Update README.md

README.md

@@ -10,321 +10,71 @@ base_model: BAAI/bge-reranker-base
 pipeline_tag: text-ranking
 library_name: sentence-transformers
 ---
+
 # KielEmbed-Rerank
-# A CrossEncoder based on BAAI/bge-reranker-base
+A high-performance Cross-Encoder reranker fine-tuned from `BAAI/bge-reranker-base`.
 
-This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
+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.
 
 ## Model Details
 
 ### Model Description
-- **Model Type:** Cross Encoder
-- **Base model:** [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) <!-- at revision 2cfc18c9415c912f9d8155881c133215df768a70 -->
+- **Model Type:** Cross Encoder / Relevance Reranker
+- **Base Model:** [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)
 - **Maximum Sequence Length:** 512 tokens
-- **Number of Output Labels:** 1 label
+- **Number of Output Labels:** 1 label (Regression / Logits score)
 - **Supported Modality:** Text
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
 
 ### Model Sources
-
 - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
+- **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:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
 
 ### 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': 'XLMRobertaForSequenceClassification'})
+  (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'BertForSequenceClassification'})
 )
-```
-
-## Usage
-
-### Direct Usage (Sentence Transformers)
-
-First install the Sentence Transformers library:
+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
 
-```bash
-pip install -U sentence-transformers
-```
+# Load your custom reranker from the Hugging Face Hub
+model = CrossEncoder("kiel/KielEmbed-Rerank")
 
-Then you can load this model and run inference.
-```python
-from sentence_transformers import CrossEncoder
-
-# Download from the 🤗 Hub
-model = CrossEncoder("cross_encoder_model_id")
-# Get scores for pairs of inputs
+# Get relevance scores for pairs of inputs (query, document)
 pairs = [
-    ['" The public is understandably losing patience with these unwanted phone calls , unwanted intrusions , " he said at a White House ceremony .', '" While many good people work in the telemarketing industry , the public is understandably losing patience with these unwanted phone calls , unwanted intrusions , " Mr. Bush said .'],
-    ['Federal agent Bill Polychronopoulos said it was not known if the man , 30 , would be charged .', 'Federal Agent Bill Polychronopoulos said last night the man involved in the Melbourne incident had been unarmed .'],
-    ['The companies uniformly declined to give specific numbers on customer turnover , saying they will release those figures only when they report overall company performance at year-end .', 'The companies , however , declined to give specifics on customer turnover , saying they would release figures only when they report their overall company performance .'],
-    ['Five more human cases of West Nile virus , were reported by the Mesa County Health Department on Wednesday .', 'As of this week , 103 human West Nile cases in 45 counties had been reported to the health department .'],
-    ['But the signal is designed to make it more difficult for consumers to then transfer those copies to the Internet and make them available to potentially millions of others .', 'FCC officials said the embedded electronic signal is designed to make it more difficult for consumers to then transfer copies to the Internet .'],
+    [
+        '" The public is understandably losing patience with these unwanted phone calls , unwanted intrusions , " he said at a White House ceremony .',
+        '" While many good people work in the telemarketing industry , the public is understandably losing patience with these unwanted phone calls , unwanted intrusions , " Mr. Bush said .'
+    ],
+    [
+        'Federal agent Bill Polychronopoulos said it was not known if the man , 30 , would be charged .',
+        'Federal Agent Bill Polychronopoulos said last night the man involved in the Melbourne incident had been unarmed .'
+    ],
 ]
 scores = model.predict(pairs)
 print(scores)
-# [0.5923 0.0526 0.9774 0.3382 0.8619]
 
