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--- |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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base_model: BAAI/bge-m3 |
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widget: |
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- source_sentence: 'search_query: i love autotrain' |
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sentences: |
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- 'search_query: huggingface auto train' |
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- 'search_query: hugging face auto train' |
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- 'search_query: i love autotrain' |
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pipeline_tag: sentence-similarity |
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datasets: |
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- avemio/German-RAG-EMBEDDING-TRIPLES-HESSIAN-AI |
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--- |
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# German-RAG-BGE-M3-TRIPLES-HESSIAN-AI |
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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. |
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It was merged with the Base-Model [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) again to maintain performance on other languages again. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) --> |
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- **Maximum Sequence Length:** 8192 tokens |
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- **Output Dimensionality:** 1024 tokens |
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- **Similarity Function:** Cosine Similarity |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
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(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}) |
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(2): Normalize() |
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) |
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``` |
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## Evaluation MTEB-Tasks |
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### Classification |
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- AmazonCounterfactualClassification |
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- AmazonReviewsClassification |
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- MassiveIntentClassification |
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- MassiveScenarioClassification |
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- MTOPDomainClassification |
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- MTOPIntentClassification |
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### Pair Classification |
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- FalseFriendsGermanEnglish |
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- PawsXPairClassification |
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### Retrieval |
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- GermanQuAD-Retrieval |
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- GermanDPR |
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### STS (Semantic Textual Similarity) |
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- GermanSTSBenchmark |
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#### Comparison between Base-Model ([BGE-M3](https://huggingface.co/BAAI/bge-m3)), Finetuned Model ([German-RAG-BGE](https://huggingface.co/avemio/German-RAG-BGE-M3-TRIPLES-HESSIAN-AI)) and Merged Model with Base-Model ([Merged-BGE](https://huggingface.co/avemio/German-RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI/)) |
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| TASK | [BGE-M3](https://huggingface.co/BAAI/bge-m3) | German-RAG-BGE | [Merged-BGE](https://huggingface.co/avemio/German-RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI/) | German-RAG vs. BGE | Merged vs. BGE | |
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|-------------------------------------|-------|----------|------------|--------------|----------------| |
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| AmazonCounterfactualClassification | 0.6908 | 0.5449 | **0.7111** | -14.59% | 2.03% | |
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| AmazonReviewsClassification | **0.4634** | 0.2745 | 0.4571 | -18.89% | -0.63% | |
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| FalseFriendsGermanEnglish | **0.5343** | 0.4777 | 0.5338 | -5.67% | -0.05% | |
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| GermanQuAD-Retrieval | **0.9444** | 0.8714 | 0.9311 | -7.30% | -1.33% | |
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| GermanSTSBenchmark | 0.8079 | 0.7921 | **0.8218** | -1.58% | 1.39% | |
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| MassiveIntentClassification | **0.6575** | 0.4884 | 0.6522 | -16.90% | -0.52% | |
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| MassiveScenarioClassification | 0.7355 | 0.5837 | **0.7381** | -15.19% | 0.25% | |
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| GermanDPR | **0.8265** | 0.7210 | 0.8159 | -10.54% | -1.06% | |
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| MTOPDomainClassification | 0.9121 | 0.7450 | **0.9139** | -16.71% | 0.17% | |
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| MTOPIntentClassification | **0.6808** | 0.4516 | 0.6684 | -22.92% | -1.25% | |
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| PawsXPairClassification | 0.5678 | 0.5077 | **0.5710** | -6.01% | 0.33% | |
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#### Comparison between Base-Model ([BGE-M3](https://huggingface.co/BAAI/bge-m3)), Merged Model with Base-Model ([Merged-BGE](https://huggingface.co/avemio/German-RAG-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) |
