Adjusts model card.

README.md

@@ -1,6 +1,12 @@
 ---
+language: 
+  - en
+datasets:
+- SNLI
+- MNLI
 pipeline_tag: sentence-similarity
 tags:
+- zero-shot-classification
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
@@ -9,9 +15,12 @@ tags:
 
 # {MODEL_NAME}
 
-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.
+A Siamese network model trained for zero-shot and few-shot text classification.
 
-<!--- Describe your model here -->
+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.
 
 ## Usage (Sentence-Transformers)
 
@@ -33,7 +42,6 @@ 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.
 
@@ -69,61 +77,3 @@ 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 -->