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1_Pooling/config.json

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+{
+    "word_embedding_dimension": 768,
+    "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
+}

README.md

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+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:1174
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Sodišče sme na prošnjo obsojenca odločiti, da se obsodba izbriše
+    iz kazenske evidence in da obsojenec velja za neobsojenega, če je potekla polovica
+    z zakonom določenega roka, po poteku katerega se obsodba izbriše, če obsojenec
+    v tem času ni storil novega kaznivega dejanja. Pri odločanju o izbrisu upošteva
+    sodišče vedenje obsojenca po prestani kazni, naravo kaznivega dejanja in druge
+    okoliščine, pomembne za izbris obsodbe.
+  sentences:
+  - Če dan izročitve stvari kupcu ni določen, mora prodajalec izročiti stvar v roku
+    15 dni po sklenitvi pogodbe, glede na naravo stvari in na druge okoliščine.
+  - 'Upravljalci, ki so subjekti javnega sektorja, za namene raziskovanja posredujejo
+    osebne podatke po tarifi, določeni za raziskovalne storitve. '
+  - Sodišče po uradni dolžnosti izbriše obsodbo iz kazenske evidence, če storilec
+    že dalj časa ni izvršil kaznivega dejanja, pri tem pa prav tako upošteva vedenje
+    obsojenca po prestani kazni, naravo kaznivega dejanja in druge okoliščine, pomembne
+    za izbris obsodbe.
+- source_sentence: 'V okviru trženja ali podobne druge poslovne dejavnosti se ne smejo
+    zahtevati,
+
+    pridobiti ali nadalje obdelovati biometrični osebni podatki v zamenjavo za določene
+    storitve,
+
+    četudi so te storitve za posameznika, na katerega se nanašajo osebni podatki,
+    brezplačne.'
+  sentences:
+  - 'Upravljavec videonadzora ni dolžan obvestiti policije, če posnetek pokaže nevaren
+    dogodek.
+
+    '
+  - Za obdelave osebnih podatkov na področju varnosti države pristojni organ s področja
+    varnosti države pripravi oceno učinka s smiselno uporabo določb tega člena. Ocena
+    učinka je za potrebe nadzorov javno dostopna.
+  - Osebe zasebnega prava lahko zahtevajo, pridobivajo ali nadalje obdelujejo biometrične
+    osebne v zameno za določene storitve in blago, pod pogojem, da se storitve izvajajo
+    za posameznika brezplačno.
+- source_sentence: (3) Prepoved odtujitve ali obremenitve se lahko časovno omeji.
+  sentences:
+  - "Z ustanovitvijo hipoteke na stavbni pravici upnik hkrati \npridobi tudi zastavno\
+    \ pravico na terjatvi imetnika stavbnih\npravic za plačilo nadomestila."
+  - V postopkih pred nadzornim organom ni dopustna stranska udeležba, kot jo določa
+    zakon, ki ureja splošni civilni postopek.
+  - 'Prepoved odtujitve mora biti časovno omejena. '
+- source_sentence: (1) Več oseb ima solastninsko pravico na nerazdeljeni stvari (solastniki),
+    če je delež vsakega izmed njih določen v sorazmerju s celoto (idealni delež).
+  sentences:
+  - Javni shodi so brezpogojno dovoljeni in vsakršna njihova prepoved ali preprečitev
+    se kaznuje.
+  - 'Posnetki videonadzora morajo se hranijo trajno, če kapacitete hranilnika tega
+    ne dopuščajo, pa se hranijo eno leto in se po tem izbrišejo. '
+  - 'Več oseb ima solastninsko pravico na nerazdeljeni stvari (solastniki), vendar
+    le če je delež vsakega izmed njih nedoločen vse do delitve. '
+- source_sentence: '(1) Posest je neposredna dejanska oblast nad stvarjo (neposredna
+    posest).
+
+    (2) Posest ima tudi tisti, ki izvršuje dejansko oblast nad stvarjo prek koga drugega,
+    ki
+
+    ima neposredno posest iz kakršnegakoli pravnega naslova (posredna posest).'
+  sentences:
+  - V postopkih pred nadzornim organom ni dopustna stranska udeležba, kot jo določa
+    zakon, ki ureja splošni civilni postopek.
+  - Posameznik lahko isto stvar istočasno poseduje neposredno in posredno.
+  - Sodišče nikoli ne more odločati o poslu, ki presega redno upravljanje, brez soglasja
+    vseh solastnikov.
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- pearson_cosine
+- spearman_cosine
+model-index:
+- name: SentenceTransformer
+  results:
+  - task:
+      type: semantic-similarity
+      name: Semantic Similarity
+    dataset:
+      name: validation dev
+      type: validation-dev
+    metrics:
+    - type: pearson_cosine
+      value: 0.07191992786363605
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.019280573682247046
+      name: Spearman Cosine
+---
+
+# SentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-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:** [Unknown](https://huggingface.co/unknown) -->
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 768 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/huggingface/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': 8192, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
