Update README.md

README.md

@@ -3,18 +3,18 @@ tags:
 - sentence-transformers
 - cross-encoder
 - reranker
-- generated_from_trainer
-- dataset_size:5000
-- loss:BinaryCrossEntropyLoss
 base_model: BAAI/bge-reranker-base
 pipeline_tag: text-ranking
 library_name: sentence-transformers
 ---
 
-# KielEmbed-Rerank
-A high-performance Cross-Encoder reranker fine-tuned from `BAAI/bge-reranker-base`.
+# Kiel-2-Rerank
 
-This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model fine-tuned from [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) using the [sentence-transformers](https://www.SBERT.net) library. It evaluates query-document text pairs jointly to compute precise relevance scores for semantic search and multi-stage retrieval pipelines.
+**Kiel-2-Rerank** is a high-performance Cross-Encoder reranker based on `BAAI/bge-reranker-base`.
+
+This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model that evaluates query-document text pairs jointly to compute precise relevance scores for semantic search and multi-stage retrieval pipelines.
+
+---
 
 ## Model Details
 
@@ -29,17 +29,25 @@ This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.h
 - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
 - **Cross Encoder Guide:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
 - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face Hub:** [kiel2/Kiel-2-Rerank](https://huggingface.co/kiel2/Kiel-2-Rerank)
 
 ### Full Model Architecture
 ```text
 CrossEncoder(
   (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'BertForSequenceClassification'})
 )
-UsageDirect Usage (Sentence Transformers)First, install the Sentence Transformers library:Bashpip install -U sentence-transformers
-Then load your deployed model and run inference:Pythonfrom sentence_transformers import CrossEncoder
+Direct Usage (Sentence Transformers)
+First, install the Sentence Transformers library:
+
+Bash
+pip install -U sentence-transformers
+Then load your model and run inference:
+
+Python
+from sentence_transformers import CrossEncoder
 
 # Load your custom reranker from the Hugging Face Hub
-model = CrossEncoder("kiel/KielEmbed-Rerank")
+model = CrossEncoder("kiel2/Kiel-2-Rerank")
 
 # Get relevance scores for pairs of inputs (query, document)
 pairs = [
@@ -65,11 +73,9 @@ ranks = model.rank(
     ]
 )
 print(ranks)
-Training DetailsTraining DatasetKiel Reranker CorpusSize: 5,000 training samplesColumns: text1, text2, and labelDataset Statistics:Text 1: Min: 15 tokens | Mean: 33.25 tokens | Max: 58 tokensText 2: Min: 16 tokens | Mean: 33.05 tokens | Max: 52 tokensLabel Distribution: Class 0 (~34.62%), Class 1 (~65.38%)Loss Function: BinaryCrossEntropyLoss with parameters:JSON{
-    "activation_fn": "torch.nn.modules.linear.Identity",
-    "pos_weight": null
-}
-Training HyperparametersPer Device Train Batch Size: 4Gradient Accumulation Steps: 8 (Effective batch size = 32)Learning Rate: 2e-05Number of Epochs: 1Warmup Steps: 0.1Mixed Precision: FP16 EnabledOptimizer: adamw_torch_fusedTraining LogsEpochStepTraining Loss0.16251.75220.32500.41950.48750.35220.641000.36460.81250.36390.961500.3551Total Training Time: 5.2 minutesFramework VersionsPython: 3.13.15Sentence Transformers: 5.7.0Transformers: 5.16.1PyTorch: 2.11.0+cu128Accelerate: 1.14.0Datasets: 4.8.5Tokenizers: 0.23.1Additional ResourcesTraining and Finetuning Reranker Models with Sentence Transformers: Comprehensive guide for training custom Cross-Encoders.CitationBibTeXCode snippet@inproceedings{reimers-2019-sentence-bert,
+Citation
+Code snippet
+@inproceedings{reimers-2019-sentence-bert,
     title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
     author = "Reimers, Nils and Gurevych, Iryna",
     booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
@@ -77,4 +83,4 @@ Training HyperparametersPer Device Train Batch Size: 4Gradient Accumulation Step
     year = "2019",
     publisher = "Association for Computational Linguistics",
     url = "[https://arxiv.org/abs/1908.10084](https://arxiv.org/abs/1908.10084)",
-}
+}