allstats-semantic-search-model-v1 / README.md

Add new SentenceTransformer model

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	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:212917
	- loss:CosineSimilarityLoss
	base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
	widget:
	- source_sentence: statistik neraca arus dana indonesia
	sentences:
	- Statistik Kelapa Sawit Indonesia 2012
	- Distribusi Perdagangan Komoditas Kedelai Indonesia 2023
	- Data Runtun Statistik Konstruksi 1990-2010
	- source_sentence: Seberapa besar kenaikan produksi IBS pada Triwulan IV Tahun 2013
	dibandingkan Triwulan IV Tahun Sebelumnya?
	sentences:
	- Pertumbuhan PDB 2013 Mencapai 5,78 Persen
	- Statistik Komuter Gerbangkertosusila Hasil Survei Komuter Gerbangkertosusila 2017
	- Statistik Penduduk Lanjut Usia Provinsi Jawa Timur 2010-Hasil Sensus Penduduk
	2010
	- source_sentence: 'Penduduk Papua: migrasi 2015'
	sentences:
	- Rata-rata Upah/Gaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Pendidikan Tertinggi
	dan jenis pekerjaan utama, 2019
	- Statistik Pemotongan Ternak 2010 dan 2011
	- Statistik Harga Produsen Pertanian Sub Sektor Tanaman Pangan, Hortikultura dan
	Perkebunan Rakyat 2010
	- source_sentence: statistik konstruksi 2022
	sentences:
	- Studi Modal Sosial 2006
	- BRS upah buruh agustus 2018
	- Statistik Perdagangan Luar Negeri Indonesia Ekspor 2006 vol 1
	- source_sentence: Statistik ekspor Indonesia Maret 2202
	sentences:
	- Produk Domestik Bruto Indonesia Triwulanan 2006-2010
	- Indeks Perilaku Anti Korupsi (IPAK) Indonesia 2023 sebesar 3,92, menurun dibandingkan
	IPAK 2022
	- Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut HS, Januari 2023
	datasets:
	- yahyaabd/allstats-semantic-search-synthetic-dataset-v1
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	model-index:
	- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: allstats semantic search v1 dev
	type: allstats-semantic-search-v1-dev
	metrics:
	- type: pearson_cosine
	value: 0.9894566758405579
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9072484378842124
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: allstat semantic search v1 test
	type: allstat-semantic-search-v1-test
	metrics:
	- type: pearson_cosine
	value: 0.9895284407960067
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9074137706349162
	name: Spearman Cosine
	---

	# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) dataset. 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: [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 -->
	- Maximum Sequence Length: 128 tokens
	- Output Dimensionality: 768 dimensions
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1)
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
	- Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

	### Full Model Architecture

	```
	SentenceTransformer(
	(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
	(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("yahyaabd/allstats-semantic-search-model-v1")
	# Run inference
	sentences = [
	'Statistik ekspor Indonesia Maret 2202',
	'Produk Domestik Bruto Indonesia Triwulanan 2006-2010',
	'Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut HS, Januari 2023',
	]
	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

	#### Semantic Similarity

	* Datasets: `allstats-semantic-search-v1-dev` and `allstat-semantic-search-v1-test`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| allstats-semantic-search-v1-dev \| allstat-semantic-search-v1-test \|
	\|:--------------------\|:--------------------------------\|:--------------------------------\|
	\| pearson_cosine \| 0.9895 \| 0.9895 \|
	\| spearman_cosine \| 0.9072 \| 0.9074 \|

	<!--
	## 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

	#### allstats-semantic-search-synthetic-dataset-v1

	* Dataset: [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) at [06f849a](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/06f849af5602fea6ce00e5ecdd9a99cd0cafc2de)
	* Size: 212,917 training samples
	* Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| doc \| label \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 11.48 tokens</li><li>max: 29 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.89 tokens</li><li>max: 53 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.52</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:-------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------\|:------------------\|
	\| <code>ringkasan aktivitas badan pusat statistik tahun 2018</code> \| <code>Statistik Harga Produsen sektor pertanian di indonesia 2008</code> \| <code>0.1</code> \|
	\| <code>indikator kesejahteraan petani rejang lebong 2015</code> \| <code>Diagram Timbang Nilai Tukar Petani Kabupaten Rejang Lebong 2015</code> \| <code>0.82</code> \|
	\| <code>Berapa persen kenaikan kunjungan wisatawan mancanegara pada April 2024?</code> \| <code>Indeks Perilaku Anti Korupsi (IPAK) Indonesia 2023 sebesar 3,92, menurun dibandingkan IPAK 2022</code> \| <code>0.0</code> \|
	* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
	```json
	{
	"loss_fct": "torch.nn.modules.loss.MSELoss"
	}
	```

	### Evaluation Dataset

	#### allstats-semantic-search-synthetic-dataset-v1

	* Dataset: [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) at [06f849a](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/06f849af5602fea6ce00e5ecdd9a99cd0cafc2de)
	* Size: 26,614 evaluation samples
	* Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| doc \| label \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:--------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 11.21 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 14.41 tokens</li><li>max: 54 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:-----------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------\|:------------------\|
	\| <code>Laporan bulanan ekonomi Indonesia bulan November 201</code> \| <code>Laporan Bulanan Data Sosial Ekonomi November 2021</code> \| <code>0.92</code> \|
	\| <code>pekerjaan layak di indonesia tahun 2022: data dan analisis</code> \| <code>Statistik Penduduk Lanjut Usia Provinsi Papua Barat 2010-Hasil Sensus Penduduk 2010</code> \| <code>0.09</code> \|
	\| <code>Tabel pendapatan rata-rata pekerja lepas berdasarkan provinsi dan pendidikan tahun 2021</code> \| <code>Nilai Impor Kendaraan Bermotor Menurut Negara Asal Utama (Nilai CIF:juta US$), 2018-2023</code> \| <code>0.1</code> \|
	* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
	```json
	{
	"loss_fct": "torch.nn.modules.loss.MSELoss"
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `num_train_epochs`: 4
	- `warmup_ratio`: 0.1
	- `fp16`: True

