## 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)
```
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"C-MTEB/CMedQAv1-reranking", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "map", "value": 83.84687250720238}, {"type": "mrr", "value": 86.34579365079364}]}, {"task": {"type": "Reranking"}, "dataset": {"name": "MTEB CMedQAv2", "type": "C-MTEB/CMedQAv2-reranking", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "map", "value": 84.7457752094449}, {"type": "mrr", "value": 87.41591269841268}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB Cmnli", "type": "C-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": {"name": "MTEB IFlyTek", "type": "C-MTEB/IFlyTek-classification", "config": "default", "split": "validation", "revision": "None"}, "metrics": [{"type": "accuracy", "value": 47.34128510965756}, {"type": "f1", "value": 35.49963469301016}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB JDReview", "type": "C-MTEB/JDReview-classification", "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": {"name": "MTEB MMarcoReranking", "type": "C-MTEB/Mmarco-reranking", "config": "default", "split": "dev", "revision": "None"}, "metrics": [{"type": "map", "value": 20.56449688140155}, {"type": "mrr", "value": 19.60753968253968}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB MassiveIntentClassification (zh-CN)", "type": "mteb/amazon_massive_intent", "config": "zh-CN", "split": "test", "revision": 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"validation", "revision": "None"}, "metrics": [{"type": "accuracy", "value": 53.269999999999996}, {"type": "f1", "value": 51.410630382886445}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB ThuNewsClusteringP2P", "type": "C-MTEB/ThuNewsClusteringP2P", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "v_measure", "value": 63.344532225921434}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB ThuNewsClusteringS2S", "type": "C-MTEB/ThuNewsClusteringS2S", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "v_measure", "value": 60.33437882010517}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB Waimai", "type": "C-MTEB/waimai-classification", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "accuracy", "value": 87.96000000000002}, {"type": "ap", "value": 72.43737061465443}, {"type": "f1", "value": 86.48668399738767}]}]}]}
transformers pytorch bert feature-extraction mteb license:apache-2.0 model-index endpoints_compatible region:us