Revert "Adjusts model card."

This reverts commit a1ca4e664ac89e2f89edcb400008512cdf1f48f6.

README.md

@@ -1,12 +1,6 @@
 ---
-language: 
-  - en
-datasets:
-- SNLI
-- MNLI
 pipeline_tag: sentence-similarity
 tags:
-- zero-shot-classification
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
@@ -15,12 +9,9 @@ tags:
 
 # {MODEL_NAME}
 
-A Siamese network model trained for zero-shot and few-shot text classification.
+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.
 
-The base model is [mpnet-base](https://huggingface.co/microsoft/mpnet-base).
-It was trained on [SNLI](https://nlp.stanford.edu/projects/snli/) and [MNLI](https://cims.nyu.edu/~sbowman/multinli/).
-
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space.
+<!--- Describe your model here -->
 
 ## Usage (Sentence-Transformers)
 
@@ -42,6 +33,7 @@ print(embeddings)
 ```
 
 
+
 ## Usage (HuggingFace Transformers)
 Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
 
@@ -77,3 +69,61 @@ sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']
 print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
+
+
+
+## Evaluation Results
+
+<!--- Describe how your model was evaluated -->
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+
+
+## Training
+The model was trained with the parameters:
+
+**DataLoader**:
+
+`zsde.training.NoDuplicatesDataLoader` of length 75000 with parameters:
+```
+{'batch_size': 16}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
+  ```
+  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
+  ```
+
+Parameters of the fit()-Method:
+```
+{
+    "callback": null,
+    "epochs": 1,
+    "evaluation_steps": 7500,
+    "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'transformers.optimization.AdamW'>",
+    "optimizer_params": {
+        "lr": 2e-05
+    },
+    "scheduler": "WarmupLinear",
+    "steps_per_epoch": 75000,
+    "warmup_steps": 7500,
+    "weight_decay": 0.01
+}
+```
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+)
+```
+
+## Citing & Authors
+
+<!--- Describe where people can find more information -->