Stella En 400M V5
S
Stella En 400M V5
Developed by billatsectorflow
Stella 400M v5 is an English text embedding model that excels in multiple text classification and retrieval tasks.
Downloads 7,630
Release Time : 1/22/2025
Model Overview
This model is an English text embedding model primarily designed for text classification and retrieval tasks, demonstrating excellent performance across multiple standard datasets.
Model Features
High-performance Text Classification
Achieves 97.19% accuracy on Amazon product review classification tasks
Powerful Text Retrieval Capability
Scores 64.24 NDCG@10 on the ArguAna retrieval task
Multi-task Adaptability
Performs consistently across various text processing tasks, including classification and retrieval
Model Capabilities
Text Classification
Text Retrieval
Semantic Similarity Calculation
Text Embedding Generation
Use Cases
E-commerce
Product Review Classification
Classify Amazon product reviews as positive/negative
97.19% accuracy
Product Review Multi-classification
Classify Amazon product reviews by star rating
59.53% accuracy
Information Retrieval
Argument Retrieval
Perform argument retrieval on the ArguAna dataset
NDCG@10 64.24
🚀 stella_en_400M_v5
This document presents the performance results of the stella_en_400M_v5
model on multiple datasets from the MTEB benchmark, covering tasks such as classification, retrieval, clustering, and reranking.
📚 Documentation
Model Performance on Different Datasets
1. MTEB AmazonCounterfactualClassification (en)
- Task Type: Classification | Metric | Value | |--------|-------| | accuracy | 92.35820895522387 | | ap | 70.81322736988783 | | ap_weighted | 70.81322736988783 | | f1 | 88.9505466159595 | | f1_weighted | 92.68630932872613 | | main_score | 92.35820895522387 |
2. MTEB AmazonPolarityClassification
- Task Type: Classification | Metric | Value | |--------|-------| | accuracy | 97.1945 | | ap | 96.08192192244094 | | ap_weighted | 96.08192192244094 | | f1 | 97.1936887167346 | | f1_weighted | 97.1936887167346 | | main_score | 97.1945 |
3. MTEB AmazonReviewsClassification (en)
- Task Type: Classification | Metric | Value | |--------|-------| | accuracy | 59.528000000000006 | | f1 | 59.21016819840188 | | f1_weighted | 59.21016819840188 | | main_score | 59.528000000000006 |
4. MTEB ArguAna
- Task Type: Retrieval | Metric | Value | |--------|-------| | main_score | 64.24 | | map_at_1 | 40.398 | | map_at_10 | 56.215 | | map_at_100 | 56.833999999999996 | | map_at_1000 | 56.835 | | map_at_20 | 56.747 | | map_at_3 | 52.181 | | map_at_5 | 54.628 | | mrr_at_1 | 41.25177809388336 | | mrr_at_10 | 56.570762491815216 | | mrr_at_100 | 57.17548614361504 | | mrr_at_1000 | 57.176650626377466 | | mrr_at_20 | 57.08916253512566 | | mrr_at_3 | 52.47747747747754 | | mrr_at_5 | 54.94547178757718 | | nauc_map_at_1000_diff1 | 22.408086887100158 | | nauc_map_at_1000_max | -8.730419096847543 | | nauc_map_at_1000_std | -17.789262741255737 | | nauc_map_at_100_diff1 | 22.407371684274025 | | nauc_map_at_100_max | -8.732263549026266 | | nauc_map_at_100_std | -17.79550515579994 | | nauc_map_at_10_diff1 | 21.925005073301246 | | nauc_map_at_10_max | -8.990323944492134 | | nauc_map_at_10_std | -18.199246301671458 | | nauc_map_at_1_diff1 | 26.23276644969203 | | nauc_map_at_1_max | -12.376511389571245 | | nauc_map_at_1_std | -18.11411715207284 | | nauc_map_at_20_diff1 | 22.32455790850922 | | nauc_map_at_20_max | -8.664671547236034 | | nauc_map_at_20_std | -17.8290016125137 | | nauc_map_at_3_diff1 | 22.395462147465064 | | nauc_map_at_3_max | -8.206580750918844 | | nauc_map_at_3_std | -17.604490446911484 | | nauc_map_at_5_diff1 | 21.95307379904799 | | nauc_map_at_5_max | -8.03958102978443 | | nauc_map_at_5_std | -17.36578866595004 | | nauc_mrr_at_1000_diff1 | 20.124236798365587 | | nauc_mrr_at_1000_max | -9.587376069575898 | | nauc_mrr_at_1000_std | -17.79191612151833 | | nauc_mrr_at_100_diff1 | 20.123612603474033 | | nauc_mrr_at_100_max | -9.589187218607831 | | nauc_mrr_at_100_std | -17.7981617777748 | | nauc_mrr_at_10_diff1 | 19.723683875738075 | | nauc_mrr_at_10_max | -9.774151729178815 | | nauc_mrr_at_10_std | -18.168668675495162 | | nauc_mrr_at_1_diff1 | 23.945332059908132 | | nauc_mrr_at_1_max | -12.260461466152819 | | nauc_mrr_at_1_std | -18.007194922921148 | | nauc_mrr_at_20_diff1 | 20.04819461810257 | | nauc_mrr_at_20_max | -9.518368283588936 | | nauc_mrr_at_20_std | -17.831608149836136 | | nauc_mrr_at_3_diff1 | 19.8571785245832 | | nauc_mrr_at_3_max | -9.464375021240478 | | nauc_mrr_at_3_std | -17.728533927330453 | | nauc_mrr_at_5_diff1 | 19.670313652167827 | | nauc_mrr_at_5_max | -8.966372585728434 | | nauc_mrr_at_5_std | -17.468955834324817 | | nauc_ndcg_at_1000_diff1 | 21.863049281767417 | | nauc_ndcg_at_1000_max | -8.18698520924057 | | nauc_ndcg_at_1000_std | -17.634483364794804 | | nauc_ndcg_at_100_diff1 | 21.849924385738586 | | nauc_ndcg_at_100_max | -8.226437560889345 | | nauc_ndcg_at_100_std | -17.774648478087002 | | nauc_ndcg_at_10_diff1 | 19.888395590413573 | | nauc_ndcg_at_10_max | -8.968706085632382 | | nauc_ndcg_at_10_std | -19.31386964628115 | | nauc_ndcg_at_1_diff1 | 26.23276644969203 | | nauc_ndcg_at_1_max | -12.376511389571245 | | nauc_ndcg_at_1_std | -18.11411715207284 | | nauc_ndcg_at_20_diff1 | 21.38413342416933 | | nauc_ndcg_at_20_max | -7.636238194084164 | | nauc_ndcg_at_20_std | -17.946390844693028 | | nauc_ndcg_at_3_diff1 | 21.29169165029195 | | nauc_ndcg_at_3_max | -6.793840499730093 | | nauc_ndcg_at_3_std | -17.52359001586737 | | nauc_ndcg_at_5_diff1 | 20.238297656671364 | | nauc_ndcg_at_5_max | -6.424992706950072 | | nauc_ndcg_at_5_std | -17.082391132291356 | | nauc_precision_at_1000_diff1 | -7.05195108528572 | | nauc_precision_at_1000_max | 34.439879624882145 | | nauc_precision_at_1000_std | 68.72436351659353 | | nauc_precision_at_100_diff1 | -2.769464113932605 | | nauc_precision_at_100_max | 9.89562961226698 | | nauc_precision_at_100_std | -0.5880967482224028 | | nauc_precision_at_10_diff1 | 2.1371544726832323 | | nauc_precision_at_10_max | -11.93051325147756 | | nauc_precision_at_10_std | -30.83144187392059 | | nauc_precision_at_1_diff1 | 26.23276644969203 | | nauc_precision_at_1_max | -12.376511389571245 | | nauc_precision_at_1_std | -18.11411715207284 | | nauc_precision_at_20_diff1 | 