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Refactor code: Pull leaderboards and models configurations out of the app.py (#106)
Browse files- Code refactor: moved leaderboards configs from app.py to .yaml files (21c6649839c15ca043a2cb24373c2f0b6eebc220)
- minor fixes (fd4838e20b8259f72b5979c5516824e36fd051a6)
- Caches model cards and dim_seq_size while first intiating the leaderboard (bbfe97ce69a629d673c2cc254c7d1c4e4caae5d1)
- Fix a weird bug that made the cicklabe model name fail to render in some boards (349b10b04819aa2b28caa334e65c4b6f954d76db)
- Fix column order on refresh (a20529c61b4aede14f52bec0f77666bbd89e593f)
- fix missing German clustering (9066f738f02c40a149891f929cb99446c3f9c504)
- Caches models metadata card to a temporary file to speed up initilization (6f8ad2faabf5533e5c8a879d0325632c0589f5d5)
- Clean some invalid tasks and columns for when loading the leaderboard and using the refresh button (879c7e7b01f9e14e39b654ef7f7a532d3327a071)
Co-authored-by: Eduardo Garcia <[email protected]>
- .gitignore +2 -1
- app.py +0 -0
- config.yaml +364 -0
- envs.py +48 -0
- model_meta.yaml +1160 -0
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*.pyc
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model_infos.json
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config:
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REPO_ID: "mteb/leaderboard"
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RESULTS_REPO: mteb/results
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LEADERBOARD_NAME: "MTEB Leaderboard"
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tasks:
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+
BitextMining:
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icon: "🎌"
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+
metric: f1
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+
metric_description: "[F1](https://huggingface.co/spaces/evaluate-metric/f1)"
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+
Classification:
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icon: "❤️"
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+
metric: accuracy
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metric_description: "[Accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy)"
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Clustering:
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icon: "✨"
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+
metric: v_measure
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metric_description: "Validity Measure (v_measure)"
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PairClassification:
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icon: "🎭"
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metric: cos_sim_ap
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metric_description: "Average Precision based on Cosine Similarities (cos_sim_ap)"
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Reranking:
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icon: "🥈"
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+
metric: map
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metric_description: "Mean Average Precision (MAP)"
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+
Retrieval:
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icon: "🔎"
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metric: ndcg_at_10
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metric_description: "Normalized Discounted Cumulative Gain @ k (ndcg_at_10)"
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+
STS:
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icon: "🤖"
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metric: cos_sim_spearman
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metric_description: "Spearman correlation based on cosine similarity"
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Summarization:
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icon: "📜"
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metric: cos_sim_spearman
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metric_description: "Spearman correlation based on cosine similarity"
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boards:
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en:
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title: English
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language_long: "English"
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has_overall: true
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acronym: null
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icon: null
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special_icons: null
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credits: null
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+
tasks:
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+
Classification:
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49 |
+
- AmazonCounterfactualClassification (en)
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+
- AmazonPolarityClassification
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+
- AmazonReviewsClassification (en)
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+
- Banking77Classification
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53 |
+
- EmotionClassification
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54 |
+
- ImdbClassification
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55 |
+
- MassiveIntentClassification (en)
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56 |
+
- MassiveScenarioClassification (en)
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57 |
+
- MTOPDomainClassification (en)
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58 |
