---
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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