Update README.md

README.md

@@ -58,54 +58,39 @@ widget:
 pipeline_tag: sentence-similarity
 library_name: sentence-transformers
 ---
+
 # KielEmbed-Code
-# A SentenceTransformer based on microsoft/codebert-base
+A specialized dense embedding model fine-tuned from `microsoft/codebert-base` for code and text representation.
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.
+This is a [sentence-transformers](https://www.SBERT.net) model fine-tuned from [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base). It maps sentences and code blocks into a 768-dimensional dense vector space optimized for semantic textual similarity, semantic search, and clustering tasks.
 
 ## Model Details
 
 ### Model Description
-- **Model Type:** Sentence Transformer
-- **Base model:** [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) <!-- at revision 3b0952feddeffad0063f274080e3c23d75e7eb39 -->
+- **Model Type:** Sentence Transformer / Dense Embedding Backbone
+- **Base Model:** [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base)
 - **Maximum Sequence Length:** 512 tokens
 - **Output Dimensionality:** 768 dimensions
 - **Similarity Function:** Cosine Similarity
-- **Supported Modality:** Text
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+- **Supported Modality:** Text & Code
 
 ### Model Sources
-
 - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
 - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+- **Hugging Face Hub:** [kiel/KielEmbed-Code](https://huggingface.co/kiel/KielEmbed-Code)
 
 ### Full Model Architecture
-
-```
+```text
 SentenceTransformer(
   (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'RobertaModel'})
   (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', 'include_prompt': True})
 )
-```
-
-## 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 model and run inference:Pythonfrom sentence_transformers import SentenceTransformer
 
-```bash
-pip install -U sentence-transformers
-```
-Then you can load this model and run inference.
-```python
-from sentence_transformers import SentenceTransformer
+# Load your custom fine-tuned CodeBERT model from the Hugging Face Hub
+model = SentenceTransformer("kiel/KielEmbed-Code")
 
-# Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
 # Run inference
 sentences = [
     '" Any decision on Charleroi will have huge implications for regional airports in France , " he said .',
@@ -119,252 +104,16 @@ print(embeddings.shape)
 # Get the similarity scores for the embeddings
 similarities = model.similarity(embeddings, embeddings)
 print(similarities)
-# tensor([[1.0000, 0.9671, 0.9596],
-#         [0.9671, 1.0000, 0.9494],
-#         [0.9596, 0.9494, 1.0000]])
-```
-<!--
-### 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: 10,000 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: 12 tokens</li><li>mean: 27.7 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 27.64 tokens</li><li>max: 46 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>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
-  ```json
-  {
-      "loss_fct": "torch.nn.modules.loss.MSELoss",
-      "cos_score_transformation": "torch.nn.modules.linear.Identity"
-  }
-  ```
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `num_train_epochs`: 1
-- `learning_rate`: 2e-05
-- `warmup_steps`: 0.1
-- `gradient_accumulation_steps`: 4
-- `fp16`: True
-
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `per_device_train_batch_size`: 8
-- `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`: 4
-- `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  | 50   | 28.0752       |
-| 0.32  | 100  | 24.7668       |
-| 0.48  | 150  | 21.0055       |
-| 0.64  | 200  | 26.5538       |
-| 0.8   | 250  | 26.0600       |
-| 0.96  | 300  | 26.3027       |
-
-
-### Training Time
-- **Training**: 5.1 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 Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-sentence-transformers): the end-to-end guide for training or finetuning Sentence Transformer models.
-- [Introduction to Matryoshka Embedding Models](https://huggingface.co/blog/matryoshka): variable-size embeddings that can be truncated with minimal quality loss.
-- [Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval](https://huggingface.co/blog/embedding-quantization): post-training compression of embedding vectors.
-- [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video models through the same API.
-- [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): train multimodal embedding models, with a Visual Document Retrieval walkthrough.
-
-## Citation
-
-### BibTeX
-
-#### Sentence Transformers
-```bibtex
-@inproceedings{reimers-2019-sentence-bert,
+Training DetailsTraining DatasetKiel Code & Semantic CorpusSize: 10,000 training samplesColumns: text1, text2, and labelApproximate Token Statistics (First 100 samples):Text 1: Min: 12 tokens | Mean: 27.7 tokens | Max: 41 tokensText 2: Min: 15 tokens | Mean: 27.64 tokens | Max: 46 tokensLabel Distribution: Class 0 (~34.62%), Class 1 (~65.38%)Loss Function: CosineSimilarityLoss with parameters:JSON{
+    "loss_fct": "torch.nn.modules.loss.MSELoss",
+    "cos_score_transformation": "torch.nn.modules.linear.Identity"
+}
+Training HyperparametersPer Device Train Batch Size: 8Gradient Accumulation Steps: 4 (Effective batch size = 32)Learning Rate: 2e-05Number of Epochs: 1Warmup Steps: 0.1Mixed Precision: FP16 EnabledOptimizer: adamw_torch_fusedTraining LogsEpochStepTraining Loss0.165028.07520.3210024.76680.4815021.00550.6420026.55380.825026.06000.9630026.3027Total Training Time: 5.1 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 Embedding Models with Sentence Transformers: End-to-end guide for fine-tuning Sentence Transformer models.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)",
 }
-```
-
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-
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