Upload multi-domain fine-tuned KielEmbed-Code weights

1_Pooling/config.json

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+{
+    "embedding_dimension": 768,
+    "pooling_mode": "mean",
+    "include_prompt": true
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:10000
+- loss:CosineSimilarityLoss
+base_model: microsoft/codebert-base
+widget:
+- source_sentence: But Close wondered whether the package would be worth the cost
+    of licensing the third-party software , along with Salesforce.com 's rental price
+    .
+  sentences:
+  - Close also questions whether it would be worth the cost of licensing third-party
+    software , along with Salesforce.com 's rental price .
+  - No tumors were detected ; rather , empty cavities and scar tissue were found in
+    their place .
+  - A race observer sits in the passenger seat of the follow vehicle to record any
+    broken rules and also keep track of the car 's time .
+- source_sentence: Almihdhar and Alhazmi were aboard American Airlines Flight 77 ,
+    which crashed into the Pentagon .
+  sentences:
+  - Both have been identified as some of the hijackers who flew American Airlines
+    Flight 77 into the Pentagon .
+  - '" The Congress of the United States has never — ever — outlawed a medical procedure
+    , " said Rep. Louise McIntosh Slaughter ( D-N.Y. ) .'
+  - The agency wants him to return the illegal proceeds with interest and pay civil
+    monetary penalties .
+- source_sentence: He also called Hovan a " person of good reputation " who had worked
+    as a bus driver since 1967 .
+  sentences:
+  - The other victim , Michael Walker , of 550 Barbey Street , was struck in the neck
+    .
+  - The Fijian military installed an all-indigenous government led by Mr Qarase ,
+    who won democratic elections in September 2001 .
+  - Hovan , a resident of Trumbull , Conn . , had worked as a bus driver since 1967
+    and had no prior criminal record .
+- source_sentence: Had the creditors turned down the bailout plan , LG Group might
+    have been forced to close down its credit card business .
+  sentences:
+  - If the creditors turn down the bailout plan , LG Group may have to closedown its
+    card business .
+  - Passengers on that cruise are to be given a refund and voucher for another trip
+    .
+  - '" People who were on their way home , all of a sudden taken from us , " Mr. Bloomberg
+    said of the collision .'
+- source_sentence: '" Any decision on Charleroi will have huge implications for regional
+    airports in France , " he said .'
+  sentences:
+  - '" A bad decision on Charleroi would have huge implications for state-owned regional
+    airports in France .'
+  - Launched from space shuttle Atlantis ( news - web sites ) in 1989 , Galileo will
+    have traveled about 2.8 billion miles by the time it hits Jupiter .
+  - He said the ferry 's crew will be interviewed and tested for drugs and alcohol
+    .
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on microsoft/codebert-base
+
+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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) <!-- at revision 3b0952feddeffad0063f274080e3c23d75e7eb39 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Supported Modality:** Text
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### 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)
+
+### Full Model Architecture
+
+```
+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:
+
+```bash
+pip install -U sentence-transformers
+```
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# 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 .',
+    '" A bad decision on Charleroi would have huge implications for state-owned regional airports in France .',
+    "He said the ferry 's crew will be interviewed and tested for drugs and alcohol .",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# 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,
+    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",
+}
+```
+
+<!--
+## 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.*
+-->

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.16.1",
+  "type_vocab_size": 1,
+  "use_cache": false,
+  "vocab_size": 50265
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "pytorch": "2.11.0+cu128",
+    "sentence_transformers": "5.7.0",
+    "transformers": "5.16.1"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "cosine"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:73ed267e458fd7704f64f9447b43204a84f689cb3fa8a2936a2289fca33af540
+size 498604904

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}