Update README.md

README.md

@@ -16,12 +16,92 @@ datasets:
 - avemio-digital/GRAG-Embedding-Triples-Hessian-AI
 ---
 
-# Model Trained Using AutoTrain
 
-- Problem type: Sentence Transformers
+# GRAG-BGE-M3-TRIPLES-HESSIAN-AI
 
-## Validation Metrics
-No validation metrics available
+This is a [sentence-transformers](https://www.SBERT.net) model trained on this [Dataset](https://huggingface.co/datasets/avemio/GRAG-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.
+It was merged with the Base-Model [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) again to maintain performance on other languages again.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 1024 tokens
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Evaluation MTEB-Tasks 
+
+### Classification
+- AmazonCounterfactualClassification
+- AmazonReviewsClassification
+- MassiveIntentClassification
+- MassiveScenarioClassification
+- MTOPDomainClassification
+- MTOPIntentClassification
+
+### Pair Classification
+- FalseFriendsGermanEnglish
+- PawsXPairClassification
+
+### Retrieval
+- GermanQuAD-Retrieval
+- GermanDPR
+
+### STS (Semantic Textual Similarity)
+- GermanSTSBenchmark
+
+#### Comparison between Base-Model ([BGE-M3](https://huggingface.co/BAAI/bge-m3)), Finetuned Model ([GRAG-BGE](https://huggingface.co/avemio/GRAG-BGE-M3-TRIPLES-HESSIAN-AI)) and Merged Model with Base-Model ([Merged-BGE](https://huggingface.co/avemio/GRAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI/))
+
+| TASK                                | [BGE-M3](https://huggingface.co/BAAI/bge-m3)   | GRAG-BGE | [Merged-BGE](https://huggingface.co/avemio/GRAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI/) | GRAG vs. BGE | Merged vs. BGE |
+|-------------------------------------|-------|----------|------------|--------------|----------------|
+| AmazonCounterfactualClassification | 0.6908 | 0.5449   | **0.7111**     | -14.59%      | 2.03%          |
+| AmazonReviewsClassification         | **0.4634** | 0.2745   | 0.4571     | -18.89%      | -0.63%         |
+| FalseFriendsGermanEnglish           | **0.5343** | 0.4777   | 0.5338     | -5.67%       | -0.05%         |
+| GermanQuAD-Retrieval                | **0.9444** | 0.8714   | 0.9311     | -7.30%       | -1.33%         |
+| GermanSTSBenchmark                  | 0.8079 | 0.7921   | **0.8218**     | -1.58%       | 1.39%          |
+| MassiveIntentClassification         | **0.6575** | 0.4884   | 0.6522     | -16.90%      | -0.52%         |
+| MassiveScenarioClassification       | 0.7355 | 0.5837   | **0.7381**     | -15.19%      | 0.25%          |
+| GermanDPR                           | **0.8265** | 0.7210   | 0.8159     | -10.54%      | -1.06%         |
+| MTOPDomainClassification            | 0.9121 | 0.7450   | **0.9139**     | -16.71%      | 0.17%          |
+| MTOPIntentClassification            | **0.6808** | 0.4516   | 0.6684     | -22.92%      | -1.25%         |
+| PawsXPairClassification             | 0.5678 | 0.5077   | **0.5710**     | -6.01%       | 0.33%          |
+
+#### Comparison between Base-Model ([BGE-M3](https://huggingface.co/BAAI/bge-m3)), Merged Model with Base-Model ([Merged-BGE](https://huggingface.co/avemio/GRAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI/)) and our Merged-Model merged with [Snowflake/snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0)
+
+| TASK                                | [BGE-M3](https://huggingface.co/BAAI/bge-m3)   | [Merged-BGE](https://huggingface.co/avemio/GRAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI/) | [Merged-Snowflake](https://huggingface.co/avemio/GRAG-BGE-M3-MERGED-x-SNOWFLAKE-ARCTIC-HESSIAN-AI/) | Merged-BGE vs. BGE | Merged-Snowflake vs. BGE | Merged-Snowflake vs. Merged-BGE |
+|-------------------------------------|-------|------------|------------------|--------------------|--------------------------|---------------------------------|
+| AmazonCounterfactualClassification | 0.6908 | 0.7111     | **0.7152**           | 2.94%             | 3.53%                   | 0.58%                          |
+| AmazonReviewsClassification         | **0.4634** | 0.4571     | 0.4577           | -1.36%            | -1.23%                  | 0.13%                          |
+| FalseFriendsGermanEnglish           | 0.5343 | 0.5338     | **0.5378**           | -0.09%            | 0.66%                   | 0.75%                          |
+| GermanQuAD-Retrieval                | 0.9444 | 0.9311     | **0.9456**           | -1.41%            | 0.13%                   | 1.56%                          |
+| GermanSTSBenchmark                  | 0.8079 | 0.8218     | **0.8558**           | 1.72%             | 5.93%                   | 4.14%                          |
+| MassiveIntentClassification         | 0.6575 | 0.6522     | **0.6826**           | -0.81%            | 3.82%                   | 4.66%                          |
+| MassiveScenarioClassification       | 0.7355 | 0.7381     | **0.7494**           | 0.35%             | 1.89%                   | 1.53%                          |
+| GermanDPR                           | 0.8265 | 0.8159     | **0.8330**           | -1.28%            | 0.79%                   | 2.10%                          |
+| MTOPDomainClassification            | 0.9121 | 0.9139     | **0.9259**           | 0.20%             | 1.52%                   | 1.31%                          |
+| MTOPIntentClassification            | 0.6808 | 0.6684     | **0.7143**           | -1.82%            | 4.91%                   | 6.87%                          |
+| PawsXPairClassification             | 0.5678 | 0.5710     | **0.5803**           | 0.56%             | 2.18%                   | 1.63%                          |
 
 ## Usage
 
@@ -37,18 +117,89 @@ Then you can load this model and run inference.
 ```python
 from sentence_transformers import SentenceTransformer
 
-# Download from the Hugging Face Hub
-model = SentenceTransformer("sentence_transformers_model_id")
+# Download from the 🤗 Hub
+model = SentenceTransformer("avemio/GRAG-BGE-M3-TRIPLES-HESSIAN-AI")
 # Run inference
 sentences = [
-    'search_query: autotrain',
-    'search_query: auto train',
-    'search_query: i love autotrain',
+    'The weather is lovely today.',
+    "It's so sunny outside!",
+    'He drove to the stadium.',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
+# [3, 1024]
 
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities.shape)
-```
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.2.1
+- Transformers: 4.44.2
+- PyTorch: 2.4.1+cu121
+- Accelerate: 0.34.2
+- Datasets: 3.0.1
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->