+  (1): Pooling({'word_embedding_dimension': 768, '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("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    '(1) Posest je neposredna dejanska oblast nad stvarjo (neposredna posest).\n(2) Posest ima tudi tisti, ki izvršuje dejansko oblast nad stvarjo prek koga drugega, ki\nima neposredno posest iz kakršnegakoli pravnega naslova (posredna posest).',
+    'Posameznik lahko isto stvar istočasno poseduje neposredno in posredno.',
+    'Sodišče nikoli ne more odločati o poslu, ki presega redno upravljanje, brez soglasja vseh solastnikov.',
+]
+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, 0.5774, 0.4022],
+#         [0.5774, 1.0000, 0.2455],
+#         [0.4022, 0.2455, 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.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Semantic Similarity
+
+* Dataset: `validation-dev`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.0719     |
+| **spearman_cosine** | **0.0193** |
+
+<!--
+## 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: 1,174 training samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor                                                                               | positive                                                                            | label                                                         |
+  |:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                               | string                                                                              | float                                                         |
+  | details | <ul><li>min: 2 tokens</li><li>mean: 152.19 tokens</li><li>max: 1336 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 63.45 tokens</li><li>max: 304 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | positive                                                                                                                                   | label            |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>(1) Vsaka stranka v dvostranski pogodbi lahko prenese pogodbo nekomu tretjemu,<br>ki postane s tem imetnik vseh njenih pravic in obveznosti iz te pogodbe, če v to privoli druga stranka.<br>(2) S prenosom pogodbe preide pogodbeno razmerje med prenositeljem in drugo<br>stranko na prevzemnika in drugo stranko takrat, ko druga stranka privoli v prenos; če je dala svojo privolitev vnaprej, pa takrat, ko je o prenosu obveščena.<br>(3) Privolitev v prenos pogodbe je veljavna samo, če je dana v obliki, ki jo predpisuje<br>zakon za sklenitev prenesene pogodbe.<br>(4) Določbe o stranskih pravicah v zvezi s pogodbo o prevzemu dolga se smiselno<br>uporabljajo tudi za prenos pogodbe.</code>                                                                                             | <code>S prenosom pogodbe preide pogodbeno razmerje, pri čemer<br>prenositelj jamči, da bo druga stranka izpolnila svoje obveznosti.</code> | <code>1.0</code> |
+  | <code>(1) Vsaka stranka v dvostranski pogodbi lahko prenese pogodbo nekomu tretjemu,<br>ki postane s tem imetnik vseh njenih pravic in obveznosti iz te pogodbe, če v to privoli druga stranka.<br>(2) S prenosom pogodbe preide pogodbeno razmerje med prenositeljem in drugo<br>stranko na prevzemnika in drugo stranko takrat, ko druga stranka privoli v prenos; če je dala svojo privolitev vnaprej, pa takrat, ko je o prenosu obveščena.<br>(3) Privolitev v prenos pogodbe je veljavna samo, če je dana v obliki, ki jo predpisuje<br>zakon za sklenitev prenesene pogodbe.<br>(4) Določbe o stranskih pravicah v zvezi s pogodbo o prevzemu dolga se smiselno<br>uporabljajo tudi za prenos pogodbe.</code>                                                                                             | <code>S prenosom pogodbe preide pogodbeno razmerje, pri čemer<br>prenositelj jamči, da bo druga stranka izpolnila svoje obveznosti.</code> | <code>1.0</code> |
+  | <code>(1) Za škodo, ki jo povzroči delavec pri delu ali v zvezi z delom tretji osebi, odgovarja pravna ali fizična oseba, pri kateri je delavec delal takrat, ko je bila škoda povzročena, razen če dokaže, da je delavec v danih okoliščinah ravnal tako, kot je bilo treba.<br>(2) Oškodovanec ima pravico zahtevati povrnitev škode tudi neposredno od delavca, če je ta škodo povzročil namenoma.<br>(3) Kdor je oškodovancu povrnil škodo, ki jo je povzročil delavec namenoma ali iz hude malomarnosti, ima pravico zahtevati od delavca povrnitev plačanega zneska.<br>(4) Ta pravica zastara v šestih mesecih od dneva, ko je bila odškodnina plačana.<br>(5) Določba prvega odstavka tega člena ne posega v pravila o odgovornosti za škodo, ki izvira od nevarne stvari ali nevarne dejavnosti.</code> | <code>Oškodovanec lahko vedno zahteva odškonino tako od delodajalca kot od delavca, njuna odgovornost je solidarna.</code>                 | <code>1.0</code> |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768
+      ],
+      "matryoshka_weights": [
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 62 evaluation samples
+* Columns: <code>anchor</code>, <code>positive</code>, and <code>label</code>
+* Approximate statistics based on the first 62 samples:
