.673502378215453 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_10_std | -18.837931889895316 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_1_diff1 | 10.048892770421086 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_1_max | -18.88033774058785 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_1_std | -18.950654138263662 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_20_diff1 | 6.369954849420038 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_20_max | -14.443991776264713 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_20_std | -18.416264332865836 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_3_diff1 | 6.224331563078568 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_3_max | -16.183370694913553 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_3_std | -18.559481650690337 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_5_diff1 | 5.659342042143408 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_5_max | -15.510631438356693 |
| MTEB ArguAna (default) | Retrieval | nauc_ndcg_at_5_std | -18.909647623269873 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_1000_diff1 | -45.740924328524436 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_1000_max | -4.436745319184523 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_1000_std | 57.94428979357973 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_100_diff1 | -23.751971897164438 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_100_max | 0.5109176204949021 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_100_std | 14.133130213074722 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_10_diff1 | -2.2741922400170953 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_10_max | -4.695134136659869 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_10_std | -21.566024184206757 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_1_diff1 | 10.048892770421086 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_1_max | -18.88033774058785 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_1_std | -18.950654138263662 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_20_diff1 | 2.366832261816588 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_20_max | 17.078759245976265 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_20_std | -17.573684824976628 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_3_diff1 | 4.062538060385958 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_3_max | -14.10949953336873 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_3_std | -18.626114079282416 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_5_diff1 | 2.425834990396102 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_5_max | -11.600278541101094 |
| MTEB ArguAna (default) | Retrieval | nauc_precision_at_5_std | -19.53326796179894 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_1000_diff1 | -45.740924328527974 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_1000_max | -4.4367453191877555 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_1000_std | 57.9442897935769 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_100_diff1 | -23.751971897160466 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_100_max | 0.5109176204928446 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_100_std | 14.133130213071956 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_10_diff1 | -2.2741922400170527 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_10_max | -4.695134136659742 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_10_std | -21.566024184206647 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_1_diff1 | 10.048892770421086 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_1_max | -18.88033774058785 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_1_std | -18.950654138263662 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_20_diff1 | 2.366832261816872 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_20_max | 17.078759245976432 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_20_std | -17.57368482497646 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_3_diff1 | 4.0625380603860055 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_3_max | -14.10949953336872 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_3_std | -18.626114079282395 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_5_diff1 | 2.425834990396135 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_5_max | -11.60027854110106 |
| MTEB ArguAna (default) | Retrieval | nauc_recall_at_5_std | -19.533267961798924 |
| MTEB ArguAna (default) | Retrieval | ndcg_at_1 | 27.595999999999997 |
| MTEB ArguAna (default) | Retrieval | ndcg_at_10 | 53.227999999999994 |
| MTEB ArguAna (default) | Retrieval | ndcg_at_100 | 56.931 |