+
- MTOPIntentClassification (en)
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59 |
+
- ToxicConversationsClassification
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60 |
+
- TweetSentimentExtractionClassification
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61 |
+
Clustering:
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62 |
+
- ArxivClusteringP2P
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63 |
+
- ArxivClusteringS2S
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64 |
+
- BiorxivClusteringP2P
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+
- BiorxivClusteringS2S
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66 |
+
- MedrxivClusteringP2P
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67 |
+
- MedrxivClusteringS2S
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68 |
+
- RedditClustering
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69 |
+
- RedditClusteringP2P
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70 |
+
- StackExchangeClustering
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71 |
+
- StackExchangeClusteringP2P
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72 |
+
- TwentyNewsgroupsClustering
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73 |
+
PairClassification:
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74 |
+
- SprintDuplicateQuestions
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75 |
+
- TwitterSemEval2015
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76 |
+
- TwitterURLCorpus
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77 |
+
Reranking:
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78 |
+
- AskUbuntuDupQuestions
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79 |
+
- MindSmallReranking
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80 |
+
- SciDocsRR
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81 |
+
- StackOverflowDupQuestions
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82 |
+
Retrieval:
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83 |
+
- ArguAna
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84 |
+
- ClimateFEVER
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85 |
+
- CQADupstackRetrieval
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86 |
+
- DBPedia
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87 |
+
- FEVER
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88 |
+
- FiQA2018
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89 |
+
- HotpotQA
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90 |
+
- MSMARCO
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91 |
+
- NFCorpus
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92 |
+
- NQ
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93 |
+
- QuoraRetrieval
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94 |
+
- SCIDOCS
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95 |
+
- SciFact
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96 |
+
- Touche2020
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97 |
+
- TRECCOVID
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98 |
+
STS:
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99 |
+
- BIOSSES
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+
- SICK-R
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+
- STS12
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- STS13
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- STS14
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- STS15
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- STS16
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+
- STS17 (en-en)
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107 |
+
- STS22 (en)
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108 |
+
- STSBenchmark
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109 |
+
Summarization:
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110 |
+
- SummEval
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111 |
+
en-x:
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title: "English-X"
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language_long: "117 (Pairs of: English & other language)"
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has_overall: false
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115 |
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acronym: null
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116 |
+
icon: null
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117 |
+
special_icons: null
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118 |
+
credits: null
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119 |
+
tasks:
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120 |
+
BitextMining: ['BUCC (de-en)', 'BUCC (fr-en)', 'BUCC (ru-en)', 'BUCC (zh-en)', 'Tatoeba (afr-eng)', 'Tatoeba (amh-eng)', 'Tatoeba (ang-eng)', 'Tatoeba (ara-eng)', 'Tatoeba (arq-eng)', 'Tatoeba (arz-eng)', 'Tatoeba (ast-eng)', 'Tatoeba (awa-eng)', 'Tatoeba (aze-eng)', 'Tatoeba (bel-eng)', 'Tatoeba (ben-eng)', 'Tatoeba (ber-eng)', 'Tatoeba (bos-eng)', 'Tatoeba (bre-eng)', 'Tatoeba (bul-eng)', 'Tatoeba (cat-eng)', 'Tatoeba (cbk-eng)', 'Tatoeba (ceb-eng)', 'Tatoeba (ces-eng)', 'Tatoeba (cha-eng)', 'Tatoeba (cmn-eng)', 'Tatoeba (cor-eng)', 'Tatoeba (csb-eng)', 'Tatoeba (cym-eng)', 'Tatoeba (dan-eng)', 'Tatoeba (deu-eng)', 'Tatoeba (dsb-eng)', 'Tatoeba (dtp-eng)', 'Tatoeba (ell-eng)', 'Tatoeba (epo-eng)', 'Tatoeba (est-eng)', 'Tatoeba (eus-eng)', 'Tatoeba (fao-eng)', 'Tatoeba (fin-eng)', 'Tatoeba (fra-eng)', 'Tatoeba (fry-eng)', 'Tatoeba (gla-eng)', 'Tatoeba (gle-eng)', 'Tatoeba (glg-eng)', 'Tatoeba (gsw-eng)', 'Tatoeba (heb-eng)', 'Tatoeba (hin-eng)', 'Tatoeba (hrv-eng)', 'Tatoeba (hsb-eng)', 'Tatoeba (hun-eng)', 'Tatoeba (hye-eng)', 'Tatoeba (ido-eng)', 'Tatoeba (ile-eng)', 'Tatoeba (ina-eng)', 'Tatoeba (ind-eng)', 'Tatoeba (isl-eng)', 'Tatoeba (ita-eng)', 'Tatoeba (jav-eng)', 'Tatoeba (jpn-eng)', 'Tatoeba (kab-eng)', 'Tatoeba (kat-eng)', 'Tatoeba (kaz-eng)', 'Tatoeba (khm-eng)', 'Tatoeba (kor-eng)', 'Tatoeba (kur-eng)', 'Tatoeba (kzj-eng)', 'Tatoeba (lat-eng)', 'Tatoeba (lfn-eng)', 'Tatoeba (lit-eng)', 'Tatoeba (lvs-eng)', 'Tatoeba (mal-eng)', 'Tatoeba (mar-eng)', 'Tatoeba (max-eng)', 'Tatoeba (mhr-eng)', 'Tatoeba (mkd-eng)', 'Tatoeba (mon-eng)', 'Tatoeba (nds-eng)', 'Tatoeba (nld-eng)', 'Tatoeba (nno-eng)', 'Tatoeba (nob-eng)', 'Tatoeba (nov-eng)', 'Tatoeba (oci-eng)', 'Tatoeba (orv-eng)', 'Tatoeba (pam-eng)', 'Tatoeba (pes-eng)', 'Tatoeba (pms-eng)', 'Tatoeba (pol-eng)', 'Tatoeba (por-eng)', 'Tatoeba (ron-eng)', 'Tatoeba (rus-eng)', 'Tatoeba (slk-eng)', 'Tatoeba (slv-eng)', 'Tatoeba (spa-eng)', 'Tatoeba (sqi-eng)', 'Tatoeba (srp-eng)', 'Tatoeba (swe-eng)', 'Tatoeba (swg-eng)', 'Tatoeba (swh-eng)', 'Tatoeba (tam-eng)', 'Tatoeba (tat-eng)', 'Tatoeba (tel-eng)', 'Tatoeba (tgl-eng)', 'Tatoeba (tha-eng)', 'Tatoeba (tuk-eng)', 'Tatoeba (tur-eng)', 'Tatoeba (tzl-eng)', 'Tatoeba (uig-eng)', 'Tatoeba (ukr-eng)', 'Tatoeba (urd-eng)', 'Tatoeba (uzb-eng)', 'Tatoeba (vie-eng)', 'Tatoeba (war-eng)', 'Tatoeba (wuu-eng)', 'Tatoeba (xho-eng)', 'Tatoeba (yid-eng)', 'Tatoeba (yue-eng)', 'Tatoeba (zsm-eng)']
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+
zh:
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title: Chinese
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+
language_long: Chinese
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+
has_overall: true
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+
acronym: C-MTEB
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icon: "🇨🇳"
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127 |
+
special_icons:
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128 |
+
Classification: "🧡"
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129 |
+
credits: "[FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding)"
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+
tasks:
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131 |
+
Classification:
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132 |
+
- AmazonReviewsClassification (zh)
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133 |
+
- IFlyTek
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134 |
+
- JDReview
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135 |
+
- MassiveIntentClassification (zh-CN)
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136 |
+
- MassiveScenarioClassification (zh-CN)
|
137 |
+
- MultilingualSentiment
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138 |
+
- OnlineShopping
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139 |
+
- TNews
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140 |
+
- Waimai
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141 |
+
Clustering:
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142 |
+
- CLSClusteringP2P
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143 |
+
- CLSClusteringS2S
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144 |
+
- ThuNewsClusteringP2P
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145 |
+
- ThuNewsClusteringS2S
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146 |
+
PairClassification:
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147 |
+
- Cmnli
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148 |
+
- Ocnli
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149 |
+
Reranking:
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150 |
+
- CMedQAv1
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151 |
+
- CMedQAv2
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152 |
+
- MMarcoReranking
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153 |
+
- T2Reranking
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154 |
+
Retrieval:
|
155 |
+
- CmedqaRetrieval
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156 |
+
- CovidRetrieval
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157 |
+
- DuRetrieval
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158 |
+
- EcomRetrieval
|
159 |
+
- MedicalRetrieval
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160 |
+
- MMarcoRetrieval
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161 |
+
- T2Retrieval
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162 |
+
- VideoRetrieval
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163 |
+
STS:
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164 |
+
- AFQMC
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165 |
+
- ATEC
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166 |
+
- BQ
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167 |
+
- LCQMC
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168 |
+
- PAWSX
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169 |
+
- QBQTC
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170 |
+
- STS22 (zh)
|
171 |
+
- STSB
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172 |
+
da:
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173 |
+
title: Danish
|
174 |
+
language_long: Danish
|
175 |
+
has_overall: false
|
176 |
+
acronym: null
|
177 |
+
icon: "🇩🇰"
|
178 |
+
special_icons:
|
179 |
+
Classification: "🤍"
|
180 |
+
credits: "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)"
|
181 |
+
tasks:
|
182 |
+
BitextMining:
|
183 |
+
- BornholmBitextMining
|
184 |
+
Classification:
|
185 |
+
- AngryTweetsClassification
|
186 |
+
- DanishPoliticalCommentsClassification
|
187 |
+
- DKHateClassification
|
188 |
+
- LccSentimentClassification
|
189 |
+
- MassiveIntentClassification (da)
|
190 |
+
- MassiveScenarioClassification (da)
|
191 |
+
