add readme and pooling

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -9,11 +9,13 @@ tags:
 
 ---
 
-# {MODEL_NAME}
+# FISMWASP Autoencoder 400k-e2
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
-<!--- Describe your model here -->
+This is an embedding model that makes it possible to organise German language and technical specifications in a vector space without vector overlays. It is an S-bert model with the TSDAE architecture. The model was developed by André Osyguß from Medienwerft with the help of colleagues from FIS ASP and FIS. 
+
+This model was trained with technical ticket data. Over four hundred thousand sentences were used for training in order to achieve optimal results. 
 
 ## Usage (Sentence-Transformers)
 
@@ -120,5 +122,4 @@ SentenceTransformer(
 ```
 
 ## Citing & Authors
-
-<!--- Describe where people can find more information -->
+authored by André Osyguß