| | --- |
| | tags: |
| | - sentence-transformers |
| | - sentence-similarity |
| | - feature-extraction |
| | - dense |
| | - generated_from_trainer |
| | - dataset_size:13576 |
| | - loss:CosineSimilarityLoss |
| | base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| | widget: |
| | - source_sentence: işlemler bu maddenin birinci fıkrasındaki hükümlere göre faturalandırılır. |
| | Ancak “yatarak tedavi” kapsamında hizmet başına |
| | sentences: |
| | - b) B Grubu tanıya dayalı işlemlerde; 10 gün |
| | - ödeme yöntemi ile bir işlem yapılması durumunda SUT eki EK -2/A Listesinde yer |
| | alan tutarlar faturalandırılmayacak olup |
| | - ve “Yurt dışı Provizyon Aktivasyon ve Sağlık Sistemi (YUPASS)” numarası ile hasta |
| | takip numarası/provizyon alınan kişilere |
| | - source_sentence: 4.2.13.3.2.A.1- Daha önce Kronik Hepatit C tedavisi almamış hastalarda |
| | tedavi |
| | sentences: |
| | - inhibitörü kullanılmaz. |
| | - (1) Nonsirotik hastalarda; tedavi süresi (Sofosbuvir+Velpatasvir+Voxilaprevir) |
| | ile toplam 8 hafta ya da |
| | - (1) SUT eki listelerde yer alan tıbbi malzemelerin temin edilmesi halinde, bu |
| | listelerdeki birim fiyatlar, sağlık hizmeti |
| | - source_sentence: immünoglobulinlere dirençli ve splenektominin kontrendike olduğu/yapılamadığı |
| | ya da splenektomi sonrası nüks eden |
| | sentences: |
| | - durumlarda, 1 yaşından itibaren trombosit sayısı 30.000’in altında olan kanamalı |
| | kronik immün trombositopenik purpura |
| | - (2)Tioguanin, tiotepa, bortezomib, talidomid, kladribin, anagrelid, i darubisin, |
| | pentostatin,fludarabin, tretinoin, |
| | - (3) Sağlık Kurulu raporu ile belirlenen ilaç dozları için SUT’un 4.2.42.C maddesinde |
| | yer alan hükümler geçerlidir. |
| | - source_sentence: 2) İTT tedavisi esnasında akut kanaması ve/veya cerrahi girişim |
| | gerekli olan hastalarda mevcut bypass edici ajanlar |
| | sentences: |
| | - 2) Nükseden veya kemorezistan CD20 pozitif foliküler lenfoma, diffüz büyük B hücreli |
| | lenfoma, mantle hücreli |
| | - ile SUT hükümleri doğrultusunda kanama tedavisi uygulanabilir ve aynı zamanda |
| | İTT tedavisi de sürdürülür. Bu tedaviler |
| | - sahip olan metastatik prostat kanserl i hastalarda progresyona kadar prednizolon |
| | ile kombine olarak kullanılması halinde |
| | - source_sentence: tamamlanmaksızın da idame tedavilere geçilebilecektir. |
| | sentences: |
| | - (10) Deksametazon intravitreal implant etkin maddeli ilacın, anti-VEGF ilaçların |
| | uygulamasını takiben en erken 1 |
| | - durumun belirtildiği 3 ay süreli sağlık kurulu raporuna dayanılarak ilaca başlanabilir. |
| | İlaca başlandıktan 3 ay sonra yapılan |
| | - bankaları aracılığı ile yapılan kemik iliği/kordon kanı tarama ve teminine ilişkin |
| | fatura bedelleri yukarıdaki hükümler |
| | pipeline_tag: sentence-similarity |
| | library_name: sentence-transformers |
| | --- |
| | |
| | # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| |
|
| | This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
| |
|
| | ## Model Details |
| |
|
| | ### Model Description |
| | - **Model Type:** Sentence Transformer |
| | - **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d --> |
| | - **Maximum Sequence Length:** 128 tokens |
| | - **Output Dimensionality:** 384 dimensions |
| | - **Similarity Function:** Cosine Similarity |
| | <!-- - **Training Dataset:** Unknown --> |
| | <!-- - **Language:** Unknown --> |
| | <!-- - **License:** Unknown --> |
| |
|
| | ### Model Sources |
| |
|
| | - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| | - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| | - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
| |
|
| | ### Full Model Architecture |
| |
|
| | ``` |
| | SentenceTransformer( |
| | (0): Transformer({'max_seq_length': 128, 'do_lower_case': False, 'architecture': 'BertModel'}) |
