Update README.md

README.md

@@ -13,18 +13,18 @@ widget:
   - 'search_query: i love autotrain'
 pipeline_tag: sentence-similarity
 datasets:
-- avemio/German_RAG-EMBEDDING-TRIPLES-HESSIAN-AI
+- avemio/German-RAG-EMBEDDING-TRIPLES-HESSIAN-AI
 license: mit
 language:
 - de
 - en
 ---
 
-<img src="https://www.German_RAG.ai/wp-content/uploads/2024/12/German_RAG-ICON-TO-WORDLOGO-Animation_Loop-small-ezgif.com-video-to-gif-converter.gif" alt="German_RAG Logo" width="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+<img src="https://www.German-RAG.ai/wp-content/uploads/2024/12/German-RAG-ICON-TO-WORDLOGO-Animation_Loop-small-ezgif.com-video-to-gif-converter.gif" alt="German-RAG Logo" width="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
 
-# German_RAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI
+# German-RAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI
 
-This is a [sentence-transformers](https://www.SBERT.net) model trained on this [Dataset](https://huggingface.co/datasets/avemio/German_RAG-Embedding-Triples-Hessian-AI) with roughly 300k Triple-Samples. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+This is a [sentence-transformers](https://www.SBERT.net) model trained on this [Dataset](https://huggingface.co/datasets/avemio/German-RAG-Embedding-Triples-Hessian-AI) with roughly 300k Triple-Samples. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
 
 ## Model Details
 
@@ -75,7 +75,7 @@ SentenceTransformer(
 ### STS (Semantic Textual Similarity)
 - GermanSTSBenchmark
 
-| TASK                                | [UAE](https://huggingface.co/WhereIsAI/UAE-Large-V1/)   | German_RAG-UAE | [Merged-UAE](https://huggingface.co/avemio/German_RAG-UAE-LARGE-V1-TRIPLES-MERGED-HESSIAN-AI/) | German_RAG vs. UAE | Merged vs. UAE |
+| TASK                                | [UAE](https://huggingface.co/WhereIsAI/UAE-Large-V1/)   | German-RAG-UAE | [Merged-UAE](https://huggingface.co/avemio/German-RAG-UAE-LARGE-V1-TRIPLES-MERGED-HESSIAN-AI/) | German-RAG vs. UAE | Merged vs. UAE |
 |-------------------------------------|-------|----------|------------|--------------|----------------|
 | AmazonCounterfactualClassification | **0.5650** | 0.5449   | 0.5401     | -2.01%       | -2.48%         |
 | AmazonReviewsClassification         | 0.2738 | 0.2745   | **0.2782**     | 0.08%        | 0.44%          |
@@ -90,20 +90,20 @@ SentenceTransformer(
 | PawsXPairClassification             | **0.5452** | 0.5077   | 0.5162     | -3.76%       | -2.90%         |
 
 
-## Evaluation on German_RAG-EMBEDDING-BENCHMARK
+## Evaluation on German-RAG-EMBEDDING-BENCHMARK
 
 Accuracy is calculated by evaluating if the relevant context is the highest ranking embedding of the whole context array.
-See Eval-Dataset and Evaluation Code [here](https://huggingface.co/datasets/avemio/German_RAG-EMBEDDING-BENCHMARK)
+See Eval-Dataset and Evaluation Code [here](https://huggingface.co/datasets/avemio/German-RAG-EMBEDDING-BENCHMARK)
 
 | Model Name                                       | Accuracy  |
 |-------------------------------------------------|-----------|
 | [bge-m3](https://huggingface.co/BAAI/bge-m3  )                                     | 0.8806    |
 | [UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1)                            | 0.8393    |
-| [German_RAG-BGE-M3-TRIPLES-HESSIAN-AI](https://huggingface.co/avemio/German_RAG-BGE-M3-TRIPLES-HESSIAN-AI)             | 0.8857    |
-| [German_RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI](https://huggingface.co/avemio/German_RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI)      | **0.8866** |
-| [German_RAG-BGE-M3-MERGED-x-SNOWFLAKE-ARCTIC-HESSIAN-AI](https://huggingface.co/avemio/German_RAG-BGE-M3-MERGED-x-SNOWFLAKE-ARCTIC-HESSIAN-AI)   | **0.8866** |
-| [German_RAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI](https://huggingface.co/avemio/German_RAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI)       | 0.8763    |
-| [German_RAG-UAE-LARGE-V1-TRIPLES-MERGED-HESSIAN-AI](https://huggingface.co/avemio/German_RAG-UAE-LARGE-V1-TRIPLES-MERGED-HESSIAN-AI)   | 0.8771    |
+| [German-RAG-BGE-M3-TRIPLES-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-BGE-M3-TRIPLES-HESSIAN-AI)             | 0.8857    |
+| [German-RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI)      | **0.8866** |
+| [German-RAG-BGE-M3-MERGED-x-SNOWFLAKE-ARCTIC-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-BGE-M3-MERGED-x-SNOWFLAKE-ARCTIC-HESSIAN-AI)   | **0.8866** |
+| [German-RAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI)       | 0.8763    |
+| [German-RAG-UAE-LARGE-V1-TRIPLES-MERGED-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-UAE-LARGE-V1-TRIPLES-MERGED-HESSIAN-AI)   | 0.8771    |
 
 ## Usage
 
@@ -120,7 +120,7 @@ Then you can load this model and run inference.
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("avemio/German_RAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI")
+model = SentenceTransformer("avemio/German-RAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI")
 # Run inference
 sentences = [
     'The weather is lovely today.',