-# Or rank different texts based on similarity to a single text
+# Alternatively, rank an array of candidate texts against a single query
 ranks = model.rank(
-    '" The public is understandably losing patience with these unwanted phone calls , unwanted intrusions , " he said at a White House ceremony .',
+    'The public is losing patience with unwanted phone calls.',
     [
-        '" While many good people work in the telemarketing industry , the public is understandably losing patience with these unwanted phone calls , unwanted intrusions , " Mr. Bush said .',
-        'Federal Agent Bill Polychronopoulos said last night the man involved in the Melbourne incident had been unarmed .',
-        'The companies , however , declined to give specifics on customer turnover , saying they would release figures only when they report their overall company performance .',
-        'As of this week , 103 human West Nile cases in 45 counties had been reported to the health department .',
-        'FCC officials said the embedded electronic signal is designed to make it more difficult for consumers to then transfer copies to the Internet .',
+        'While many good people work in telemarketing, the public is losing patience with unwanted intrusions.',
+        'Federal agents reported an unarmed suspect in the downtown incident.',
+        'Corporate earnings reports will be released at the close of the fiscal year.'
     ]
 )
-# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
-```
-
-<!--
-### 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
-
-### Training Dataset
-
-#### Unnamed Dataset
-
-* Size: 5,000 training samples
-* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
-* Approximate statistics based on the first 100 samples:
-  |          | text1                                                                              | text2                                                                              | label                                           |
-  |:---------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
-  | type     | string                                                                             | string                                                                             | int                                             |
-  | modality | text                                                                               | text                                                                               |                                                 |
-  | details  | <ul><li>min: 15 tokens</li><li>mean: 33.25 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 33.05 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>0: ~34.62%</li><li>1: ~65.38%</li></ul> |
-* Samples:
-  | text1                                                                                                                                                                                                | text2                                                                                                                                                                                             | label          |
-  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
-  | <code>" The public is understandably losing patience with these unwanted phone calls , unwanted intrusions , " he said at a White House ceremony .</code>                                            | <code>" While many good people work in the telemarketing industry , the public is understandably losing patience with these unwanted phone calls , unwanted intrusions , " Mr. Bush said .</code> | <code>0</code> |
-  | <code>Federal agent Bill Polychronopoulos said it was not known if the man , 30 , would be charged .</code>                                                                                          | <code>Federal Agent Bill Polychronopoulos said last night the man involved in the Melbourne incident had been unarmed .</code>                                                                    | <code>0</code> |
-  | <code>The companies uniformly declined to give specific numbers on customer turnover , saying they will release those figures only when they report overall company performance at year-end .</code> | <code>The companies , however , declined to give specifics on customer turnover , saying they would release figures only when they report their overall company performance .</code>              | <code>1</code> |
-* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
-  ```json
-  {
-      "activation_fn": "torch.nn.modules.linear.Identity",
-      "pos_weight": null
-  }
-  ```
