Update README.md

README.md

@@ -1195,7 +1195,7 @@ model-index:
     - type: map_at_5
       value: 15.271
     - type: mrr_at_1
-      value: 69.0
+      value: 69
     - type: mrr_at_10
       value: 75.304
     - type: mrr_at_100
@@ -1219,9 +1219,9 @@ model-index:
     - type: ndcg_at_5
       value: 42.104
     - type: precision_at_1
-      value: 69.0
+      value: 69
     - type: precision_at_10
-      value: 33.0
+      value: 33
     - type: precision_at_100
       value: 10.75
     - type: precision_at_1000
@@ -1815,7 +1815,7 @@ model-index:
     - type: ndcg_at_3
       value: 85.435
     - type: ndcg_at_5
-      value: 87.0
+      value: 87
     - type: precision_at_1
       value: 81.24
     - type: precision_at_10
@@ -1910,13 +1910,13 @@ model-index:
     - type: precision_at_1
       value: 24.8
     - type: precision_at_10
-      value: 12.0
+      value: 12
     - type: precision_at_100
       value: 2.5420000000000003
     - type: precision_at_1000
       value: 0.39899999999999997
     - type: precision_at_3
-      value: 20.0
+      value: 20
     - type: precision_at_5
       value: 17.4
     - type: recall_at_1
@@ -2197,7 +2197,7 @@ model-index:
     - type: recall_at_100
       value: 96.167
     - type: recall_at_1000
-      value: 100.0
+      value: 100
     - type: recall_at_3
       value: 74.117
     - type: recall_at_5
@@ -2250,7 +2250,7 @@ model-index:
     - type: manhattan_precision
       value: 91.72482552342971
     - type: manhattan_recall
-      value: 92.0
+      value: 92
     - type: max_accuracy
       value: 99.83861386138614
     - type: max_ap
@@ -2331,7 +2331,7 @@ model-index:
     - type: map_at_5
       value: 1.001
     - type: mrr_at_1
-      value: 76.0
+      value: 76
     - type: mrr_at_10
       value: 85.667
     - type: mrr_at_100
@@ -2343,7 +2343,7 @@ model-index:
     - type: mrr_at_5
       value: 85.667
     - type: ndcg_at_1
-      value: 72.0
+      value: 72
     - type: ndcg_at_10
       value: 68.637
     - type: ndcg_at_100
@@ -2355,7 +2355,7 @@ model-index:
     - type: ndcg_at_5
       value: 71.808
     - type: precision_at_1
-      value: 76.0
+      value: 76
     - type: precision_at_10
       value: 73.8
     - type: precision_at_100
@@ -2365,7 +2365,7 @@ model-index:
     - type: precision_at_3
       value: 74.667
     - type: precision_at_5
-      value: 78.0
+      value: 78
     - type: recall_at_1
       value: 0.22100000000000003
     - type: recall_at_10
@@ -2596,6 +2596,10 @@ model-index:
       value: 85.53503846009764
     - type: max_f1
       value: 77.68167368965773
+license: apache-2.0
+language:
+- en
+library_name: transformers
 ---
 
 <br><br>
@@ -2605,7 +2609,7 @@ model-index:
 </p>
 
 <p align="center">
-<b>The crispy rerank family from <a href="https://mixedbread.ai"><b>mixedbread ai</b></a>.</b>
+<b>The crispy sentence embedding family from <a href="https://mixedbread.ai"><b>mixedbread ai</b></a>.</b>
 </p>
 
 # mxbai-embed-2d-large-v1
@@ -2617,7 +2621,73 @@ model-index:
 
 Currently, the best way to use our models is with the most recent version of sentence-transformers.
 
-`pip install -U sentence-transformers`
+```bash
+python -m pip install -U sentence-transformers
+```
+
+
+```python
+from sentence_transformers import models, SentenceTransformer
+from sentence_transformers.util import cos_sim
+
+
+# 1. load model with `cls` pooling
+word_embedding_model = models.Transformer("mixedbread-ai/mxbai-embed-2d-large-v1")
+pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), pooling_mode="cls")
+model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
+
+# 2. set adaptive layer and embedding size.
+# it is recommended to set layers from 20 to 24.
+new_num_layers = 22  # 1d: layer
+model[0].auto_model.encoder.layer = model[0].auto_model.encoder.layer[:new_num_layers]
+new_embedding_size = 768  # 2d: embedding size
+
+
+# 3. encode
+embeddings = model.encode(
+    [
+        'Who is german and likes bread?',
+        'Everybody in German.'
+    ]
+)
+
+# Similarity of the first sentence with the other two
+similarities = cos_sim(embeddings[0, :new_embedding_size], embeddings[1, :new_embedding_size])
+
+print('similarities:', similarities)
+```
 
 ### angle-emb
 
+You can also use the lastest `angle-emb` for inference, as follows:
+
+```bash
+python -m pip install -U angle-emb
+```
+
+```python
+from angle_emb import AnglE
+from sentence_transformers.util import cos_sim
+
+# 1. load model
+model = AnglE.from_pretrained("mixedbread-ai/mxbai-embed-2d-large-v1", pooling_strategy='cls').cuda()
+
+
+# 2. set adaptive layer and embedding size.
+# it is recommended to set layers from 20 to 24.
+layer_index = 22  # 1d: layer
+embedding_size = 768  # 2d: embedding size
+
+# 3. encode
+embeddings = model.encode([
+    'Who is german and likes bread?',
+    'Everybody in German.'
+], layer_index=layer_index, embedding_size=embedding_size)
+
+similarities = cos_sim(embeddings[0], embeddings[1:])
+print('similarities:', similarities)
+```
+
+### Using API
+You’ll be able to use the models through our API as well. The API is coming soon and will have some exciting features. Stay tuned!
+