update

README.md

@@ -98,6 +98,17 @@ for doc, score in doc_score_pairs:
     print(score, doc)
 ```
 
+## Technical Details
+
+In the following some technical details how this model must be used:
+
+| Setting | Value |
+| --- | :---: |
+| Dimensions | 384 |
+| Produces normalized embeddings | No |
+| Pooling-Method | CLS pooling |
+| Suitable score functions | dot-product (e.g. `util.dot_score`) |
+
 ----
 
 
@@ -127,11 +138,13 @@ The full training script is accessible in this current repository: `train_script
 
 We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
 
-#### Training data
+#### Training
 
 We use the concatenation from multiple datasets to fine-tune our model. In total we have about 215M (question, answer) pairs.
 We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
 
+The model was trained with [MultipleNegativesRankingLoss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) using CLS-pooling, dot-product as similarity function, and a scale of 1.
+