---
tags:
- mteb
model-index:
- name: student
  results:
  - task:
      type: STS
    dataset:
      type: C-MTEB/AFQMC
      name: MTEB AFQMC
      config: default
      split: validation
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 42.01013972878128
    - type: cos_sim_spearman
      value: 43.4493974759166
    - type: euclidean_pearson
      value: 41.9332741602486
    - type: euclidean_spearman
      value: 43.4565546063627
    - type: manhattan_pearson
      value: 41.9297043571561
    - type: manhattan_spearman
      value: 43.44509515848548
  - task:
      type: STS
    dataset:
      type: C-MTEB/ATEC
      name: MTEB ATEC
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 47.48357848831134
    - type: cos_sim_spearman
      value: 48.0096502737997
  - task:
      type: STS
    dataset:
      type: mteb/biosses-sts
      name: MTEB BIOSSES
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 70.06631340065852
    - type: cos_sim_spearman
      value: 70.56425845690775
  - task:
      type: STS
    dataset:
      type: C-MTEB/BQ
      name: MTEB BQ
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 63.30619967351764
    - type: cos_sim_spearman
      value: 65.57791727146774
    - type: euclidean_pearson
      value: 64.41653053459552
    - type: euclidean_spearman
      value: 65.60244311139472
    - type: manhattan_pearson
      value: 64.37518298990945
    - type: manhattan_spearman
      value: 65.56983205786409
  - task:
      type: BitextMining
    dataset:
      type: mteb/bucc-bitext-mining
      name: MTEB BUCC (zh-en)
      config: zh-en
      split: test
      revision: d51519689f32196a32af33b075a01d0e7c51e252
    metrics:
    - type: accuracy
      value: 98.42022116903634
    - type: f1
      value: 98.38511497279269
    - type: precision
      value: 98.36756187467088
    - type: recall
      value: 98.42022116903634
  - task:
      type: STS
    dataset:
      type: C-MTEB/LCQMC
      name: MTEB LCQMC
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 71.3095132213625
    - type: cos_sim_spearman
      value: 75.55615792829865
    - type: euclidean_pearson
      value: 74.37147909656647
    - type: euclidean_spearman
      value: 75.54784459711308
    - type: manhattan_pearson
      value: 74.29759624788565
    - type: manhattan_spearman
      value: 75.49037321257157
  - task:
      type: STS
    dataset:
      type: C-MTEB/PAWSX
      name: MTEB PAWSX
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 42.821882144591406
    - type: cos_sim_spearman
      value: 47.616725737501724
    - type: euclidean_pearson
      value: 46.991556480777675
    - type: euclidean_spearman
      value: 47.624128831089685
    - type: manhattan_pearson
      value: 46.83451589707148
    - type: manhattan_spearman
      value: 47.47345373932411
  - task:
      type: STS
    dataset:
      type: C-MTEB/QBQTC
      name: MTEB QBQTC
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 39.48274306266568
    - type: cos_sim_spearman
      value: 40.43254828668596
    - type: euclidean_pearson
      value: 39.121198397707374
    - type: euclidean_spearman
      value: 40.47848829374869
    - type: manhattan_pearson
      value: 39.07044184765326
    - type: manhattan_spearman
      value: 40.41344728276686
  - task:
      type: STS
    dataset:
      type: mteb/sickr-sts
      name: MTEB SICK-R
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 81.60488630930521
    - type: cos_sim_spearman
      value: 79.04311658059933
    - type: euclidean_pearson
      value: 78.95158745413384
    - type: euclidean_spearman
      value: 78.99206332696008
    - type: manhattan_pearson
      value: 78.93956396383128
    - type: manhattan_spearman
      value: 78.94138617747835
  - task:
      type: STS
    dataset:
      type: mteb/sts12-sts
      name: MTEB STS12
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 85.50516203958485
    - type: cos_sim_spearman
      value: 78.39314964894021
    - type: euclidean_pearson
      value: 83.03876157406377
    - type: euclidean_spearman
      value: 78.43128279495177
    - type: manhattan_pearson
      value: 83.00734833664097
    - type: manhattan_spearman
      value: 78.33755694741544
  - task:
      type: STS
    dataset:
      type: mteb/sts13-sts
      name: MTEB STS13
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 82.52249245791886