- NordicLangClassification
|
192 |
+
- ScalaDaClassification
|
193 |
+
fr:
|
194 |
+
title: French
|
195 |
+
language_long: "French"
|
196 |
+
has_overall: true
|
197 |
+
acronym: "F-MTEB"
|
198 |
+
icon: "🇫🇷"
|
199 |
+
special_icons:
|
200 |
+
Classification: "💙"
|
201 |
+
credits: "[Lyon-NLP](https://github.com/Lyon-NLP): [Gabriel Sequeira](https://github.com/GabrielSequeira), [Imene Kerboua](https://github.com/imenelydiaker), [Wissam Siblini](https://github.com/wissam-sib), [Mathieu Ciancone](https://github.com/MathieuCiancone), [Marion Schaeffer](https://github.com/schmarion)"
|
202 |
+
tasks:
|
203 |
+
Classification:
|
204 |
+
- AmazonReviewsClassification (fr)
|
205 |
+
- MasakhaNEWSClassification (fra)
|
206 |
+
- MassiveIntentClassification (fr)
|
207 |
+
- MassiveScenarioClassification (fr)
|
208 |
+
- MTOPDomainClassification (fr)
|
209 |
+
- MTOPIntentClassification (fr)
|
210 |
+
Clustering:
|
211 |
+
- AlloProfClusteringP2P
|
212 |
+
- AlloProfClusteringS2S
|
213 |
+
- HALClusteringS2S
|
214 |
+
- MLSUMClusteringP2P
|
215 |
+
- MLSUMClusteringS2S
|
216 |
+
- MasakhaNEWSClusteringP2P (fra)
|
217 |
+
- MasakhaNEWSClusteringS2S (fra)
|
218 |
+
PairClassification:
|
219 |
+
- OpusparcusPC (fr)
|
220 |
+
- PawsX (fr)
|
221 |
+
Reranking:
|
222 |
+
- AlloprofReranking
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223 |
+
- SyntecReranking
|
224 |
+
Retrieval:
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225 |
+
- AlloprofRetrieval
|
226 |
+
- BSARDRetrieval
|
227 |
+
- MintakaRetrieval (fr)
|
228 |
+
- SyntecRetrieval
|
229 |
+
- XPQARetrieval (fr)
|
230 |
+
STS:
|
231 |
+
- STS22 (fr)
|
232 |
+
- STSBenchmarkMultilingualSTS (fr)
|
233 |
+
- SICKFr
|
234 |
+
Summarization:
|
235 |
+
- SummEvalFr
|
236 |
+
'no':
|
237 |
+
title: Norwegian
|
238 |
+
language_long: "Norwegian Bokmål"
|
239 |
+
has_overall: false
|
240 |
+
acronym: null
|
241 |
+
icon: "🇳🇴"
|
242 |
+
special_icons:
|
243 |
+
Classification: "💙"
|
244 |
+
credits: "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)"
|
245 |
+
tasks:
|
246 |
+
Classification: &id001
|
247 |
+
- NoRecClassification
|
248 |
+
- NordicLangClassification
|
249 |
+
- NorwegianParliament
|
250 |
+
- MassiveIntentClassification (nb)
|
251 |
+
- MassiveScenarioClassification (nb)
|
252 |
+
- ScalaNbClassification
|
253 |
+
law:
|
254 |
+
title: Law
|
255 |
+
language_long: "English, German, Chinese"
|
256 |
+
has_overall: false
|
257 |
+
acronym: null
|
258 |
+
icon: "⚖️"
|
259 |
+
special_icons: null
|
260 |
+
credits: "[Voyage AI](https://www.voyageai.com/)"
|
261 |
+
tasks:
|
262 |
+
Retrieval:
|
263 |
+
- AILACasedocs
|
264 |
+
- AILAStatutes
|
265 |
+
- GerDaLIRSmall
|
266 |
+
- LeCaRDv2
|
267 |
+
- LegalBenchConsumerContractsQA
|
268 |
+
- LegalBenchCorporateLobbying
|
269 |
+
- LegalQuAD
|
270 |
+
- LegalSummarization
|
271 |
+
de:
|
272 |
+
title: German
|
273 |
+
language_long: "German"
|
274 |
+
has_overall: false
|
275 |
+
acronym: null
|
276 |
+
icon: "🇩🇪"
|
277 |
+
special_icons: null
|
278 |
+
credits: "[Silvan](https://github.com/slvnwhrl)"
|
279 |
+
tasks:
|
280 |
+
Clustering:
|
281 |
+
- BlurbsClusteringP2P
|
282 |
+
- BlurbsClusteringS2S
|
283 |
+
- TenKGnadClusteringP2P
|
284 |
+
- TenKGnadClusteringS2S
|
285 |
+
pl:
|
286 |
+
title: Polish
|
287 |
+
language_long: Polish
|
288 |
+
has_overall: true
|
289 |
+
acronym: null
|
290 |
+
icon: "🇵🇱"
|
291 |
+
special_icons:
|
292 |
+
Classification: "🤍"
|
293 |
+
credits: "[Rafał Poświata](https://github.com/rafalposwiata)"
|
294 |
+
tasks:
|
295 |
+
Classification:
|
296 |
+
- AllegroReviews
|
297 |
+
- CBD
|
298 |
+
- MassiveIntentClassification (pl)
|
299 |
+
- MassiveScenarioClassification (pl)
|
300 |
+
- PAC
|
301 |
+
- PolEmo2.0-IN
|
302 |
+
- PolEmo2.0-OUT
|
303 |
+
Clustering:
|
304 |
+
- 8TagsClustering
|
305 |
+
PairClassification:
|
306 |
+
- CDSC-E
|
307 |
+
- PPC
|
308 |
+
- PSC
|
309 |
+
- SICK-E-PL
|
310 |
+
Retrieval:
|
311 |
+
- ArguAna-PL
|
312 |
+
- DBPedia-PL
|
313 |
+
- FiQA-PL
|
314 |
+
- HotpotQA-PL
|
315 |
+
- MSMARCO-PL
|
316 |
+
- NFCorpus-PL
|
317 |
+
- NQ-PL
|
318 |
+
- Quora-PL
|
319 |
+
- SCIDOCS-PL
|
320 |
+
- SciFact-PL
|
321 |
+
- TRECCOVID-PL
|
322 |
+
STS:
|
323 |
+
- CDSC-R
|
324 |
+
- SICK-R-PL
|
325 |
+
- STS22 (pl)
|
326 |
+
se:
|
327 |
+
title: Swedish
|
328 |
+
language_long: Swedish
|
329 |
+
has_overall: false
|
330 |
+
acronym: null
|
331 |
+
icon: "🇸🇪"
|
332 |
+
special_icons:
|
333 |
+
Classification: "💛"
|
334 |
+
credits: "[Kenneth Enevoldsen](https://github.com/KennethEnevoldsen), [scandinavian-embedding-benchmark](https://kennethenevoldsen.github.io/scandinavian-embedding-benchmark/)"
|
335 |
+
tasks:
|
336 |
+
Classification:
|
337 |
+
- NoRecClassification
|
338 |
+
- NordicLangClassification
|
339 |
+
- NorwegianParliament
|
340 |
+
- MassiveIntentClassification (nb)
|
341 |
+
- MassiveScenarioClassification (nb)
|
342 |
+
- ScalaNbClassification
|
343 |
+
other-cls:
|
344 |
+
title: "Other Languages"
|
345 |
+
language_long: "47 (Only languages not included in the other tabs)"
|
346 |
+
has_overall: false
|
347 |
+
acronym: null
|
348 |
+
icon: null
|
349 |
+
special_icons:
|
350 |
+
Classification: "💜💚💙"
|
351 |
+
credits: null
|
352 |
+
tasks:
|
353 |
+