+  |         | anchor                                                                              | positive                                                                            | label                                                         |
+  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                              | float                                                         |
+  | details | <ul><li>min: 2 tokens</li><li>mean: 105.61 tokens</li><li>max: 313 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 63.13 tokens</li><li>max: 187 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 1.0</li><li>max: 1.0</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                                                           | positive                                                                                                    | label            |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:-----------------|
+  | <code>(1) Posest je neposredna dejanska oblast nad stvarjo (neposredna posest).<br>(2) Posest ima tudi tisti, ki izvršuje dejansko oblast nad stvarjo prek koga drugega, ki<br>ima neposredno posest iz kakršnegakoli pravnega naslova (posredna posest).</code> | <code>Posameznik lahko isto stvar istočasno poseduje neposredno in posredno.</code>                         | <code>1.0</code> |
+  | <code>(1) Posest je neposredna dejanska oblast nad stvarjo (neposredna posest).<br>(2) Posest ima tudi tisti, ki izvršuje dejansko oblast nad stvarjo prek koga drugega, ki<br>ima neposredno posest iz kakršnegakoli pravnega naslova (posredna posest).</code> | <code>Posameznik lahko isto stvar istočasno poseduje neposredno in posredno.</code>                         | <code>1.0</code> |
+  | <code>Upravljavec videonadzornega sistema, ki izvaja videonadzor javnih površin, mora v primeru, ko videonadzorni sistem posname dogodek, ki ogroža zdravje ali življenje posameznika, o tem nemudoma obvestiti policijo ali drug pristojni subjekt.</code>      | <code>Upravljavec videonadzora ni dolžan obvestiti policije, če posnetek pokaže nevaren dogodek.<br></code> | <code>1.0</code> |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768
+      ],
+      "matryoshka_weights": [
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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
+- `use_cpu`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `bf16`: True
+- `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
+- `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_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `optim_args`: None
+- `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_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `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`: no
+- `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`: True
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | validation-dev_spearman_cosine |
+|:------:|:----:|:-------------:|:---------------:|:------------------------------:|
+| 0.3378 | 25   | -             | 0.3447          | 0.0390                         |
+| 0.6757 | 50   | -             | 0.2570          | -0.0466                        |
+| 1.0135 | 75   | -             | 0.2282          | -0.0269                        |
+| 1.3514 | 100  | 0.3073        | 0.1797          | 0.0677                         |
+| 1.6892 | 125  | -             | 0.2085          | 0.0184                         |
+| 2.0270 | 150  | -             | 0.1725          | 0.0479                         |
+| 2.3649 | 175  | -             | 0.1636          | 0.0183                         |
+| 2.7027 | 200  | 0.0371        | 0.1707          | 0.0193                         |
+
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 5.1.2
+- Transformers: 4.57.0.dev0
+- PyTorch: 2.9.0+cu128
+- Accelerate: 1.10.1
+- Datasets: 4.1.1
+- Tokenizers: 0.22.1
+
+## 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",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## 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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+{
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+  "layer_norm_eps": 1e-05,
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+  "transformers_version": "4.57.0.dev0",
+  "vocab_size": 50368
+}

config_sentence_transformers.json

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+{
+  "model_type": "SentenceTransformer",
+  "__version__": {
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+    "transformers": "4.57.0.dev0",
+    "pytorch": "2.9.0+cu128"
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+}

model.safetensors

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+oid sha256:c55d876ee0250a9c042665840a48c6ce38776b12dac5e85085e341d12702fbf4
+size 596070136

modules.json

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+[
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+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
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sentence_bert_config.json

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+}

special_tokens_map.json

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