metadata
license: apache-2.0
tags:
- mteb
model-index:
- name: lim_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 installed:
pip install -U sentence-transformers
Then you can use the model like this:
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)