Classification: ['AmazonCounterfactualClassification (de)', 'AmazonCounterfactualClassification (ja)', 'AmazonReviewsClassification (de)', 'AmazonReviewsClassification (es)', 'AmazonReviewsClassification (fr)', 'AmazonReviewsClassification (ja)', 'AmazonReviewsClassification (zh)', 'MTOPDomainClassification (de)', 'MTOPDomainClassification (es)', 'MTOPDomainClassification (fr)', 'MTOPDomainClassification (hi)', 'MTOPDomainClassification (th)', 'MTOPIntentClassification (de)', 'MTOPIntentClassification (es)', 'MTOPIntentClassification (fr)', 'MTOPIntentClassification (hi)', 'MTOPIntentClassification (th)', 'MassiveIntentClassification (af)', 'MassiveIntentClassification (am)', 'MassiveIntentClassification (ar)', 'MassiveIntentClassification (az)', 'MassiveIntentClassification (bn)', 'MassiveIntentClassification (cy)', 'MassiveIntentClassification (de)', 'MassiveIntentClassification (el)', 'MassiveIntentClassification (es)', 'MassiveIntentClassification (fa)', 'MassiveIntentClassification (fi)', 'MassiveIntentClassification (fr)', 'MassiveIntentClassification (he)', 'MassiveIntentClassification (hi)', 'MassiveIntentClassification (hu)', 'MassiveIntentClassification (hy)', 'MassiveIntentClassification (id)', 'MassiveIntentClassification (is)', 'MassiveIntentClassification (it)', 'MassiveIntentClassification (ja)', 'MassiveIntentClassification (jv)', 'MassiveIntentClassification (ka)', 'MassiveIntentClassification (km)', 'MassiveIntentClassification (kn)', 'MassiveIntentClassification (ko)', 'MassiveIntentClassification (lv)', 'MassiveIntentClassification (ml)', 'MassiveIntentClassification (mn)', 'MassiveIntentClassification (ms)', 'MassiveIntentClassification (my)', 'MassiveIntentClassification (nl)', 'MassiveIntentClassification (pt)', 'MassiveIntentClassification (ro)', 'MassiveIntentClassification (ru)', 'MassiveIntentClassification (sl)', 'MassiveIntentClassification (sq)', 'MassiveIntentClassification (sw)', 'MassiveIntentClassification (ta)', 'MassiveIntentClassification (te)', 'MassiveIntentClassification (th)', 'MassiveIntentClassification (tl)', 'MassiveIntentClassification (tr)', 'MassiveIntentClassification (ur)', 'MassiveIntentClassification (vi)', 'MassiveIntentClassification (zh-TW)', 'MassiveScenarioClassification (af)', 'MassiveScenarioClassification (am)', 'MassiveScenarioClassification (ar)', 'MassiveScenarioClassification (az)', 'MassiveScenarioClassification (bn)', 'MassiveScenarioClassification (cy)', 'MassiveScenarioClassification (de)', 'MassiveScenarioClassification (el)', 'MassiveScenarioClassification (es)', 'MassiveScenarioClassification (fa)', 'MassiveScenarioClassification (fi)', 'MassiveScenarioClassification (fr)', 'MassiveScenarioClassification (he)', 'MassiveScenarioClassification (hi)', 'MassiveScenarioClassification (hu)', 'MassiveScenarioClassification (hy)', 'MassiveScenarioClassification (id)', 'MassiveScenarioClassification (is)', 'MassiveScenarioClassification (it)', 'MassiveScenarioClassification (ja)', 'MassiveScenarioClassification (jv)', 'MassiveScenarioClassification (ka)', 'MassiveScenarioClassification (km)', 'MassiveScenarioClassification (kn)', 'MassiveScenarioClassification (ko)', 'MassiveScenarioClassification (lv)', 'MassiveScenarioClassification (ml)', 'MassiveScenarioClassification (mn)', 'MassiveScenarioClassification (ms)', 'MassiveScenarioClassification (my)', 'MassiveScenarioClassification (nl)', 'MassiveScenarioClassification (pt)', 'MassiveScenarioClassification (ro)', 'MassiveScenarioClassification (ru)', 'MassiveScenarioClassification (sl)', 'MassiveScenarioClassification (sq)', 'MassiveScenarioClassification (sw)', 'MassiveScenarioClassification (ta)', 'MassiveScenarioClassification (te)', 'MassiveScenarioClassification (th)', 'MassiveScenarioClassification (tl)', 'MassiveScenarioClassification (tr)', 'MassiveScenarioClassification (ur)', 'MassiveScenarioClassification (vi)', 'MassiveScenarioClassification (zh-TW)']
|
354 |
+
other-sts:
|
355 |
+
title: Other
|
356 |
+
language_long: "Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Russian, Spanish (Only language combos not included in the other tabs)"
|
357 |
+
has_overall: false
|
358 |
+
acronym: null
|
359 |
+
icon: null
|
360 |
+
special_icons:
|
361 |
+
STS: "👽"
|
362 |
+
credits: null
|
363 |
+
tasks:
|
364 |
+
STS: ["STS17 (ar-ar)", "STS17 (en-ar)", "STS17 (en-de)", "STS17 (en-tr)", "STS17 (es-en)", "STS17 (es-es)", "STS17 (fr-en)", "STS17 (it-en)", "STS17 (ko-ko)", "STS17 (nl-en)", "STS22 (ar)", "STS22 (de)", "STS22 (de-en)", "STS22 (de-fr)", "STS22 (de-pl)", "STS22 (es)", "STS22 (es-en)", "STS22 (es-it)", "STS22 (fr)", "STS22 (fr-pl)", "STS22 (it)", "STS22 (pl)", "STS22 (pl-en)", "STS22 (ru)", "STS22 (tr)", "STS22 (zh-en)", "STSBenchmark"]
|
@@ -0,0 +1,48 @@
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|
1 |
+
import os
|
2 |
+
from yaml import safe_load
|
3 |
+
|
4 |
+
from huggingface_hub import HfApi
|
5 |
+
|
6 |
+
LEADERBOARD_CONFIG_PATH = "config.yaml"
|
7 |
+
with open(LEADERBOARD_CONFIG_PATH, 'r', encoding='utf-8') as f:
|
8 |
+
LEADERBOARD_CONFIG = safe_load(f)
|
9 |
+
MODEL_META_PATH = "model_meta.yaml"
|
10 |
+
with open(MODEL_META_PATH, 'r', encoding='utf-8') as f:
|
11 |
+
MODEL_META = safe_load(f)
|
12 |
+
|
13 |
+
# Try first to get the config from the environment variables, then from the config.yaml file
|
14 |
+
def get_config(name, default):
|
15 |
+
res = None
|
16 |
+
|
17 |
+
if name in os.environ:
|
18 |
+
res = os.environ[name]
|
19 |
+
elif 'config' in LEADERBOARD_CONFIG:
|
20 |
+
res = LEADERBOARD_CONFIG['config'].get(name, None)
|
21 |
+
|
22 |
+
if res is None:
|
23 |
+
return default
|
24 |
+
return res
|
25 |
+
|
26 |
+
def str2bool(v):
|
27 |
+
return str(v).lower() in ("yes", "true", "t", "1")
|
28 |
+
|
29 |
+
# clone / pull the lmeh eval data
|
30 |
+
HF_TOKEN = get_config("HF_TOKEN", None)
|
31 |
+
|
32 |
+
LEADERBOARD_NAME = get_config("LEADERBOARD_NAME", "MTEB Leaderboard")
|
33 |
+
|
34 |
+
REPO_ID = get_config("REPO_ID", "mteb/leaderboard")
|
35 |
+
RESULTS_REPO = get_config("RESULTS_REPO", "mteb/results")
|
36 |
+
|
37 |
+
CACHE_PATH=get_config("HF_HOME", ".")
|
38 |
+
os.environ["HF_HOME"] = CACHE_PATH
|
39 |
+
|
40 |
+
# Check if it is using persistent storage
|
41 |
+
if not os.access(CACHE_PATH, os.W_OK):
|
42 |
+
print(f"No write access to HF_HOME: {CACHE_PATH}. Resetting to current directory.")
|
43 |
+
CACHE_PATH = "."