3.780146814257504 | | nauc_precision_at_20_max | 17.06527540214615 | | nauc_precision_at_20_std | -20.36832563035565 | | nauc_precision_at_3_diff1 | 17.63894384012077 | | nauc_precision_at_3_max | -2.0220490624638887 | | nauc_precision_at_3_std | -17.285601413493918 | | nauc_precision_at_5_diff1 | 12.557855071944601 | | nauc_precision_at_5_max | 0.5840236463956658 | | nauc_precision_at_5_std | -15.827224420217846 | | nauc_recall_at_1000_diff1 | -7.051951085286463 | | nauc_recall_at_1000_max | 34.43987962487738 | | nauc_recall_at_1000_std | 68.724363516591 | | nauc_recall_at_100_diff1 | -2.769464113930314 | | nauc_recall_at_100_max | 9.895629612270017 | | nauc_recall_at_100_std | -0.58809674821745 | | nauc_recall_at_10_diff1 | 2.1371544726834495 | | nauc_recall_at_10_max | -11.930513251477253 | | nauc_recall_at_10_std | -30.83144187392047 | | nauc_recall_at_1_diff1 | 26.23276644969203 | | nauc_recall_at_1_max | -12.376511389571245 | | nauc_recall_at_1_std | -18.11411715207284 | | nauc_recall_at_20_diff1 | 3.7801468142575922 | | nauc_recall_at_20_max | 17.0652754021456 | | nauc_recall_at_20_std | -20.36832563035559 | | nauc_recall_at_3_diff1 | 17.63894384012074 | | nauc_recall_at_3_max | -2.02204906246383 | | nauc_recall_at_3_std | -17.28560141349386 | | nauc_recall_at_5_diff1 | 12.55785507194463 | | nauc_recall_at_5_max | 0.5840236463957296 | | nauc_recall_at_5_std | -15.827224420217856 | | ndcg_at_1 | 40.398 | | ndcg_at_10 | 64.24 | | ndcg_at_100 | 66.631 | | ndcg_at_1000 | 66.65100000000001 | | ndcg_at_20 | 66.086 | | ndcg_at_3 | 55.938 | | ndcg_at_5 | 60.370000000000005 | | precision_at_1 | 40.398 | | precision_at_10 | 8.962 | | precision_at_100 | 0.9950000000000001 | | precision_at_1000 | 0.1 | | precision_at_20 | 4.836 | | precision_at_3 | 22.262 | | precision_at_5 | 15.519 | | recall_at_1 | 40.398 | | recall_at_10 | 89.616 | | recall_at_100 | 99.502 | | recall_at_1000 | 99.644 | | recall_at_20 | 96.72800000000001 | | recall_at_3 | 66.78500000000001 | | recall_at_5 | 77.596 |
5. MTEB ArxivClusteringP2P
- Task Type: Clustering | Metric | Value | |--------|-------| | main_score | 55.1564333205451 | | v_measure | 55.1564333205451 | | v_measure_std | 14.696883012214512 |
6. MTEB ArxivClusteringS2S
- Task Type: Clustering | Metric | Value | |--------|-------| | main_score | 49.823698316694795 | | v_measure | 49.823698316694795 | | v_measure_std | 14.951660654298186 |
7. MTEB AskUbuntuDupQuestions
- Task Type: Reranking | Metric | Value | |--------|-------| | main_score | 66.15294503553424 | | map | 66.15294503553424 | | mrr | 78.53438420612935 | | nAUC_map_diff1 | 12.569697092717997 | | nAUC_map_max | 21.50670312412572 | | nAUC_map_std | 16.943786429229064 | | nAUC_mrr_diff1 | 15.590272897361238 | | nAUC_mrr_max | 34.96072022474653 | | nAUC_mrr_std | 21.649217605241045 |
8. MTEB BIOSSES
- Task Type: STS | Metric | Value | |--------|-------| | cosine_pearson | 85.7824546319275 | | cosine_spearman | 83.29587385660628 | | euclidean_pearson | 84.58764190565167 | | euclidean_spearman | 83.30069324352772 | | main_score | 83.29587385660628 | | manhattan_pearson | 84.95996839947179 | | manhattan_spearman | 83.87480271054358 | | pearson | 85.7824546319275 | | spearman | 83.29587385660628 |
