add sentence transformers configs (#4)

- sbert.net (67a456de1d77c6159603f4bad4a2fe6b12d8bcda)
- update readme (0f45e2944ec95914b340972ad1b9bd6d66ef6c70)
- add python example (a68bac4365867eaf42a3ac2e2530cd379479c056)

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+    "word_embedding_dimension": 384,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false
+}

README.md

@@ -2671,4 +2671,14 @@ If you find our paper or models helpful, please consider cite as follows:
 
 ## Limitations
 
-This model only works for English texts. Long texts will be truncated to at most 512 tokens.
+This model only works for English texts. Long texts will be truncated to at most 512 tokens.
+
+## Sentence Transformers
+
+Below is an example for usage with sentence_transformers. `pip install sentence_transformers~=2.2.2`
+This is community contributed, and results may vary up to numerical precision.
+```python
+from sentence_transformers import SentenceTransformer
+model = SentenceTransformer('intfloat/e5-small-v2')
+embeddings = model.encode(input_texts, normalize_embeddings=True)
+```

modules.json

@@ -0,0 +1,20 @@
+[
+    {
+      "idx": 0,
+      "name": "0",
+      "path": "",
+      "type": "sentence_transformers.models.Transformer"
+    },
+    {
+      "idx": 1,
+      "name": "1",
+      "path": "1_Pooling",
+      "type": "sentence_transformers.models.Pooling"
+    },
+    {
+      "idx": 2,
+      "name": "2",
+      "path": "2_Normalize",
+      "type": "sentence_transformers.models.Normalize"
+    }
+  ]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}