|
44 |
+
os.environ["HF_HOME"] = CACHE_PATH
|
45 |
+
else:
|
46 |
+
print(f"Write access confirmed for HF_HOME")
|
47 |
+
|
48 |
+
API = HfApi(token=HF_TOKEN)
|
@@ -0,0 +1,1160 @@
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|
|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
1 |
+
model_meta:
|
2 |
+
Baichuan-text-embedding:
|
3 |
+
link: https://platform.baichuan-ai.com/docs/text-Embedding
|
4 |
+
seq_len: 512
|
5 |
+
size: null
|
6 |
+
dim: 1024
|
7 |
+
is_external: true
|
8 |
+
is_proprietary: true
|
9 |
+
is_sentence_transformers_compatible: false
|
10 |
+
Cohere-embed-english-v3.0:
|
11 |
+
link: https://huggingface.co/Cohere/Cohere-embed-english-v3.0
|
12 |
+
seq_len: 512
|
13 |
+
size: null
|
14 |
+
dim: 1024
|
15 |
+
is_external: true
|
16 |
+
is_proprietary: true
|
17 |
+
is_sentence_transformers_compatible: false
|
18 |
+
Cohere-embed-multilingual-light-v3.0:
|
19 |
+
link: https://huggingface.co/Cohere/Cohere-embed-multilingual-light-v3.0
|
20 |
+
seq_len: 512
|
21 |
+
size: null
|
22 |
+
dim: 384
|
23 |
+
is_external: true
|
24 |
+
is_proprietary: true
|
25 |
+
is_sentence_transformers_compatible: false
|
26 |
+
Cohere-embed-multilingual-v3.0:
|
27 |
+
link: https://huggingface.co/Cohere/Cohere-embed-multilingual-v3.0
|
28 |
+
seq_len: 512
|
29 |
+
size: null
|
30 |
+
dim: 1024
|
31 |
+
is_external: true
|
32 |
+
is_proprietary: true
|
33 |
+
is_sentence_transformers_compatible: false
|
34 |
+
DanskBERT:
|
35 |
+
link: https://huggingface.co/vesteinn/DanskBERT
|
36 |
+
seq_len: 514
|
37 |
+
size: 125
|
38 |
+
dim: 768
|
39 |
+
is_external: true
|
40 |
+
is_proprietary: false
|
41 |
+
is_sentence_transformers_compatible: true
|
42 |
+
LASER2:
|
43 |
+
link: https://github.com/facebookresearch/LASER
|
44 |
+
seq_len: N/A
|
45 |
+
size: 43
|
46 |
+
dim: 1024
|
47 |
+
is_external: true
|
48 |
+
is_proprietary: false
|
49 |
+
is_sentence_transformers_compatible: false
|
50 |
+
LLM2Vec-Llama-supervised:
|
51 |
+
link: https://huggingface.co/McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised
|
52 |
+
seq_len: 4096
|
53 |
+
size: 6607
|
54 |
+
dim: 4096
|
55 |
+
is_external: true
|
56 |
+
is_proprietary: false
|
57 |
+
is_sentence_transformers_compatible: false
|
58 |
+
LLM2Vec-Llama-unsupervised:
|
59 |
+
link: https://huggingface.co/McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp
|
60 |
+
seq_len: 4096
|
61 |
+
size: 6607
|
62 |
+
dim: 4096
|
63 |
+
is_external: true
|
64 |
+
is_proprietary: false
|
65 |
+
is_sentence_transformers_compatible: false
|
66 |
+
LLM2Vec-Mistral-supervised:
|
67 |
+
link: https://huggingface.co/McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised
|
68 |
+
seq_len: 32768
|
69 |
+
size: 7111
|
70 |
+
dim: 4096
|
71 |
+
is_external: true
|
72 |
+
is_proprietary: false
|
73 |
+
is_sentence_transformers_compatible: false
|
74 |
+
LLM2Vec-Mistral-unsupervised:
|
75 |
+
link: https://huggingface.co/McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp
|
76 |
+
seq_len: 32768
|
77 |
+
size: 7111
|
78 |
+
dim: 4096
|
79 |
+
is_external: true
|
80 |
+
is_proprietary: false
|
81 |
+
is_sentence_transformers_compatible: false
|
82 |
+
LLM2Vec-Sheared-Llama-supervised:
|
83 |
+
link: https://huggingface.co/McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-supervised
|
84 |
+
seq_len: 4096
|
85 |
+
size: 1280
|
86 |
+
dim: 2048
|
87 |
+
is_external: true
|
88 |
+
is_proprietary: false
|
89 |
+
is_sentence_transformers_compatible: false
|
90 |
+
LLM2Vec-Sheared-Llama-unsupervised:
|
91 |
+
link: https://huggingface.co/McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp
|
92 |
+
seq_len: 4096
|
93 |
+
size: 1280
|
94 |
+
dim: 2048
|
95 |
+
is_external: true
|
96 |
+
is_proprietary: false
|
97 |
+
is_sentence_transformers_compatible: false
|
98 |
+
LaBSE:
|
99 |
+
link: https://huggingface.co/sentence-transformers/LaBSE
|
100 |
+
seq_len: 512
|
101 |
+
size: 471
|
102 |
+
dim: 768
|
103 |
+
is_external: true
|
104 |
+
is_proprietary: false
|
105 |
+
is_sentence_transformers_compatible: true
|
106 |
+
OpenSearch-text-hybrid:
|
107 |
+
link: https://help.aliyun.com/zh/open-search/vector-search-edition/hybrid-retrieval
|
108 |
+
seq_len: 512
|
109 |
+
size: null
|
110 |
+
dim: 1792
|
111 |
+
is_external: true
|
112 |
+
is_proprietary: true
|
113 |
+
is_sentence_transformers_compatible: false
|
114 |
+
all-MiniLM-L12-v2:
|
115 |
+
link: https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2
|
116 |
+
seq_len: 512
|
117 |
+
size: 33
|
118 |
+
dim: 384
|
119 |
+
is_external: true
|
120 |
+
is_proprietary: false
|
121 |
+
is_sentence_transformers_compatible: true
|
122 |
+
all-MiniLM-L6-v2:
|
123 |
+
link: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
|
124 |
+
seq_len: 512
|
125 |
