Upload README.md

README.md

@@ -1066,13 +1066,22 @@ Yinka embedding 模型是在开原模型[stella-v3.5-mrl](https://huggingface.co
 from sentence_transformers import SentenceTransformer
 from sklearn.preprocessing import normalize
 
-model = SentenceTransformer("")
+model = SentenceTransformer("Classical/Yinka")
 # 注意先不要normalize! 选取前n维后再normalize
 vectors = model.encode(["text1", "text2"], normalize_embeddings=False)
 print(vectors.shape)  # shape is [2,1792]
 n_dims = 768
 cut_vecs = normalize(vectors[:, :n_dims])
 ```
+## 结果
+| Model Name | Model Size (GB) | Dimension | Sequence Length |  Classification (9) | Clustering (4) | Pair Classification (2) | Reranking (4) | Retrieval (8) | STS (8) | Average (35) |
+|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| [Yinka](https://huggingface.co/Classical/Yinka) | 1.21 | 1792 | 512 | 74.30 | 61.99 | 89.87 | 69.77 | 74.40 | 63.30 | 70.79 |
+| [stella-v3.5-mrl](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d) |1.21 | 1792 | 512 | 71.56 | 54.39 | 88.09 | 68.45 | 73.51 | 62.48 |  68.56 |
+| [Yinka](https://huggingface.co/Classical/Yinka) | 1.21 | 1792 | 512 | 74.30 | 61.99 | 89.87 | 69.77 | 74.40 | 63.30 | 70.79 |
+| [piccolo-large-zh-v2](https://huggingface.co/sensenova/piccolo-large-zh-v2) | 1.21 | 1792 | 512 | 74.59 | 62.17 | 90.24 | 70 | 74.36 | 63.5 | 70.95 |
+
+
 
 ## 训练细节
 TODO