---
language:
- tr
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:814596
- loss:MultipleNegativesRankingLoss
base_model: dbmdz/distilbert-base-turkish-cased
widget:
- source_sentence: Bir adam kitap okuyor.
  sentences:
  - Gözlüklü ve mavi gömlekli bir adam dizüstü bilgisayar ekranını okuyor.
  - Suyun içinde olduğunun farkındasın.
  - Plajda bir adam yüzüstü yatıp kitap okurken, puantiyeli bikinili bir kadın güneşleniyor.
- source_sentence: İki kişi parlak bir şekilde aydınlatılmış bir demiryolu geçidinin
    yanında duruyor.
  sentences:
  - Balık kesen bir adam
  - Uçakta bir hostes kahve servisi yapar.
  - Demiryolu raylarının yanında iki kişi duruyor.
- source_sentence: Ağzında beyaz bir frizbi olan siyah beyaz köpek için frizbi fırlatan
    beyaz gömlekli adam.
  sentences:
  - Hiçbir kardeşten bahsetmedi.
  - Adam ve köpek su altında.
  - Adam köpeğe frizbi atıyor
- source_sentence: Natüralist Sorgulamanın Mantığı.
  sentences:
  - İnsanlar otobüs bekliyor.
  - Natüralist Sorgulamayı anlamak zordur.
  - Natüralist Sorgulamanın anlaşılması kolaydır.
- source_sentence: İki kadın, Çin'deki bir markette bir ürüne bakıyor.
  sentences:
  - Kadınlar bir spor salonunda çalışıyorlar.
  - Müzenin en büyüleyici parçaları arasında San Macro'daki Geçit Töreni yer alıyor.
  - Alışveriş yapan iki kadın
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: distilbert-base-turkish-case trained on AllNLI Turkish translate triplets
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: all nli turkish dev
      type: all-nli-turkish-dev
    metrics:
    - type: cosine_accuracy
      value: 0.9801920038886863
      name: Cosine Accuracy
---

# distilbert-base-turkish-case trained on AllNLI Turkish translate triplets

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased). 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:** [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) <!-- at revision 8ecd4d034c2612d4c5940795b4f2552a9f3543d6 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** tr
- **License:** apache-2.0

### 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': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (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("orhanxakarsu/sentence-distilbert-turkish")
# Run inference
sentences = [
    "İki kadın, Çin'deki bir markette bir ürüne bakıyor.",
    'Alışveriş yapan iki kadın',
    'Kadınlar bir spor salonunda çalışıyorlar.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### 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

#### Triplet

* Dataset: `all-nli-turkish-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9802** |

