zeroshot/gte-large-sparse
235
1---2tags:3- sparse sparsity quantized onnx embeddings int84- mteb5model-index:6- name: gte-large-sparse7 results:8 - task:9 type: STS10 dataset:11 type: mteb/biosses-sts12 name: MTEB BIOSSES13 config: default14 split: test15 revision: d3fb88f8f02e40887cd149695127462bbcf29b4a16 metrics:17 - type: cos_sim_pearson18 value: 88.6425341092821419 - type: cos_sim_spearman20 value: 85.8338834941065221 - type: euclidean_pearson22 value: 86.8612615931873523 - type: euclidean_spearman24 value: 85.6158062359116325 - type: manhattan_pearson26 value: 86.690113288338327 - type: manhattan_spearman28 value: 85.6025529218776929 - task:30 type: STS31 dataset:32 type: mteb/sickr-sts33 name: MTEB SICK-R34 config: default35 split: test36 revision: a6ea5a8cab320b040a23452cc28066d9beae2cee37 metrics:38 - type: cos_sim_pearson39 value: 85.2331464059160740 - type: cos_sim_spearman41 value: 79.0007854510433842 - type: euclidean_pearson43 value: 83.4800925450071444 - type: euclidean_spearman45 value: 78.9541300138993946 - type: manhattan_pearson47 value: 83.4694556602594148 - type: manhattan_spearman49 value: 78.924170720813550 - task:51 type: STS52 dataset:53 type: mteb/sts12-sts54 name: MTEB STS1255 config: default56 split: test57 revision: a0d554a64d88156834ff5ae9920b964011b1638458 metrics:59 - type: cos_sim_pearson60 value: 81.7752666604380461 - type: cos_sim_spearman62 value: 73.484906328586763 - type: euclidean_pearson64 value: 78.0447793274052465 - type: euclidean_spearman66 value: 73.0139420577174367 - type: manhattan_pearson68 value: 78.0883668450329469 - type: manhattan_spearman70 value: 73.0507471109814971 - task:72 type: STS73 dataset:74 type: mteb/sts13-sts75 name: MTEB STS1376 config: default77 split: test78 revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca79 metrics:80 - type: cos_sim_pearson81 value: 84.5783921566135282 - type: cos_sim_spearman83 value: 86.1385476734515384 - type: euclidean_pearson85 value: 85.1271260994644986 - type: euclidean_spearman87 value: 85.5249799478902688 - type: manhattan_pearson89 value: 85.0683314161117390 - type: manhattan_spearman91 value: 85.4500306863646692 - task:93 type: STS94 dataset:95 type: mteb/sts14-sts96 name: MTEB STS1497 config: default98 split: test99 revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375100 metrics:101 - type: cos_sim_pearson102 value: 83.30485126978374103 - type: cos_sim_spearman104 value: 80.36497172462357105 - type: euclidean_pearson106 value: 82.91977909424605107 - type: euclidean_spearman108 value: 80.16995106297438109 - type: manhattan_pearson110 value: 82.88200991402184111 - type: manhattan_spearman112 value: 80.14259757215227113 - task:114 type: STS115 dataset:116 type: mteb/sts15-sts117 name: MTEB STS15118 config: default119 split: test120 revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3121 metrics:122 - type: cos_sim_pearson123 value: 86.99883111314007124 - type: cos_sim_spearman125 value: 88.531352572377126 - type: euclidean_pearson127 value: 87.96834578059067128 - type: euclidean_spearman129 value: 88.44800718542935130 - type: manhattan_pearson131 value: 87.94889391725033132 - type: manhattan_spearman133 value: 88.45467695837115134 - task:135 type: STS136 dataset:137 type: mteb/sts16-sts138 name: MTEB STS16139 config: default140 split: test141 revision: 4d8694f8f0e0100860b497b999b3dbed754a0513142 metrics:143 - type: cos_sim_pearson144 value: 82.4636984892402145 - type: cos_sim_spearman146 value: 84.0808920789148147 - type: euclidean_pearson148 value: 83.70613486028309149 - type: euclidean_spearman150 value: 84.35941626905009151 - type: manhattan_pearson152 value: 83.70259457073782153 - type: manhattan_spearman154 value: 84.35496521501604155 - task:156 type: STS157 dataset:158 type: mteb/sts17-crosslingual-sts159 name: MTEB STS17 (en-en)160 config: en-en161 split: test162 revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d163 metrics:164 - type: cos_sim_pearson165 value: 88.76172944971023166 - type: cos_sim_spearman167 value: 89.4190945039165168 - type: euclidean_pearson169 value: 89.47263005347381170 - type: euclidean_spearman171 value: 89.49228360724095172 - type: manhattan_pearson173 value: 89.49959868816694174 - type: manhattan_spearman175 value: 89.5314536157954176 - task:177 type: STS178 dataset:179 type: mteb/sts22-crosslingual-sts180 name: MTEB STS22 (en)181 config: en182 split: test183 revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80184 metrics:185 - type: cos_sim_pearson186 value: 64.57158223787549187 - type: cos_sim_spearman188 value: 66.75053533168037189 - type: euclidean_pearson190 value: 66.45526604831747191 - type: euclidean_spearman192 value: 66.14567667353113193 - type: manhattan_pearson194 value: 66.47352000151176195 - type: manhattan_spearman196 value: 66.21099856852885197 - task:198 type: STS199 dataset:200 type: mteb/stsbenchmark-sts201 name: MTEB STSBenchmark202 config: default203 split: test204 revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831205 metrics:206 - type: cos_sim_pearson207 value: 85.055653571006208 - type: cos_sim_spearman209 value: 85.45387832634702210 - type: euclidean_pearson211 value: 86.31667154906651212 - type: euclidean_spearman213 value: 85.66079590537946214 - type: manhattan_pearson215 value: 86.2806853257308216 - type: manhattan_spearman217 value: 85.63700636713952218 - task:219 type: PairClassification220 dataset:221 type: mteb/sprintduplicatequestions-pairclassification222 name: MTEB SprintDuplicateQuestions223 config: default224 split: test225 revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46226 metrics:227 - type: cos_sim_accuracy228 value: 99.78811881188119229 - type: cos_sim_ap230 value: 94.67027715905307231 - type: cos_sim_f1232 value: 89.33074684772066233 - type: cos_sim_precision234 value: 86.7231638418079235 - type: cos_sim_recall236 value: 92.10000000000001237 - type: dot_accuracy238 value: 99.47128712871287239 - type: dot_ap240 value: 78.41478815918727241 - type: dot_f1242 value: 73.30049261083744243 - type: dot_precision244 value: 72.23300970873787245 - type: dot_recall246 value: 74.4247 - type: euclidean_accuracy248 value: 99.78415841584159249 - type: euclidean_ap250 value: 94.60075930867181251 - type: euclidean_f1252 value: 89.12175648702593253 - type: euclidean_precision254 value: 88.94422310756973255 - type: euclidean_recall256 value: 89.3257 - type: manhattan_accuracy258 value: 99.78415841584159259 - type: manhattan_ap260 value: 94.62867439278095261 - type: manhattan_f1262 value: 89.2337536372454263 - type: manhattan_precision264 value: 86.62900188323917265 - type: manhattan_recall266 value: 92.0267 - type: max_accuracy268 value: 99.78811881188119269 - type: max_ap270 value: 94.67027715905307271 - type: max_f1272 value: 89.33074684772066273 - task:274 type: PairClassification275 dataset:276 type: mteb/twittersemeval2015-pairclassification277 name: MTEB TwitterSemEval2015278 config: default279 split: test280 revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1281 metrics:282 - type: cos_sim_accuracy283 value: 85.09864695714371284 - type: cos_sim_ap285 value: 70.33704198164713286 - type: cos_sim_f1287 value: 66.22893954410307288 - type: cos_sim_precision289 value: 62.42410088743577290 - type: cos_sim_recall291 value: 70.52770448548813292 - type: dot_accuracy293 value: 79.11426357513263294 - type: dot_ap295 value: 49.15484584572233296 - type: dot_f1297 value: 51.12580243364951298 - type: dot_precision299 value: 40.13840830449827300 - type: dot_recall301 value: 70.3957783641161302 - type: euclidean_accuracy303 value: 85.15825236931514304 - type: euclidean_ap305 value: 70.51017350854076306 - type: euclidean_f1307 value: 66.45416294785159308 - type: euclidean_precision309 value: 64.29805082654823310 - type: euclidean_recall311 value: 68.7598944591029312 - type: manhattan_accuracy313 value: 85.1403707456637314 - type: manhattan_ap315 value: 70.47587863399994316 - type: manhattan_f1317 value: 66.4576802507837318 - type: manhattan_precision319 