| | (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, '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("Erol35/sut-embed-model") |
| | # Run inference |
| | sentences = [ |
| | 'tamamlanmaksızın da idame tedavilere geçilebilecektir.', |
| | '(10) Deksametazon intravitreal implant etkin maddeli ilacın, anti-VEGF ilaçların uygulamasını takiben en erken 1', |
| | 'bankaları aracılığı ile yapılan kemik iliği/kordon kanı tarama ve teminine ilişkin fatura bedelleri yukarıdaki hükümler', |
| | ] |
| | embeddings = model.encode(sentences) |
| | print(embeddings.shape) |
| | # [3, 384] |
| | |
| | # Get the similarity scores for the embeddings |
| | similarities = model.similarity(embeddings, embeddings) |
| | print(similarities) |
| | # tensor([[1.0000, 0.8021, 0.4142], |
| | # [0.8021, 1.0000, 0.3381], |
| | # [0.4142, 0.3381, 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: 13,576 training samples |
| | * Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
| | * Approximate statistics based on the first 1000 samples: |
| | | | sentence_0 | sentence_1 | label | |
| | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | | type | string | string | float | |
| | | details | <ul><li>min: 3 tokens</li><li>mean: 24.5 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 24.11 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.63</li><li>max: 1.0</li></ul> | |
| | * Samples: |
| | | sentence_0 | sentence_1 | label | |
| | |:------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------|:-----------------| |
| | | <code>süre) hastalarda yeniden başlangıç kriterleri aranır.</code> | <code>4.2.1.C-14 – Bimekizumab</code> | <code>1.0</code> | |
| | | <code>hekimleri tarafından düzenlenen en fazla 6 ay süreli uzman hekim raporuna dayanılarak başlanır. Bu sürenin sonunda; yukarıda</code> | <code>belirtilen malnütrisyon koşullarının devam etmesi durumunda çocuk gastroenteroloji, çocuk nöroloji, çocuk metabolizma ,</code> | <code>1.0</code> | |
| | | <code>(3) Bu durumların belirtildiği üçüncü basamak hastanelerde hematoloji uzman hekiminin yer aldığı üç ay süreli sağlık</code> | <code>kurulu raporuna dayanılarak hematoloji uzman hekimlerince reçete edilir. Her doz değişikliğinde trombosit sayısı raporun</code> | <code>1.0</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" |
| | } |
| | ``` |
| |
|
| | ### Training Hyperparameters |
| | #### Non-Default Hyperparameters |
| |
|
| | - `per_device_train_batch_size`: 16 |
| | - `per_device_eval_batch_size`: 16 |
| | - `num_train_epochs`: 1 |
| | - `multi_dataset_batch_sampler`: round_robin |
| | |
| | #### All Hyperparameters |
| | <details><summary>Click to expand</summary> |
| | |
| | - `overwrite_output_dir`: False |
| | - `do_predict`: False |
| | - `eval_strategy`: no |
| | - `prediction_loss_only`: True |
| | - `per_device_train_batch_size`: 16 |
| | - `per_device_eval_batch_size`: 16 |
| | - `per_gpu_train_batch_size`: None |
| | - `per_gpu_eval_batch_size`: None |
| | - `gradient_accumulation_steps`: 1 |
| | - `eval_accumulation_steps`: None |
| | - `torch_empty_cache_steps`: None |
| | - `learning_rate`: 5e-05 |
| | - `weight_decay`: 0.0 |
| | - `adam_beta1`: 0.9 |
| | - `adam_beta2`: 0.999 |
| | - `adam_epsilon`: 1e-08 |
| | - `max_grad_norm`: 1 |
| | - `num_train_epochs`: 1 |
| | - `max_steps`: -1 |
| | - `lr_scheduler_type`: linear |
| | - `lr_scheduler_kwargs`: {} |
| | - `warmup_ratio`: 0.0 |
| | - `warmup_steps`: 0 |
| | - `log_level`: passive |
| | - `log_level_replica`: warning |
| | - `log_on_each_node`: True |
| | - `logging_nan_inf_filter`: True |
| | - `save_safetensors`: True |
| | - `save_on_each_node`: False |
| | - `save_only_model`: False |
| | - `restore_callback_states_from_checkpoint`: False |
| | - `no_cuda`: False |
| | - `use_cpu`: False |
| | - `use_mps_device`: False |
| | - `seed`: 42 |
| | - `data_seed`: None |