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| TASK | [BGE-M3](https://huggingface.co/BAAI/bge-m3) | [Merged-BGE](https://huggingface.co/avemio/German-RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI/) | [Merged-Snowflake](https://huggingface.co/avemio/German-RAG-BGE-M3-MERGED-x-SNOWFLAKE-ARCTIC-HESSIAN-AI/) | Merged-BGE vs. BGE | Merged-Snowflake vs. BGE | Merged-Snowflake vs. Merged-BGE | |
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|-------------------------------------|-------|------------|------------------|--------------------|--------------------------|---------------------------------| |
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| AmazonCounterfactualClassification | 0.6908 | 0.7111 | **0.7152** | 2.94% | 3.53% | 0.58% | |
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| AmazonReviewsClassification | **0.4634** | 0.4571 | 0.4577 | -1.36% | -1.23% | 0.13% | |
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| FalseFriendsGermanEnglish | 0.5343 | 0.5338 | **0.5378** | -0.09% | 0.66% | 0.75% | |
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| GermanQuAD-Retrieval | 0.9444 | 0.9311 | **0.9456** | -1.41% | 0.13% | 1.56% | |
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| GermanSTSBenchmark | 0.8079 | 0.8218 | **0.8558** | 1.72% | 5.93% | 4.14% | |
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| MassiveIntentClassification | 0.6575 | 0.6522 | **0.6826** | -0.81% | 3.82% | 4.66% | |
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| MassiveScenarioClassification | 0.7355 | 0.7381 | **0.7494** | 0.35% | 1.89% | 1.53% | |
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| GermanDPR | 0.8265 | 0.8159 | **0.8330** | -1.28% | 0.79% | 2.10% | |
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| MTOPDomainClassification | 0.9121 | 0.9139 | **0.9259** | 0.20% | 1.52% | 1.31% | |
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| MTOPIntentClassification | 0.6808 | 0.6684 | **0.7143** | -1.82% | 4.91% | 6.87% | |
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| PawsXPairClassification | 0.5678 | 0.5710 | **0.5803** | 0.56% | 2.18% | 1.63% | |
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## Evaluation on German-RAG-EMBEDDING-BENCHMARK |
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Accuracy is calculated by evaluating if the relevant context is the highest ranking embedding of the whole context array. |
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See Eval-Dataset and Evaluation Code [here](https://huggingface.co/datasets/avemio/German-RAG-EMBEDDING-BENCHMARK) |
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| Model Name | Accuracy | |
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|-------------------------------------------------|-----------| |
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| [bge-m3](https://huggingface.co/BAAI/bge-m3 ) | 0.8806 | |
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| [UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1) | 0.8393 | |
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| [German-RAG-BGE-M3-TRIPLES-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-BGE-M3-TRIPLES-HESSIAN-AI) | 0.8857 | |
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| [German-RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-BGE-M3-TRIPLES-MERGED-HESSIAN-AI) | **0.8866** | |
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| [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** | |
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| [German-RAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-UAE-LARGE-V1-TRIPLES-HESSIAN-AI) | 0.8763 | |
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| [German-RAG-UAE-LARGE-V1-TRIPLES-MERGED-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-UAE-LARGE-V1-TRIPLES-MERGED-HESSIAN-AI) | 0.8771 | |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("avemio/German-RAG-BGE-M3-TRIPLES-HESSIAN-AI") |
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# Run inference |
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sentences = [ |
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'The weather is lovely today.', |
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"It's so sunny outside!", |
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'He drove to the stadium.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1024] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.2.1 |
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- Transformers: 4.44.2 |
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- PyTorch: 2.4.1+cu121 |
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- Accelerate: 0.34.2 |
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- Datasets: 3.0.1 |
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- Tokenizers: 0.19.1 |
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## Citation |
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``` |
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@misc{bge-m3, |
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title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, |
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author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu}, |
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year={2024}, |
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eprint={2402.03216}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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## The German-RAG AI Team |
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[Marcel Rosiak](https://de.linkedin.com/in/marcel-rosiak) |
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[Soumya Paul](https://de.linkedin.com/in/soumya-paul-1636a68a) |
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[Siavash Mollaebrahim](https://de.linkedin.com/in/siavash-mollaebrahim-4084b5153?trk=people-guest_people_search-card) |
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[Zain ul Haq](https://de.linkedin.com/in/zain-ul-haq-31ba35196) |
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