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `per_device_train_batch_size`: 4
-- `num_train_epochs`: 1
-- `learning_rate`: 2e-05
-- `warmup_steps`: 0.1
-- `gradient_accumulation_steps`: 8
-- `fp16`: True
-
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `per_device_train_batch_size`: 4
-- `num_train_epochs`: 1
-- `max_steps`: -1
-- `learning_rate`: 2e-05
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: None
-- `warmup_steps`: 0.1
-- `optim`: adamw_torch_fused
-- `optim_args`: None
-- `weight_decay`: 0.0
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `optim_target_modules`: None
-- `gradient_accumulation_steps`: 8
-- `average_tokens_across_devices`: True
-- `max_grad_norm`: 1.0
-- `label_smoothing_factor`: 0.0
-- `bf16`: False
-- `fp16`: True
-- `bf16_full_eval`: False
-- `fp16_full_eval`: False
-- `tf32`: None
-- `gradient_checkpointing`: False
-- `gradient_checkpointing_kwargs`: None
-- `torch_compile`: False
-- `torch_compile_backend`: None
-- `torch_compile_mode`: None
-- `use_liger_kernel`: False
-- `liger_kernel_config`: None
-- `use_cache`: False
-- `neftune_noise_alpha`: None
-- `torch_empty_cache_steps`: None
-- `auto_find_batch_size`: False
-- `log_on_each_node`: True
-- `logging_nan_inf_filter`: True
-- `include_num_input_tokens_seen`: no
-- `log_level`: passive
-- `log_level_replica`: warning
-- `disable_tqdm`: False
-- `project`: huggingface
-- `trackio_space_id`: None
-- `trackio_bucket_id`: None
-- `trackio_static_space_id`: None
-- `per_device_eval_batch_size`: 8
-- `prediction_loss_only`: True
-- `eval_on_start`: False
-- `eval_do_concat_batches`: True
-- `eval_use_gather_object`: False
-- `eval_accumulation_steps`: None
-- `include_for_metrics`: []
-- `batch_eval_metrics`: False
-- `save_only_model`: False
-- `save_on_each_node`: False
-- `enable_jit_checkpoint`: False
-- `push_to_hub`: False
-- `hub_private_repo`: None
-- `hub_model_id`: None
-- `hub_strategy`: every_save
-- `hub_always_push`: False
-- `hub_revision`: None
-- `load_best_model_at_end`: False
-- `ignore_data_skip`: False
-- `restore_callback_states_from_checkpoint`: False
-- `full_determinism`: False
-- `seed`: 42
-- `data_seed`: None
-- `use_cpu`: False
-- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
-- `parallelism_config`: None
-- `dataloader_drop_last`: False
-- `dataloader_num_workers`: 0
-- `dataloader_pin_memory`: True
-- `dataloader_persistent_workers`: False
-- `dataloader_prefetch_factor`: None
-- `dataloader_multiprocessing_context`: None
-- `dataloader_in_order`: True
-- `remove_unused_columns`: True
-- `label_names`: None
-- `train_sampling_strategy`: random
-- `length_column_name`: length
-- `ddp_find_unused_parameters`: None
-- `ddp_bucket_cap_mb`: None
-- `ddp_broadcast_buffers`: False
-- `ddp_static_graph`: None
-- `ddp_backend`: None
-- `ddp_timeout`: 1800
-- `fsdp`: None
-- `fsdp_config`: None
-- `deepspeed`: None
-- `debug`: []
-- `skip_memory_metrics`: True
-- `do_predict`: False
-- `resume_from_checkpoint`: None
-- `local_rank`: -1
-- `prompts`: None
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: proportional
-- `router_mapping`: {}
-- `learning_rate_mapping`: {}
-- `warmup_ratio`: None
-
-</details>
-
-### Training Logs
-| Epoch | Step | Training Loss |
-|:-----:|:----:|:-------------:|
-| 0.16  | 25   | 1.7522        |
-| 0.32  | 50   | 0.4195        |
-| 0.48  | 75   | 0.3522        |
-| 0.64  | 100  | 0.3646        |
-| 0.8   | 125  | 0.3639        |
-| 0.96  | 150  | 0.3551        |
-
-
-### Training Time
-- **Training**: 5.2 minutes
-
-### Framework Versions
-- Python: 3.13.15
-- Sentence Transformers: 5.7.0
-- Transformers: 5.16.1
-- PyTorch: 2.11.0+cu128
-- Accelerate: 1.14.0
-- Datasets: 4.8.5
-- Tokenizers: 0.23.1
-
-## Additional Resources
-
-- [Training and Finetuning Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-reranker): the end-to-end guide for training or finetuning Cross Encoder (reranker) models.
-- [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video reranker models through the same API.
-- [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): training multimodal Cross Encoders.
-
-## Citation
-
-### BibTeX
-
-#### Sentence Transformers
-```bibtex
-@inproceedings{reimers-2019-sentence-bert,
+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,
     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",
     month = "11",
     year = "2019",
     publisher = "Association for Computational Linguistics",
-    url = "https://arxiv.org/abs/1908.10084",
+    url = "[https://arxiv.org/abs/1908.10084](https://arxiv.org/abs/1908.10084)",
 }
-```
-
-<!--
-## 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.*
--->