<!--
## 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: 814,596 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                           | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                             | string                                                                            |
  | details | <ul><li>min: 3 tokens</li><li>mean: 18.16 tokens</li><li>max: 91 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.54 tokens</li><li>max: 136 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.73 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
  | anchor                                                                                                           | positive                                                             | negative                                                                                                                                           |
  |:-----------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Beyaz gömlekli ve güneş gözlüklü bir kadın, kucağında bir bebekle dışarıda bir sandalyede oturuyor.</code> | <code>Bebek yerden yukarıda oturuyor</code>                          | <code>Adam bir top atıyor</code>                                                                                                                   |
  | <code>Mavi yakalı gömlek giyen ve kazaklı bir adam ve beyaz gömlek giyen hasır şapka takan bir kadın.</code>     | <code>Yan yana bir erkek ve bir kadın var.</code>                    | <code>Evli bir çift akşam yemeği yiyor.</code>                                                                                                     |
  | <code>Adam içeride.</code>                                                                                       | <code>Siyah fötr şapkalı bir adam bir arenada boğaya biniyor.</code> | <code>Yeşil üniforma giyen beş subayla birlikte taş bir binanın önünde cep telefonuyla konuşan bir papaz; ikisi ayakta, diğerleri oturuyor.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 8,229 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 17.91 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.62 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.01 tokens</li><li>max: 33 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                                                  | positive                                                                      | negative                                                         |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------|:-----------------------------------------------------------------|
  | <code>Patlamanın büyüklüğünün güçlü bir örneği, Haragosha Tapınağı'nda bulunur, burada tapınağın kemerinin üst crosebar'ını görebilirsiniz, geri kalanı sertleşmiş lav tarafından batırılmıştır.</code> | <code>Patlamanın büyüklüğünün sonucu Haragosha Tapınağı'nda görülüyor.</code> | <code>Haragosha Tapınağı bu güne kadar tamamen sağlamdır.</code> |
  | <code>Arkeolojik kazı yapan iki kişi.</code>                                                                                                                                                            | <code>Kazı yapan insanlar var.</code>                                         | <code>Kimse kazmıyor.</code>                                     |
  | <code>İşçiler, Martins'in ünlü Louisiana sosis satıcısı çadırının önünde sıraya giren müşterilere hizmet veriyor</code>                                                                                 | <code>Müşteriler bir satıcı çadırının önünde sıraya giriyor.</code>           | <code>Pamuk şeker yiyen bir grup insan var.</code>               |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `fp16`: 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`: 64
- `per_device_eval_batch_size`: 64
- `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`: 2e-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`: 10
- `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
- `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`: True
- `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}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `dispatch_batches`: None
- `split_batches`: 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
- `eval_use_gather_object`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch  | Step   | Training Loss | Validation Loss | all-nli-turkish-dev_cosine_accuracy |
|:------:|:------:|:-------------:|:---------------:|:-----------------------------------:|
| 0      | 0      | -             | -               | 0.5808                              |
| 0.0786 | 1000   | 3.5327        | 1.9481          | 0.7607                              |
| 0.1571 | 2000   | 1.5833        | 1.2787          | 0.8260                              |
| 0.2357 | 3000   | 1.2338        | 1.0960          | 0.8533                              |
| 0.3142 | 4000   | 1.1031        | 0.9897          | 0.8695                              |
| 0.3928 | 5000   | 0.998         | 0.9077          | 0.8793                              |
| 0.4714 | 6000   | 0.9412        | 0.8434          | 0.8914                              |
| 0.5499 | 7000   | 0.8703        | 0.7904          | 0.8982                              |
| 0.6285 | 8000   | 0.8094        | 0.7311          | 0.9068                              |
| 0.7070 | 9000   | 0.7653        | 0.6894          | 0.9086                              |
| 0.7856 | 10000  | 0.7248        | 0.6509          | 0.9162                              |