| | - `jit_mode_eval`: False |
| | - `use_ipex`: False |
| | - `bf16`: False |
| | - `fp16`: False |
| | - `fp16_opt_level`: O1 |
| | - `half_precision_backend`: auto |
| | - `bf16_full_eval`: False |
| | - `fp16_full_eval`: False |
| | - `tf32`: None |
| | - `local_rank`: 0 |
| | - `ddp_backend`: None |
| | - `tpu_num_cores`: None |
| | - `tpu_metrics_debug`: False |
| | - `debug`: [] |
| | - `dataloader_drop_last`: False |
| | - `dataloader_num_workers`: 0 |
| | - `dataloader_prefetch_factor`: None |
| | - `past_index`: -1 |
| | - `disable_tqdm`: False |
| | - `remove_unused_columns`: True |
| | - `label_names`: None |
| | - `load_best_model_at_end`: False |
| | - `ignore_data_skip`: False |
| | - `fsdp`: [] |
| | - `fsdp_min_num_params`: 0 |
| | - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| | - `fsdp_transformer_layer_cls_to_wrap`: None |
| | - `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 |
| | - `deepspeed`: None |
| | - `label_smoothing_factor`: 0.0 |
| | - `optim`: adamw_torch_fused |
| | - `optim_args`: None |
| | - `adafactor`: False |
| | - `group_by_length`: False |
| | - `length_column_name`: length |
| | - `ddp_find_unused_parameters`: None |
| | - `ddp_bucket_cap_mb`: None |
| | - `ddp_broadcast_buffers`: False |
| | - `dataloader_pin_memory`: True |
| | - `dataloader_persistent_workers`: False |
| | - `skip_memory_metrics`: True |
| | - `use_legacy_prediction_loop`: False |
| | - `push_to_hub`: False |
| | - `resume_from_checkpoint`: None |
| | - `hub_model_id`: None |
| | - `hub_strategy`: every_save |
| | - `hub_private_repo`: None |
| | - `hub_always_push`: False |
| | - `hub_revision`: None |
| | - `gradient_checkpointing`: False |
| | - `gradient_checkpointing_kwargs`: None |
| | - `include_inputs_for_metrics`: False |
| | - `include_for_metrics`: [] |
| | - `eval_do_concat_batches`: True |
| | - `fp16_backend`: auto |
| | - `push_to_hub_model_id`: None |
| | - `push_to_hub_organization`: None |
| | - `mp_parameters`: |
| | - `auto_find_batch_size`: False |
| | - `full_determinism`: False |
| | - `torchdynamo`: None |
| | - `ray_scope`: last |
| | - `ddp_timeout`: 1800 |
| | - `torch_compile`: False |
| | - `torch_compile_backend`: None |
| | - `torch_compile_mode`: None |
| | - `include_tokens_per_second`: False |
| | - `include_num_input_tokens_seen`: False |
| | - `neftune_noise_alpha`: None |
| | - `optim_target_modules`: None |
| | - `batch_eval_metrics`: False |
| | - `eval_on_start`: False |
| | - `use_liger_kernel`: False |
| | - `liger_kernel_config`: None |
| | - `eval_use_gather_object`: False |
| | - `average_tokens_across_devices`: False |
| | - `prompts`: None |
| | - `batch_sampler`: batch_sampler |
| | - `multi_dataset_batch_sampler`: round_robin |
| | - `router_mapping`: {} |
| | - `learning_rate_mapping`: {} |
| |
|
| | </details> |
| |
|
| | ### Training Logs |
| | | Epoch | Step | Training Loss | |
| | |:------:|:----:|:-------------:| |
| | | 0.5889 | 500 | 0.1882 | |
| |
|
| |
|
| | ### Framework Versions |
| | - Python: 3.12.11 |
| | - Sentence Transformers: 5.1.0 |
| | - Transformers: 4.56.2 |
| | - PyTorch: 2.8.0+cu126 |
| | - Accelerate: 1.10.1 |
| | - Datasets: 4.1.1 |
| | - Tokenizers: 0.22.0 |
| |
|
| | ## 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 |
| |
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| | *Clearly define terms in order to be accessible across audiences.* |
| | --> |
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| | ## Model Card Authors |
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| | *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| | --> |
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| | ## Model Card Contact |
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| | *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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