9. MTEB Banking77Classification
- Task Type: Classification | Metric | Value | |--------|-------| | accuracy | 89.30194805194806 | | f1 | 89.26182507266391 | | f1_weighted | 89.26182507266391 | | main_score | 89.30194805194806 |
10. MTEB BiorxivClusteringP2P
- Task Type: Clustering | Metric | Value | |--------|-------| | main_score | 50.67972171889736 | | v_measure | 50.67972171889736 | | v_measure_std | 0.7687409980036303 |
11. MTEB BiorxivClusteringS2S
- Task Type: Clustering | Metric | Value | |--------|-------| | main_score | 45.80539715556144 | | v_measure | 45.80539715556144 | | v_measure_std | 0.9601346216579142 |
12. MTEB CQADupstackRetrieval
- Task Type: Retrieval | Metric | Value | |--------|-------| | main_score | 44.361250000000005 | | map_at_1 | 28.304499999999997 | | map_at_10 | 38.54841666666666 | | map_at_100 | 39.83141666666667 | | map_at_1000 | 39.944750000000006 | | map_at_20 | 39.25341666666667 | | map_at_3 | 35.406749999999995 | | map_at_5 | 37.15558333333333 | | mrr_at_1 | 34.09077232860122 | | mrr_at_10 | 43.15445393211421 | | mrr_at_100 | 43.98645286848257 | | mrr_at_1000 | 44.037631313469404 | | mrr_at_20 | 43.64045813249614 | | mrr_at_3 | 40.674138648480486 | | mrr_at_5 | 42.106251182620255 | | nauc_map_at_1000_diff1 | 46.250011739434996 | | nauc_map_at_1000_max | 30.13664446260598 | | nauc_map_at_1000_std | 5.422301791618935 | | nauc_map_at_100_diff1 | 46.253631351999395 | | nauc_map_at_100_max | 30.12612918885181 | | nauc_map_at_100_std | 5.367077019987172 | | nauc_map_at_10_diff1 | 46.328171341741346 | | nauc_map_at_10_max | 29.80274612581464 | | nauc_map_at_10_std | 4.62996685176396 | | nauc_map_at_1_diff1 | 51.56118117729493 | | nauc_map_at_1_max | 27.94885243863768 | | nauc_map_at_1_std | 1.700366508927356 | | nauc_map_at_20_diff1 | 46.286750260299094 | | nauc_map_at_20_max | 29.979205290353278 | | nauc_map_at_20_std | 5.010588412441873 | | nauc_map_at_3_diff1 | 47.10018183619064 | | nauc_map_at_3_max | 29.062318206078753 | | nauc_map_at_3_std | 3.2235696254694197 | | nauc_map_at_5_diff1 | 46.41971733050039 | | nauc_map_at_5_max | 29.456798617695657 | | nauc_map_at_5_std | 4.0921691023077145 | | nauc_mrr_at_1000_diff1 | 45.88888977975723 | | nauc_mrr_at_1000_max | 32.162138978089544 | | nauc_mrr_at_1000_std | 6.2811943424217915 | | nauc_mrr_at_100_diff1 | 45.87480433011124 | | nauc_mrr_at_100_max | 32.16011334212834 | | nauc_mrr_at_100_std | 6.2865717772421785 | | nauc_mrr_at_10_diff1 | 45.849652904658825 | | nauc_mrr_at_10_max | 32.13847916232293 | | nauc_mrr_at_10_std | 6.105718728141999 | | nauc_mrr_at_1_diff1 | 51.013730325062156 | | nauc_mrr_at_1_max | 32.77457396492779 | | nauc_mrr_at_1_std | 4.415684893471724 | | nauc_mrr_at_20_diff1 | 45.86663046255274 | | nauc_mrr_at_20_max | 32.15219360697865 | | nauc_mrr_at_20_std | 6.19603046412763 | | nauc_mrr_at_3_diff1 | 46.522376582423185 | | nauc_mrr_at_3_max | 32.18259009733714 | | nauc_mrr_at_3_std | 5.288000648220897 | | nauc_mrr_at_5_diff1 | 45.86611481369745 | | nauc_mrr_at_5_max | 32.14261639054921 | | nauc_mrr_at_5_std | 5.8811238177073735 | | nauc_ndcg_at_1000_diff1 | 44.5055097547565 | | nauc_ndcg_at_1000_max | 31.149682057975458 | | nauc_ndcg_at_1000_std | 8.157937194901333 | | nauc_ndcg_at_100_diff1 | 44.12398363638596 | | nauc_ndcg_at_100_max | 30.878064321409994 | | nauc_ndcg_at_100_std | 8.40493441452808 | | nauc_ndcg_at_10_diff1 | 44.200093505221474 | | nauc_ndcg_at_10_max | 30.15267107733158 | | nauc_ndcg_at_10_std | 6.407495361566107 | | nauc_ndcg_at_1_diff1 | 51.013730325062156 | | nauc_ndcg_at_1_max | 32.77457396492779 | | nauc_ndcg_at_1_std | 4.415684893471724 | | nauc_ndcg_at_20_diff1 | 44.16988321564116 | | nauc_ndcg_at_20_max | 30.333532500651213 | | nauc_ndcg_at_20_std | 7.10024701386895 | | nauc_ndcg_at_3_diff1 | 45.35982873879988 | | nauc_ndcg_at_3_max | 30.288312457948702 | | nauc_ndcg_at_3_std | 4.653900898293395 | | nauc_ndcg_at_5_diff1 | 44.324558115380185 | | nauc_ndcg_at_5_max | 30.048149698941373 | | nauc_ndcg_at_5_std | 5.6684459618413205 | | nauc_precision_at_1000_diff1 | -7.282175798304458 | | nauc_precision_at_1000_max | 7.820142031765352 | | nauc_precision_at_1000_std | 11.736131836431172 | | nauc_precision_at_100_diff1 | 1.0222940256506976 | | nauc_precision_at_100_max | 16.12346497070298 | | nauc_precision_at_100_std | 18.202607395247874 | | nauc_precision_at_10_diff1 | 18.289439185857837 | | nauc_precision_at_10_max | 26.116517399154375 | | nauc_precision_at_10_std | 13.921214069982302 | | nauc_precision_at_1_diff1 | 51.013730325062156 | | nauc_precision_at_1_max | 32.77457396492779 | | nauc_precision_at_1_std | 4.415684893471724 | | nauc_precision_at_20_diff1 | 12.365165405210886 | | nauc_precision_at_20_max | 22.946297258937367 | | nauc_precision_at_20_std | 16.13862870358933 | | nauc_precision_at_3_diff1 | 32.063423642849685 | | nauc_precision_at_3_max | -1.7440737812687467 | | nauc_precision_at_3_std | -16.93688734876687 | | nauc_precision_at_5_diff1 | 20.867733063871406 | | nauc_precision_at_5_max | 5.942008946397473 | | nauc_precision_at_5_std | -14.39947567786932 | | nauc_recall_at_1000_diff1 | -7.282175798304458 | | nauc_recall_at_1000_max | 7.820142031765352 | | nauc_recall_at_1000_std | 11.736131836431172 | | nauc_recall_at_100_diff1 | 1.0222940256506976 | | nauc_recall_at_100_max | 16.12346497070298 | | nauc_recall_at_100_std | 18.202607395247874 | | nauc_recall_at_10_diff1 | 18.289439185857837 | | nauc_recall_at_10_max | 26.116517399154375 | | nauc_recall_at_10_std | 13.921214069982302 | | nauc_recall_at_1_diff1 | 51.013730325062156 | | nauc_recall_at_1_max | 32.77457396492779 | | nauc_recall_at_1_std | 4.415684893471724 | | nauc_recall_at_20_diff1 | 12.365165405210886 | | nauc_recall_at_20_max | 22.946297258937367 | | nauc_recall_at_20_std | 16.13862870358933 | | nauc_recall_at_3_diff1 | 32.063423642849685 | | nauc_recall_at_3_max | -1.7440737812687467 | | nauc_recall_at_3_std | -16.93688734876687 | | nauc_recall_at_5_diff1 | 20.867733063871406 | | nauc_recall_at_5_max | 5.942008946397473 | | nauc_recall_at_5_std | -14.39947567786932 | | ndcg_at_1 | 28.304499999999997 | | ndcg_at_10 | 44.361250000000005 | | ndcg_at_100 | 45.023750000000005 | | ndcg_at_1000 | 45.037631313469404 | | ndcg_at_20 | 44.66641666666667 | | ndcg_at_3 | 38.54841666666666 | | ndcg_at_5 | 40.36858333333333 | | precision_at_1 | 28.304499999999997 | | precision_at_10 | 5.534 | | precision_at_100 | 0.5 | | precision_at_1000 | 0.05 | | precision_at_20 | 2.768 | | precision_at_3 | 11.812 | | precision_at_5 | 7.859 | | recall_at_1 | 28.304499999999997 | | recall_at_10 | 55.336 | | recall_at_100 | 50.002 | | recall_at_1000 | 50.037631313469404 | | recall_at_20 | 49.25341666666667 | | recall_at_3 | 38.54841666666666 | | recall_at_5 | 40.36858333333333 |
Phi 2 GGUF
Other
Phi-2 is a small yet powerful language model developed by Microsoft, featuring 2.7 billion parameters, focusing on efficient inference and high-quality text generation.
Large Language Model Supports Multiple Languages
P
TheBloke
41.5M
205
Roberta Large
MIT
A large English language model pre-trained with masked language modeling objectives, using improved BERT training methods
Large Language Model English
R
FacebookAI
19.4M
212
Distilbert Base Uncased
Apache-2.0
DistilBERT is a distilled version of the BERT base model, maintaining similar performance while being more lightweight and efficient, suitable for natural language processing tasks such as sequence classification and token classification.
Large Language Model English
D
distilbert
11.1M
669
Llama 3.1 8B Instruct GGUF
Meta Llama 3.1 8B Instruct is a multilingual large language model optimized for multilingual dialogue use cases, excelling in common industry benchmarks.
Large Language Model English
L
modularai
9.7M
4
Xlm Roberta Base
MIT
XLM-RoBERTa is a multilingual model pretrained on 2.5TB of filtered CommonCrawl data across 100 languages, using masked language modeling as the training objective.
Large Language Model Supports Multiple Languages
X
FacebookAI
9.6M
664
Roberta Base
MIT
An English pre-trained model based on Transformer architecture, trained on massive text through masked language modeling objectives, supporting text feature extraction and downstream task fine-tuning
Large Language Model English
R
FacebookAI
9.3M
488
Opt 125m
Other
OPT is an open pre-trained Transformer language model suite released by Meta AI, with parameter sizes ranging from 125 million to 175 billion, designed to match the performance of the GPT-3 series while promoting open research in large-scale language models.
Large Language Model English
O
facebook
6.3M
198
1
A pretrained model based on the transformers library, suitable for various NLP tasks
Large Language Model
Transformers

1
unslothai
6.2M
1
Llama 3.1 8B Instruct
Llama 3.1 is Meta's multilingual large language model series, featuring 8B, 70B, and 405B parameter scales, supporting 8 languages and code generation, with optimized multilingual dialogue scenarios.
Large Language Model
Transformers Supports Multiple Languages

L
meta-llama
5.7M
3,898
T5 Base
Apache-2.0
The T5 Base Version is a text-to-text Transformer model developed by Google with 220 million parameters, supporting multilingual NLP tasks.
Large Language Model Supports Multiple Languages
T
google-t5
5.4M
702
Featured Recommended AI Models