Add new SentenceTransformer model

628cfaa verified 18 days ago

30 kB

	---
	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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