+
size: 23
|
126 |
+
dim: 384
|
127 |
+
is_external: true
|
128 |
+
is_proprietary: false
|
129 |
+
is_sentence_transformers_compatible: true
|
130 |
+
all-mpnet-base-v2:
|
131 |
+
link: https://huggingface.co/sentence-transformers/all-mpnet-base-v2
|
132 |
+
seq_len: 514
|
133 |
+
size: 110
|
134 |
+
dim: 768
|
135 |
+
is_external: true
|
136 |
+
is_proprietary: false
|
137 |
+
is_sentence_transformers_compatible: true
|
138 |
+
allenai-specter:
|
139 |
+
link: https://huggingface.co/sentence-transformers/allenai-specter
|
140 |
+
seq_len: 512
|
141 |
+
size: 110
|
142 |
+
dim: 768
|
143 |
+
is_external: true
|
144 |
+
is_proprietary: false
|
145 |
+
is_sentence_transformers_compatible: true
|
146 |
+
bert-base-10lang-cased:
|
147 |
+
link: https://huggingface.co/Geotrend/bert-base-10lang-cased
|
148 |
+
seq_len: 512
|
149 |
+
size: 138
|
150 |
+
dim: 768
|
151 |
+
is_external: true
|
152 |
+
is_proprietary: false
|
153 |
+
is_sentence_transformers_compatible: true
|
154 |
+
bert-base-15lang-cased:
|
155 |
+
link: https://huggingface.co/Geotrend/bert-base-15lang-cased
|
156 |
+
seq_len: 512
|
157 |
+
size: 138
|
158 |
+
dim: 768
|
159 |
+
is_external: true
|
160 |
+
is_proprietary: false
|
161 |
+
is_sentence_transformers_compatible: true
|
162 |
+
bert-base-25lang-cased:
|
163 |
+
link: https://huggingface.co/Geotrend/bert-base-25lang-cased
|
164 |
+
seq_len: 512
|
165 |
+
size: 138
|
166 |
+
dim: 768
|
167 |
+
is_external: true
|
168 |
+
is_proprietary: false
|
169 |
+
is_sentence_transformers_compatible: true
|
170 |
+
bert-base-multilingual-cased:
|
171 |
+
link: https://huggingface.co/google-bert/bert-base-multilingual-cased
|
172 |
+
seq_len: 512
|
173 |
+
size: 179
|
174 |
+
dim: 768
|
175 |
+
is_external: true
|
176 |
+
is_proprietary: false
|
177 |
+
is_sentence_transformers_compatible: true
|
178 |
+
bert-base-multilingual-uncased:
|
179 |
+
link: https://huggingface.co/google-bert/bert-base-multilingual-uncased
|
180 |
+
seq_len: 512
|
181 |
+
size: 168
|
182 |
+
dim: 768
|
183 |
+
is_external: true
|
184 |
+
is_proprietary: false
|
185 |
+
is_sentence_transformers_compatible: true
|
186 |
+
bert-base-swedish-cased:
|
187 |
+
link: https://huggingface.co/KB/bert-base-swedish-cased
|
188 |
+
seq_len: 512
|
189 |
+
size: 125
|
190 |
+
dim: 768
|
191 |
+
is_external: true
|
192 |
+
is_proprietary: false
|
193 |
+
is_sentence_transformers_compatible: true
|
194 |
+
bert-base-uncased:
|
195 |
+
link: https://huggingface.co/bert-base-uncased
|
196 |
+
seq_len: 512
|
197 |
+
size: 110
|
198 |
+
dim: 768
|
199 |
+
is_external: true
|
200 |
+
is_proprietary: false
|
201 |
+
is_sentence_transformers_compatible: true
|
202 |
+
bge-base-zh-v1.5:
|
203 |
+
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seq_len: 4000
|
997 |
+
size: null
|
998 |
+
dim: 1024
|
999 |
+
is_external: true
|
1000 |
+
is_proprietary: true
|
1001 |
+
is_sentence_transformers_compatible: false
|
1002 |
+
voyage-lite-02-instruct:
|
1003 |
+
link: https://docs.voyageai.com/embeddings/
|
1004 |
+
seq_len: 4000
|
1005 |
+
size: 1220
|
1006 |
+
dim: 1024
|
1007 |
+
is_external: true
|
1008 |
+
is_proprietary: true
|
1009 |
+
is_sentence_transformers_compatible: false
|
1010 |
+
xlm-roberta-base:
|
1011 |
+
link: https://huggingface.co/xlm-roberta-base
|
1012 |
+
seq_len: 514
|
1013 |
+
size: 279
|
1014 |
+
dim: 768
|
1015 |
+
is_external: true
|
1016 |
+
is_proprietary: false
|
1017 |
+
is_sentence_transformers_compatible: true
|
1018 |
+
xlm-roberta-large:
|
1019 |
+
link: https://huggingface.co/xlm-roberta-large
|
1020 |
+
seq_len: 514
|
1021 |
+
size: 560
|
1022 |
+
dim: 1024
|
1023 |
+
is_external: true
|
1024 |
+
is_proprietary: false
|
1025 |
+
is_sentence_transformers_compatible: true
|
1026 |
+
models_to_skip:
|
1027 |
+
- michaelfeil/ct2fast-e5-large-v2
|
1028 |
+
- McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse
|
1029 |
+
- newsrx/instructor-xl
|
1030 |
+
- sionic-ai/sionic-ai-v1
|
1031 |
+
- lsf1000/bge-evaluation
|
1032 |
+
- Intel/bge-small-en-v1.5-sst2
|
1033 |
+
- newsrx/instructor-xl-newsrx
|
1034 |
+
- McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-unsup-simcse
|
1035 |
+
- davidpeer/gte-small
|
1036 |
+
- goldenrooster/multilingual-e5-large
|
1037 |
+
- kozistr/fused-large-en
|
1038 |
+
- mixamrepijey/instructor-small
|
1039 |
+
- McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised
|
1040 |
+
- DecisionOptimizationSystem/DeepFeatEmbeddingLargeContext
|
1041 |
+
- Intel/bge-base-en-v1.5-sst2-int8-dynamic
|
1042 |
+
- morgendigital/multilingual-e5-large-quantized
|
1043 |
+
- BAAI/bge-small-en
|
1044 |
+
- ggrn/e5-small-v2
|
1045 |
+
- vectoriseai/gte-small
|
1046 |
+
- giulio98/placeholder
|
1047 |
+
- odunola/UAE-Large-VI
|
1048 |
+