| MTEB ArguAna (default) | Retrieval | ndcg_at_1000 | 56.967999999999996 |
| MTEB ArguAna (default) | Retrieval | ndcg_at_20 | 55.921 |
| MTEB ArguAna (default) | Retrieval | ndcg_at_3 | 41.908 |
| MTEB ArguAna (default) | Retrieval | ndcg_at_5 | 47.285 |
| MTEB ArguAna (default) | Retrieval | precision_at_1 | 27.595999999999997 |
| MTEB ArguAna (default) | Retrieval | precision_at_10 | 8.371 |
| MTEB ArguAna (default) | Retrieval | precision_at_100 | 0.9939999999999999 |
| MTEB ArguAna (default) | Retrieval | precision_at_1000 | 0.1 |
| MTEB ArguAna (default) | Retrieval | precision_at_20 | 4.712000000000001 |
| MTEB ArguAna (default) | Retrieval | precision_at_3 | 17.449 |
| MTEB ArguAna (default) | Retrieval | precision_at_5 | 13.100999999999999 |
| MTEB ArguAna (default) | Retrieval | recall_at_1 | 27.595999999999997 |
| MTEB ArguAna (default) | Retrieval | recall_at_10 | 83.71300000000001 |
| MTEB ArguAna (default) | Retrieval | recall_at_100 | 99.36 |
| MTEB ArguAna (default) | Retrieval | recall_at_1000 | 99.644 |
| MTEB ArguAna (default) | Retrieval | recall_at_20 | 94.23899999999999 |
| MTEB ArguAna (default) | Retrieval | recall_at_3 | 52.347 |
| MTEB ArguAna (default) | Retrieval | recall_at_5 | 65.505 |
| MTEB AskUbuntuDupQuestions (default) | Reranking | main_score | 58.18725222465893 |
| MTEB AskUbuntuDupQuestions (default) | Reranking | map | 58.18725222465893 |
| MTEB AskUbuntuDupQuestions (default) | Reranking | mrr | 69.84335839598998 |
| MTEB AskUbuntuDupQuestions (default) | Reranking | nAUC_map_diff1 | 19.11598989756231 |
| MTEB AskUbuntuDupQuestions (default) | Reranking | nAUC_map_max | 15.695053858587466 |
| MTEB AskUbuntuDupQuestions (default) | Reranking | nAUC_map_std | 22.147773436080342 |
| MTEB AskUbuntuDupQuestions (default) | Reranking | nAUC_mrr_diff1 | 25.38427130339339 |
| MTEB AskUbuntuDupQuestions (default) | Reranking | nAUC_mrr_max | 24.2962940173052 |
| MTEB AskUbuntuDupQuestions (default) | Reranking | nAUC_mrr_std | 20.846599304343176 |
| MTEB BIOSSES (default) | STS | cosine_pearson | 87.40814966131607 |
| MTEB BIOSSES (default) | STS | cosine_spearman | 84.21758160533057 |
| MTEB BIOSSES (default) | STS | euclidean_pearson | 86.68087011664755 |
| MTEB BIOSSES (default) | STS | euclidean_spearman | 84.21758160533057 |
| MTEB BIOSSES (default) | STS | main_score | 84.21758160533057 |
| MTEB BIOSSES (default) | STS | manhattan_pearson | 86.8885717540405 |
| MTEB BIOSSES (default) | STS | manhattan_spearman | 84.69409848718736 |
| MTEB BIOSSES (default) | STS | pearson | 87.40814966131607 |
| MTEB BIOSSES (default) | STS | spearman | 84.21758160533057 |
| MTEB Banking77Classification (default) | Classification | accuracy | 87.78571428571429 |
| MTEB Banking77Classification (default) | Classification | f1 | 87.55183393575304 |
| MTEB Banking77Classification (default) | Classification | f1_weighted | 87.55183393575307 |
| MTEB Banking77Classification (default) | Classification | main_score | 87.78571428571429 |
| MTEB CEDRClassification (default) | Classification | accuracy | 59.6894792773645 |
| MTEB CEDRClassification (default) | Classification | f1 | 59.07371458842751 |
| MTEB CEDRClassification (default) | Classification | lrap | 84.46838469713137 |
| MTEB CEDRClassification (default) | Classification | main_score | 59 |
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GTE-base-en-v1.5 is an English sentence transformer model focused on sentence similarity tasks, excelling in multiple text embedding benchmarks.
Text Embedding Transformers Supports Multiple Languages
Transformers Supports Multiple LanguagesG
Alibaba-NLP
1.5M
63
Gte Multilingual Base
Apache-2.0
GTE Multilingual Base is a multilingual sentence embedding model supporting over 50 languages, suitable for tasks like sentence similarity calculation.
Text Embedding Transformers Supports Multiple Languages
Transformers Supports Multiple LanguagesG
Alibaba-NLP
1.2M
246
Polybert
polyBERT is a chemical language model designed to achieve fully machine-driven ultrafast polymer informatics. It maps PSMILES strings into 600-dimensional dense fingerprints to numerically represent polymer chemical structures.
Text Embedding Transformers
TransformersP
kuelumbus
1.0M
5
Bert Base Turkish Cased Mean Nli Stsb Tr
Apache-2.0
A sentence embedding model based on Turkish BERT, optimized for semantic similarity tasks
Text Embedding Transformers Other
Transformers OtherB
emrecan
1.0M
40
GIST Small Embedding V0
MIT
A text embedding model fine-tuned based on BAAI/bge-small-en-v1.5, trained with the MEDI dataset and MTEB classification task datasets, optimized for query encoding in retrieval tasks.
Text Embedding
Safetensors English
Safetensors EnglishG
avsolatorio
945.68k
29
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