---
license: apache-2.0
tags:
- mteb
model-index:
- name: kim_base_zh_v0
  results:
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (zh)
      config: zh
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 46.66600000000001
    - type: f1
      value: 43.88121213919628
  - task:
      type: Clustering
    dataset:
      type: C-MTEB/CLSClusteringP2P
      name: MTEB CLSClusteringP2P
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 33.55469933811146
  - task:
      type: Clustering
    dataset:
      type: C-MTEB/CLSClusteringS2S
      name: MTEB CLSClusteringS2S
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 36.17977796122646
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/CMedQAv1-reranking
      name: MTEB CMedQAv1
      config: default
      split: test
      revision: None
    metrics:
    - type: map
      value: 83.84687250720238
    - type: mrr
      value: 86.34579365079364
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/CMedQAv2-reranking
      name: MTEB CMedQAv2
      config: default
      split: test
      revision: None
    metrics:
    - type: map
      value: 84.7457752094449
    - type: mrr
      value: 87.41591269841268
  - task:
      type: PairClassification
    dataset:
      type: C-MTEB/CMNLI
      name: MTEB Cmnli
      config: default
      split: validation
      revision: None
    metrics:
    - type: cos_sim_accuracy
      value: 70.99218280216476
    - type: cos_sim_ap
      value: 79.5838273070596
    - type: cos_sim_f1
      value: 73.01215092730762
    - type: cos_sim_precision
      value: 67.09108716944172
    - type: cos_sim_recall
      value: 80.07949497311199
    - type: dot_accuracy
      value: 70.99218280216476
    - type: dot_ap
      value: 79.58744690895374
    - type: dot_f1
      value: 73.01215092730762
    - type: dot_precision
      value: 67.09108716944172
    - type: dot_recall
      value: 80.07949497311199
    - type: euclidean_accuracy
      value: 70.99218280216476
    - type: euclidean_ap
      value: 79.5838273070596
    - type: euclidean_f1
      value: 73.01215092730762
    - type: euclidean_precision
      value: 67.09108716944172
    - type: euclidean_recall
      value: 80.07949497311199
    - type: manhattan_accuracy
      value: 70.88394467829224
    - type: manhattan_ap
      value: 79.42301231718942
    - type: manhattan_f1
      value: 72.72536687631029
    - type: manhattan_precision
      value: 65.91297738932168
    - type: manhattan_recall
      value: 81.10825344867898
    - type: max_accuracy
      value: 70.99218280216476
    - type: max_ap
      value: 79.58744690895374
    - type: max_f1
      value: 73.01215092730762
  - task:
      type: Classification
    dataset:
      type: C-MTEB/IFlyTek-classification
      name: MTEB IFlyTek
      config: default
      split: validation
      revision: None
    metrics:
    - type: accuracy
      value: 47.34128510965756
    - type: f1
      value: 35.49963469301016
  - task:
      type: Classification
    dataset:
      type: C-MTEB/JDReview-classification
      name: MTEB JDReview
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 85.66604127579738
    - type: ap
      value: 53.038152290755555
    - type: f1
      value: 80.14685686902159
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/Mmarco-reranking
      name: MTEB MMarcoReranking
      config: default
      split: dev
      revision: None
    metrics:
    - type: map
      value: 20.56449688140155
    - type: mrr
      value: 19.60753968253968
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_intent
      name: MTEB MassiveIntentClassification (zh-CN)
      config: zh-CN
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 72.38399462004035
    - type: f1
      value: 70.33023134666634
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_massive_scenario
      name: MTEB MassiveScenarioClassification (zh-CN)
      config: zh-CN
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 74.87222595830531
    - type: f1
      value: 74.25722751562503
  - task:
      type: Classification
    dataset:
      type: C-MTEB/MultilingualSentiment-classification
      name: MTEB MultilingualSentiment
      config: default
      split: validation
      revision: None
    metrics:
    - type: accuracy
      value: 76.27000000000001
    - type: f1
      value: 75.9660773461064
  - task:
      type: PairClassification
    dataset:
      type: C-MTEB/OCNLI
      name: MTEB Ocnli
      config: default
      split: validation
      revision: None
    metrics:
    - type: cos_sim_accuracy
      value: 67.35246345425013
    - type: cos_sim_ap
      value: 69.69618171375657
    - type: cos_sim_f1
      value: 71.70665459483928
    - type: cos_sim_precision
      value: 62.75752773375595
    - type: cos_sim_recall
      value: 83.6325237592397
    - type: dot_accuracy
      value: 67.35246345425013
    - type: dot_ap
      value: 69.69618171375657
    - type: dot_f1
      value: 71.70665459483928
    - type: dot_precision
      value: 62.75752773375595
    - type: dot_recall
      value: 83.6325237592397
    - type: euclidean_accuracy
      value: 67.35246345425013
    - type: euclidean_ap
      value: 69.69618171375657
    - type: euclidean_f1
      value: 71.70665459483928
    - type: euclidean_precision
      value: 62.75752773375595
    - type: euclidean_recall
      value: 83.6325237592397
    - type: manhattan_accuracy
      value: 66.81104493773688
    - type: manhattan_ap
      value: 69.33781930832232
    - type: manhattan_f1
      value: 71.6342082980525
    - type: manhattan_precision
      value: 59.78798586572438
    - type: manhattan_recall
      value: 89.33474128827878
    - type: max_accuracy
      value: 67.35246345425013
    - type: max_ap
      value: 69.69618171375657
    - type: max_f1
      value: 71.70665459483928
  - task:
      type: Classification
    dataset:
      type: C-MTEB/OnlineShopping-classification
      name: MTEB OnlineShopping
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 93.05
    - type: ap
      value: 91.26069801777923
    - type: f1
      value: 93.04149818231389
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/T2Reranking
      name: MTEB T2Reranking
      config: default
      split: dev
      revision: None
    metrics:
    - type: map
      value: 65.74883739850293
    - type: mrr
      value: 75.47326869136282
  - task:
      type: Classification
    dataset:
      type: C-MTEB/TNews-classification
      name: MTEB TNews
      config: default
      split: validation
      revision: None
    metrics:
    - type: accuracy
      value: 53.269999999999996
    - type: f1
      value: 51.410630382886445
  - task:
      type: Clustering
    dataset:
      type: C-MTEB/ThuNewsClusteringP2P
      name: MTEB ThuNewsClusteringP2P
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 63.344532225921434
  - task:
      type: Clustering
    dataset:
      type: C-MTEB/ThuNewsClusteringS2S
      name: MTEB ThuNewsClusteringS2S
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 60.33437882010517
  - task:
      type: Classification
    dataset:
      type: C-MTEB/waimai-classification
      name: MTEB Waimai
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 87.96000000000002
    - type: ap
      value: 72.43737061465443
    - type: f1
      value: 86.48668399738767
---
## Model Details
Lim is a general text embedding model(chinese),We are continuously optimizing it.

## History
『2023-12-22』Published lim_base_zh_v0 model


## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
model_name="liujiarik/lim_base_zh"
from sentence_transformers import SentenceTransformer
sentences = ['我换手机号了', '如果我换手机怎么办?']

model = SentenceTransformer(model_name)
embeddings = model.encode(sentences)
print(embeddings)
```