| 0.8642 | 11000  | 0.673         | 0.6145          | 0.9205                              |
| 0.9427 | 12000  | 0.6514        | 0.5762          | 0.9273                              |
| 1.0213 | 13000  | 0.6259        | 0.5463          | 0.9334                              |
| 1.0999 | 14000  | 0.5874        | 0.5276          | 0.9332                              |
| 1.1784 | 15000  | 0.5518        | 0.5053          | 0.9366                              |
| 1.2570 | 16000  | 0.5277        | 0.4783          | 0.9391                              |
| 1.3355 | 17000  | 0.5075        | 0.4571          | 0.9419                              |
| 1.4141 | 18000  | 0.4906        | 0.4379          | 0.9454                              |
| 1.4927 | 19000  | 0.475         | 0.4234          | 0.9465                              |
| 1.5712 | 20000  | 0.447         | 0.4046          | 0.9499                              |
| 1.6498 | 21000  | 0.4307        | 0.3908          | 0.9508                              |
| 1.7283 | 22000  | 0.4126        | 0.3773          | 0.9548                              |
| 1.8069 | 23000  | 0.3985        | 0.3654          | 0.9564                              |
| 1.8855 | 24000  | 0.3748        | 0.3582          | 0.9560                              |
| 1.9640 | 25000  | 0.3675        | 0.3449          | 0.9581                              |
| 2.0426 | 26000  | 0.3545        | 0.3390          | 0.9586                              |
| 2.1211 | 27000  | 0.3456        | 0.3335          | 0.9595                              |
| 2.1997 | 28000  | 0.3295        | 0.3255          | 0.9626                              |
| 2.2783 | 29000  | 0.3198        | 0.3146          | 0.9624                              |
| 2.3568 | 30000  | 0.3107        | 0.3101          | 0.9642                              |
| 2.4354 | 31000  | 0.3139        | 0.3014          | 0.9665                              |
| 2.5139 | 32000  | 0.2982        | 0.3005          | 0.9659                              |
| 2.5925 | 33000  | 0.2903        | 0.2891          | 0.9663                              |
| 2.6711 | 34000  | 0.2778        | 0.2859          | 0.9662                              |
| 2.7496 | 35000  | 0.2731        | 0.2812          | 0.9667                              |
| 2.8282 | 36000  | 0.2613        | 0.2757          | 0.9677                              |
| 2.9067 | 37000  | 0.2566        | 0.2680          | 0.9689                              |
| 2.9853 | 38000  | 0.2488        | 0.2674          | 0.9699                              |
| 3.0639 | 39000  | 0.2434        | 0.2594          | 0.9694                              |
| 3.1424 | 40000  | 0.2375        | 0.2574          | 0.9705                              |
| 3.2210 | 41000  | 0.2295        | 0.2553          | 0.9706                              |
| 3.2996 | 42000  | 0.223         | 0.2501          | 0.9703                              |
| 3.3781 | 43000  | 0.2209        | 0.2455          | 0.9719                              |
| 3.4567 | 44000  | 0.2211        | 0.2409          | 0.9711                              |
| 3.5352 | 45000  | 0.2097        | 0.2396          | 0.9728                              |
| 3.6138 | 46000  | 0.2068        | 0.2345          | 0.9734                              |
| 3.6924 | 47000  | 0.1994        | 0.2298          | 0.9731                              |
| 3.7709 | 48000  | 0.1986        | 0.2299          | 0.9730                              |
| 3.8495 | 49000  | 0.1878        | 0.2271          | 0.9728                              |
| 3.9280 | 50000  | 0.1872        | 0.2244          | 0.9739                              |
| 4.0066 | 51000  | 0.1821        | 0.2249          | 0.9734                              |
| 4.0852 | 52000  | 0.1823        | 0.2188          | 0.9739                              |
| 4.1637 | 53000  | 0.1736        | 0.2176          | 0.9748                              |
| 4.2423 | 54000  | 0.1691        | 0.2152          | 0.9745                              |
| 4.3208 | 55000  | 0.1665        | 0.2148          | 0.9753                              |
| 4.3994 | 56000  | 0.1663        | 0.2133          | 0.9748                              |
| 4.4780 | 57000  | 0.1666        | 0.2123          | 0.9755                              |
| 4.5565 | 58000  | 0.1589        | 0.2082          | 0.9758                              |
| 4.6351 | 59000  | 0.155         | 0.2053          | 0.9762                              |
| 4.7136 | 60000  | 0.155         | 0.2037          | 0.9762                              |
| 4.7922 | 61000  | 0.1536        | 0.2031          | 0.9764                              |
| 4.8708 | 62000  | 0.1443        | 0.2020          | 0.9759                              |
| 4.9493 | 63000  | 0.146         | 0.1999          | 0.9752                              |
| 5.0279 | 64000  | 0.1417        | 0.1969          | 0.9764                              |
| 5.1064 | 65000  | 0.1407        | 0.1966          | 0.9761                              |
| 5.1850 | 66000  | 0.1342        | 0.1981          | 0.9757                              |
| 5.2636 | 67000  | 0.1342        | 0.1933          | 0.9768                              |
| 5.3421 | 68000  | 0.1312        | 0.1944          | 0.9758                              |
| 5.4207 | 69000  | 0.1329        | 0.1932          | 0.9772                              |
| 5.4993 | 70000  | 0.1304        | 0.1908          | 0.9768                              |
| 5.5778 | 71000  | 0.1247        | 0.1880          | 0.9772                              |