- vectoriseai/e5-large-v2
|
1049 |
+
- gruber/e5-small-v2-ggml
|
1050 |
+
- Severian/nomic
|
1051 |
+
- arcdev/e5-mistral-7b-instruct
|
1052 |
+
- mlx-community/multilingual-e5-base-mlx
|
1053 |
+
- michaelfeil/ct2fast-bge-base-en-v1.5
|
1054 |
+
- Intel/bge-small-en-v1.5-sst2-int8-static
|
1055 |
+
- jncraton/stella-base-en-v2-ct2-int8
|
1056 |
+
- vectoriseai/multilingual-e5-large
|
1057 |
+
- rlsChapters/Chapters-SFR-Embedding-Mistral
|
1058 |
+
- arcdev/SFR-Embedding-Mistral
|
1059 |
+
- McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised
|
1060 |
+
- vectoriseai/gte-base
|
1061 |
+
- mixamrepijey/instructor-models
|
1062 |
+
- GovCompete/e5-large-v2
|
1063 |
+
- ef-zulla/e5-multi-sml-torch
|
1064 |
+
- khoa-klaytn/bge-small-en-v1.5-angle
|
1065 |
+
- krilecy/e5-mistral-7b-instruct
|
1066 |
+
- vectoriseai/bge-base-en-v1.5
|
1067 |
+
- vectoriseai/instructor-base
|
1068 |
+
- jingyeom/korean_embedding_model
|
1069 |
+
- rizki/bgr-tf
|
1070 |
+
- barisaydin/bge-base-en
|
1071 |
+
- jamesgpt1/zzz
|
1072 |
+
- Malmuk1/e5-large-v2_Sharded
|
1073 |
+
- vectoriseai/ember-v1
|
1074 |
+
- Consensus/instructor-base
|
1075 |
+
- barisaydin/bge-small-en
|
1076 |
+
- barisaydin/gte-base
|
1077 |
+
- woody72/multilingual-e5-base
|
1078 |
+
- Einas/einas_ashkar
|
1079 |
+
- michaelfeil/ct2fast-bge-large-en-v1.5
|
1080 |
+
- vectoriseai/bge-small-en-v1.5
|
1081 |
+
- iampanda/Test
|
1082 |
+
- cherubhao/yogamodel
|
1083 |
+
- ieasybooks/multilingual-e5-large-onnx
|
1084 |
+
- jncraton/e5-small-v2-ct2-int8
|
1085 |
+
- radames/e5-large
|
1086 |
+
- khoa-klaytn/bge-base-en-v1.5-angle
|
1087 |
+
- Intel/bge-base-en-v1.5-sst2-int8-static
|
1088 |
+
- vectoriseai/e5-large
|
1089 |
+
- TitanML/jina-v2-base-en-embed
|
1090 |
+
- Koat/gte-tiny
|
1091 |
+
- binqiangliu/EmbeddingModlebgelargeENv1.5
|
1092 |
+
- beademiguelperez/sentence-transformers-multilingual-e5-small
|
1093 |
+
- sionic-ai/sionic-ai-v2
|
1094 |
+
- jamesdborin/jina-v2-base-en-embed
|
1095 |
+
- maiyad/multilingual-e5-small
|
1096 |
+
- dmlls/all-mpnet-base-v2
|
1097 |
+
- odunola/e5-base-v2
|
1098 |
+
- vectoriseai/bge-large-en-v1.5
|
1099 |
+
- vectoriseai/bge-small-en
|
1100 |
+
- karrar-alwaili/UAE-Large-V1
|
1101 |
+
- t12e/instructor-base
|
1102 |
+
- Frazic/udever-bloom-3b-sentence
|
1103 |
+
- Geolumina/instructor-xl
|
1104 |
+
- hsikchi/dump
|
1105 |
+
- recipe/embeddings
|
1106 |
+
- michaelfeil/ct2fast-bge-small-en-v1.5
|
1107 |
+
- ildodeltaRule/multilingual-e5-large
|
1108 |
+
- shubham-bgi/UAE-Large
|
1109 |
+
- BAAI/bge-large-en
|
1110 |
+
- michaelfeil/ct2fast-e5-small-v2
|
1111 |
+
- cgldo/semanticClone
|
1112 |
+
- barisaydin/gte-small
|
1113 |
+
- aident-ai/bge-base-en-onnx
|
1114 |
+
- jamesgpt1/english-large-v1
|
1115 |
+
- michaelfeil/ct2fast-e5-small
|
1116 |
+
- baseplate/instructor-large-1
|
1117 |
+
- newsrx/instructor-large
|
1118 |
+
- Narsil/bge-base-en
|
1119 |
+
- michaelfeil/ct2fast-e5-large
|
1120 |
+
- mlx-community/multilingual-e5-small-mlx
|
1121 |
+
- lightbird-ai/nomic
|
1122 |
+
- MaziyarPanahi/GritLM-8x7B-GGUF
|
1123 |
+
- newsrx/instructor-large-newsrx
|
1124 |
+
- dhairya0907/thenlper-get-large
|
1125 |
+
- barisaydin/bge-large-en
|
1126 |
+
- jncraton/bge-small-en-ct2-int8
|
1127 |
+
- retrainai/instructor-xl
|
1128 |
+
- BAAI/bge-base-en
|
1129 |
+
- gentlebowl/instructor-large-safetensors
|
1130 |
+
- d0rj/e5-large-en-ru
|
1131 |
+
- atian-chapters/Chapters-SFR-Embedding-Mistral
|
1132 |
+
- Intel/bge-base-en-v1.5-sts-int8-static
|
1133 |
+
- Intel/bge-base-en-v1.5-sts-int8-dynamic
|
1134 |
+
- jncraton/GIST-small-Embedding-v0-ct2-int8
|
1135 |
+
- jncraton/gte-tiny-ct2-int8
|
1136 |
+
- d0rj/e5-small-en-ru
|
1137 |
+
- vectoriseai/e5-small-v2
|
1138 |
+
- SmartComponents/bge-micro-v2
|
1139 |
+
- michaelfeil/ct2fast-gte-base
|
1140 |
+
- vectoriseai/e5-base-v2
|
1141 |
+
- Intel/bge-base-en-v1.5-sst2
|
1142 |
+
- McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-supervised
|
1143 |
+
- Research2NLP/electrical_stella
|
1144 |
+
- weakit-v/bge-base-en-v1.5-onnx
|
1145 |
+
- GovCompete/instructor-xl
|
1146 |
+
- barisaydin/text2vec-base-multilingual
|
1147 |
+
- Intel/bge-small-en-v1.5-sst2-int8-dynamic
|
1148 |
+
- jncraton/gte-small-ct2-int8
|
1149 |
+
- d0rj/e5-base-en-ru
|
1150 |
+
- barisaydin/gte-large
|
1151 |
+
- fresha/e5-large-v2-endpoint
|
1152 |
+
- vectoriseai/instructor-large
|
1153 |
+
- Severian/embed
|
1154 |
+
- vectoriseai/e5-base
|
1155 |
+
- mlx-community/multilingual-e5-large-mlx
|
1156 |
+
- vectoriseai/gte-large
|
1157 |
+
- anttip/ct2fast-e5-small-v2-hfie
|
1158 |
+
- michaelfeil/ct2fast-gte-large
|
1159 |
+
- gizmo-ai/Cohere-embed-multilingual-v3.0
|
1160 |
+
- McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-unsup-simcse
|