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README.md

@@ -5,15 +5,14 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-datasets:
-- legacy107/qa_wikipedia_sentence_transformer
+
 ---
 
-# multi-qa-mpnet-base-dot-v1-wikipedia-search
+# {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.
 
-This is a fine-tuned version on [legacy107/qa_wikipedia_sentence_transformer](https://huggingface.co/datasets/legacy107/qa_wikipedia_sentence_transformer)
+<!--- Describe your model here -->
 
 ## Usage (Sentence-Transformers)
 
@@ -73,13 +72,7 @@ print(sentence_embeddings)
 
 ## Evaluation Results
 
-Result at step 16000
-
-| accuracy_cosinus | accuracy_manhattan | accuracy_euclidean |
-| -------- | -------- | -------- |
-| 0.986408328513592 | 0.986697513013302 | 0.986408328513592 |
-
-
+<!--- 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})
 
@@ -89,7 +82,7 @@ The model was trained with the parameters:
 
 **DataLoader**:
 
-`torch.utils.data.dataloader.DataLoader` of length 3468 with parameters:
+`torch.utils.data.dataloader.DataLoader` of length 3746 with parameters:
 ```
 {'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
 ```
@@ -114,7 +107,7 @@ Parameters of the fit()-Method:
     },
     "scheduler": "WarmupLinear",
     "steps_per_epoch": null,
-    "warmup_steps": 1734,
+    "warmup_steps": 1873,
     "weight_decay": 0.01
 }
 ```