value: 63.32138590203107320 - type: manhattan_recall321 value: 69.92084432717678322 - type: max_accuracy323 value: 85.15825236931514324 - type: max_ap325 value: 70.51017350854076326 - type: max_f1327 value: 66.4576802507837328 - task:329 type: PairClassification330 dataset:331 type: mteb/twitterurlcorpus-pairclassification332 name: MTEB TwitterURLCorpus333 config: default334 split: test335 revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf336 metrics:337 - type: cos_sim_accuracy338 value: 88.8539604921023339 - type: cos_sim_ap340 value: 85.71869912577101341 - type: cos_sim_f1342 value: 78.00535626720983343 - type: cos_sim_precision344 value: 76.46232344893885345 - type: cos_sim_recall346 value: 79.61194949183862347 - type: dot_accuracy348 value: 84.57717235223348349 - type: dot_ap350 value: 74.89496650237145351 - type: dot_f1352 value: 69.05327823892932353 - type: dot_precision354 value: 65.75666829166377355 - type: dot_recall356 value: 72.69787496150293357 - type: euclidean_accuracy358 value: 88.89471028835332359 - type: euclidean_ap360 value: 85.75169460500409361 - type: euclidean_f1362 value: 78.17055393586006363 - type: euclidean_precision364 value: 74.21118184334348365 - type: euclidean_recall366 value: 82.57622420696026367 - type: manhattan_accuracy368 value: 88.92187681918733369 - type: manhattan_ap370 value: 85.7496679471825371 - type: manhattan_f1372 value: 78.11088295687884373 - type: manhattan_precision374 value: 75.82083061535117375 - type: manhattan_recall376 value: 80.5435786880197377 - type: max_accuracy378 value: 88.92187681918733379 - type: max_ap380 value: 85.75169460500409381 - type: max_f1382 value: 78.17055393586006383license: mit384language:385- en386---387 388# gte-large-sparse389 390This is the sparse ONNX variant of the [gte-large](https://huggingface.co/thenlper/gte-large) embeddings model created with [DeepSparse Optimum](https://github.com/neuralmagic/optimum-deepsparse) for ONNX export/inference and Neural Magic's [Sparsify](https://github.com/neuralmagic/sparsify) for one-shot quantization (INT8) and unstructured pruning 50%.391 392Current list of sparse and quantized gte ONNX models:393 394| Links | Sparsification Method |395| --------------------------------------------------------------------------------------------------- | ---------------------- |396| [zeroshot/gte-large-sparse](https://huggingface.co/zeroshot/gte-large-sparse) | Quantization (INT8) & 50% Pruning |397| [zeroshot/gte-large-quant](https://huggingface.co/zeroshot/gte-large-quant) | Quantization (INT8) |398| [zeroshot/gte-base-sparse](https://huggingface.co/zeroshot/gte-base-sparse) | Quantization (INT8) & 50% Pruning |399| [zeroshot/gte-base-quant](https://huggingface.co/zeroshot/gte-base-quant) | Quantization (INT8) |400| [zeroshot/gte-small-sparse](https://huggingface.co/zeroshot/gte-small-sparse) | Quantization (INT8) & 50% Pruning |401| [zeroshot/gte-small-quant](https://huggingface.co/zeroshot/gte-small-quant) | Quantization (INT8) |402 403```bash404pip install -U deepsparse-nightly[sentence_transformers]405```406 407```python408from deepsparse.sentence_transformers import SentenceTransformer409model = SentenceTransformer('zeroshot/gte-large-sparse', export=False)410 411# Our sentences we like to encode412sentences = ['This framework generates embeddings for each input sentence',413 'Sentences are passed as a list of string.',414 'The quick brown fox jumps over the lazy dog.']415 416# Sentences are encoded by calling model.encode()417embeddings = model.encode(sentences)418 419# Print the embeddings420for sentence, embedding in zip(sentences, embeddings):421 print("Sentence:", sentence)422 print("Embedding:", embedding.shape)423 print("")424```425 426For further details regarding DeepSparse & Sentence Transformers integration, refer to the [DeepSparse README](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/sentence_transformers).427 428For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ).429 430