    - type: cos_sim_spearman
      value: 83.71503684399218
    - type: euclidean_pearson
      value: 82.83033355582003
    - type: euclidean_spearman
      value: 83.6956570069731
    - type: manhattan_pearson
      value: 82.74415910929217
    - type: manhattan_spearman
      value: 83.58167243171766
  - task:
      type: STS
    dataset:
      type: mteb/sts14-sts
      name: MTEB STS14
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 81.00915974657362
    - type: cos_sim_spearman
      value: 79.19276300509559
    - type: euclidean_pearson
      value: 80.17657754340593
    - type: euclidean_spearman
      value: 79.19425018312683
    - type: manhattan_pearson
      value: 80.04321829436775
    - type: manhattan_spearman
      value: 79.0458687679498
  - task:
      type: STS
    dataset:
      type: mteb/sts15-sts
      name: MTEB STS15
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 84.99452083625762
    - type: cos_sim_spearman
      value: 85.57952966879047
    - type: euclidean_pearson
      value: 85.14932626009531
    - type: euclidean_spearman
      value: 85.59697259700918
    - type: manhattan_pearson
      value: 85.11214415799934
    - type: manhattan_spearman
      value: 85.54871088485925
  - task:
      type: STS
    dataset:
      type: mteb/sts16-sts
      name: MTEB STS16
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 80.33170312674788
    - type: cos_sim_spearman
      value: 82.3316942254394
    - type: euclidean_pearson
      value: 82.00948134099386
    - type: euclidean_spearman
      value: 82.32475375375705
    - type: manhattan_pearson
      value: 81.94953036676401
    - type: manhattan_spearman
      value: 82.26329177825353
  - task:
      type: STS
    dataset:
      type: mteb/sts17-crosslingual-sts
      name: MTEB STS17 (en-en)
      config: en-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 87.60426458021554
    - type: cos_sim_spearman
      value: 87.89776827373123
    - type: euclidean_pearson
      value: 88.19401282603557
    - type: euclidean_spearman
      value: 87.90080500648473
    - type: manhattan_pearson
      value: 88.39099772653003
    - type: manhattan_spearman
      value: 88.03019288557621
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (en)
      config: en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 60.38925903960008
    - type: cos_sim_spearman
      value: 63.91952542589123
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (zh)
      config: zh
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 61.51076949065575
    - type: cos_sim_spearman
      value: 67.24427398434739
  - task:
      type: STS
    dataset:
      type: mteb/sts22-crosslingual-sts
      name: MTEB STS22 (zh-en)
      config: zh-en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 70.08946142653247
    - type: cos_sim_spearman
      value: 70.01280058113731
  - task:
      type: STS
    dataset:
      type: C-MTEB/STSB
      name: MTEB STSB
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 75.52896222293855
    - type: cos_sim_spearman
      value: 75.38140772041567
  - task:
      type: STS
    dataset:
      type: mteb/stsbenchmark-sts
      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 85.09649790270096
    - type: cos_sim_spearman
      value: 85.99053080606336
    - type: euclidean_pearson
      value: 85.9554143396231
    - type: euclidean_spearman
      value: 85.9826211701156
    - type: manhattan_pearson
      value: 85.91951912635923
    - type: manhattan_spearman
      value: 85.90751385480418
  - task:
      type: BitextMining
    dataset:
      type: mteb/tatoeba-bitext-mining
      name: MTEB Tatoeba (cmn-eng)
      config: cmn-eng
      split: test
      revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
    metrics:
    - type: accuracy
      value: 96.3
    - type: f1
      value: 95.15
    - type: precision
      value: 94.58333333333333
    - type: recall
      value: 96.3
---


Use Chinese and English STS and NLI corpora to conduct contrastive learning finetuning on xlmr



## Using HuggingFace Transformers

```
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('zhou-xl/bi-cse')
model = AutoModel.from_pretrained('zhou-xl/bi-cse')
model.eval()

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)
    # Perform pooling. In this case, cls pooling.
    sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```