| 5.6564 | 72000  | 0.1221        | 0.1861          | 0.9779                              |
| 5.7349 | 73000  | 0.1225        | 0.1831          | 0.9784                              |
| 5.8135 | 74000  | 0.1205        | 0.1854          | 0.9790                              |
| 5.8921 | 75000  | 0.1152        | 0.1815          | 0.9789                              |
| 5.9706 | 76000  | 0.1161        | 0.1827          | 0.9782                              |
| 6.0492 | 77000  | 0.1151        | 0.1819          | 0.9781                              |
| 6.1277 | 78000  | 0.113         | 0.1818          | 0.9780                              |
| 6.2063 | 79000  | 0.1102        | 0.1823          | 0.9784                              |
| 6.2849 | 80000  | 0.1067        | 0.1798          | 0.9780                              |
| 6.3634 | 81000  | 0.1067        | 0.1782          | 0.9790                              |
| 6.4420 | 82000  | 0.1116        | 0.1779          | 0.9782                              |
| 6.5205 | 83000  | 0.107         | 0.1752          | 0.9782                              |
| 6.5991 | 84000  | 0.1039        | 0.1739          | 0.9792                              |
| 6.6777 | 85000  | 0.1013        | 0.1728          | 0.9789                              |
| 6.7562 | 86000  | 0.1029        | 0.1713          | 0.9786                              |
| 6.8348 | 87000  | 0.0972        | 0.1721          | 0.9791                              |
| 6.9133 | 88000  | 0.0991        | 0.1703          | 0.9790                              |
| 6.9919 | 89000  | 0.0955        | 0.1708          | 0.9791                              |
| 7.0705 | 90000  | 0.097         | 0.1715          | 0.9786                              |
| 7.1490 | 91000  | 0.0941        | 0.1716          | 0.9793                              |
| 7.2276 | 92000  | 0.0922        | 0.1712          | 0.9795                              |
| 7.3062 | 93000  | 0.0921        | 0.1706          | 0.9789                              |
| 7.3847 | 94000  | 0.091         | 0.1691          | 0.9793                              |
| 7.4633 | 95000  | 0.0942        | 0.1689          | 0.9787                              |
| 7.5418 | 96000  | 0.0905        | 0.1678          | 0.9790                              |
| 7.6204 | 97000  | 0.0871        | 0.1664          | 0.9792                              |
| 7.6990 | 98000  | 0.0859        | 0.1666          | 0.9793                              |
| 7.7775 | 99000  | 0.0876        | 0.1656          | 0.9785                              |
| 7.8561 | 100000 | 0.084         | 0.1643          | 0.9795                              |
| 7.9346 | 101000 | 0.0853        | 0.1654          | 0.9795                              |
| 8.0132 | 102000 | 0.083         | 0.1640          | 0.9789                              |
| 8.0918 | 103000 | 0.0849        | 0.1637          | 0.9795                              |
| 8.1703 | 104000 | 0.0816        | 0.1626          | 0.9797                              |
| 8.2489 | 105000 | 0.0803        | 0.1627          | 0.9796                              |
| 8.3274 | 106000 | 0.0802        | 0.1623          | 0.9796                              |
| 8.4060 | 107000 | 0.0808        | 0.1622          | 0.9798                              |
| 8.4846 | 108000 | 0.0836        | 0.1632          | 0.9792                              |
| 8.5631 | 109000 | 0.0791        | 0.1612          | 0.9796                              |
| 8.6417 | 110000 | 0.0761        | 0.1609          | 0.9798                              |
| 8.7202 | 111000 | 0.0782        | 0.1604          | 0.9797                              |
| 8.7988 | 112000 | 0.0784        | 0.1604          | 0.9803                              |
| 8.8774 | 113000 | 0.0737        | 0.1600          | 0.9804                              |
| 8.9559 | 114000 | 0.0762        | 0.1602          | 0.9799                              |
| 9.0345 | 115000 | 0.0764        | 0.1597          | 0.9802                              |
| 9.1130 | 116000 | 0.0761        | 0.1600          | 0.9799                              |
| 9.1916 | 117000 | 0.0729        | 0.1592          | 0.9797                              |
| 9.2702 | 118000 | 0.0728        | 0.1595          | 0.9803                              |
| 9.3487 | 119000 | 0.0722        | 0.1590          | 0.9798                              |
| 9.4273 | 120000 | 0.0745        | 0.1591          | 0.9797                              |
| 9.5059 | 121000 | 0.0741        | 0.1591          | 0.9798                              |
| 9.5844 | 122000 | 0.0715        | 0.1587          | 0.9797                              |
| 9.6630 | 123000 | 0.0719        | 0.1581          | 0.9799                              |
| 9.7415 | 124000 | 0.0716        | 0.1578          | 0.9799                              |
| 9.8201 | 125000 | 0.0714        | 0.1582          | 0.9801                              |
| 9.8987 | 126000 | 0.0712        | 0.1579          | 0.9803                              |
| 9.9772 | 127000 | 0.0707        | 0.1581          | 0.9802                              |

</details>

### Framework Versions
- Python: 3.12.4
- Sentence Transformers: 3.3.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu124
- Accelerate: 0.33.0
- Datasets: 3.1.0
- Tokenizers: 0.19.1

## 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",
}
```

#### 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}
}
```

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