	#### 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`: 32
	- `per_device_eval_batch_size`: 32
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 5e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 4
	- `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`: None
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `include_for_metrics`: []
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `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
	- `use_liger_kernel`: False
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

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

	\| Epoch \| Step \| Training Loss \| Validation Loss \| allstats-semantic-search-v1-dev_spearman_cosine \| allstat-semantic-search-v1-test_spearman_cosine \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|:-----------------------------------------------:\|:-----------------------------------------------:\|
	\| 0.0376 \| 250 \| 0.0683 \| 0.0432 \| 0.6955 \| - \|
	\| 0.0751 \| 500 \| 0.0393 \| 0.0322 \| 0.7230 \| - \|
	\| 0.1127 \| 750 \| 0.0321 \| 0.0270 \| 0.7476 \| - \|
	\| 0.1503 \| 1000 \| 0.0255 \| 0.0226 \| 0.7789 \| - \|
	\| 0.1879 \| 1250 \| 0.024 \| 0.0213 \| 0.7683 \| - \|
	\| 0.2254 \| 1500 \| 0.022 \| 0.0199 \| 0.7727 \| - \|
	\| 0.2630 \| 1750 \| 0.0219 \| 0.0195 \| 0.7853 \| - \|
	\| 0.3006 \| 2000 \| 0.0202 \| 0.0188 \| 0.7795 \| - \|
	\| 0.3381 \| 2250 \| 0.0191 \| 0.0187 \| 0.7943 \| - \|
	\| 0.3757 \| 2500 \| 0.0198 \| 0.0178 \| 0.7842 \| - \|
	\| 0.4133 \| 2750 \| 0.0179 \| 0.0184 \| 0.7974 \| - \|
	\| 0.4509 \| 3000 \| 0.0179 \| 0.0194 \| 0.7810 \| - \|
	\| 0.4884 \| 3250 \| 0.0182 \| 0.0168 \| 0.8080 \| - \|
	\| 0.5260 \| 3500 \| 0.0174 \| 0.0164 \| 0.8131 \| - \|
	\| 0.5636 \| 3750 \| 0.0174 \| 0.0154 \| 0.8113 \| - \|
	\| 0.6011 \| 4000 \| 0.0169 \| 0.0157 \| 0.7981 \| - \|
	\| 0.6387 \| 4250 \| 0.0152 \| 0.0146 \| 0.8099 \| - \|
	\| 0.6763 \| 4500 \| 0.0148 \| 0.0147 \| 0.8091 \| - \|
	\| 0.7139 \| 4750 \| 0.0145 \| 0.0145 \| 0.8178 \| - \|
	\| 0.7514 \| 5000 \| 0.014 \| 0.0139 \| 0.8184 \| - \|
	\| 0.7890 \| 5250 \| 0.0145 \| 0.0130 \| 0.8166 \| - \|
	\| 0.8266 \| 5500 \| 0.0134 \| 0.0129 \| 0.8306 \| - \|
	\| 0.8641 \| 5750 \| 0.013 \| 0.0122 \| 0.8251 \| - \|
	\| 0.9017 \| 6000 \| 0.0136 \| 0.0130 \| 0.8265 \| - \|
	\| 0.9393 \| 6250 \| 0.0123 \| 0.0126 \| 0.8224 \| - \|
	\| 0.9769 \| 6500 \| 0.0113 \| 0.0120 \| 0.8305 \| - \|
	\| 1.0144 \| 6750 \| 0.0129 \| 0.0117 \| 0.8204 \| - \|
	\| 1.0520 \| 7000 \| 0.0106 \| 0.0116 \| 0.8284 \| - \|
	\| 1.0896 \| 7250 \| 0.01 \| 0.0116 \| 0.8303 \| - \|
	\| 1.1271 \| 7500 \| 0.0096 \| 0.0110 \| 0.8303 \| - \|
	\| 1.1647 \| 7750 \| 0.01 \| 0.0113 \| 0.8305 \| - \|
	\| 1.2023 \| 8000 \| 0.0116 \| 0.0108 \| 0.8300 \| - \|
	\| 1.2399 \| 8250 \| 0.0095 \| 0.0104 \| 0.8432 \| - \|
	\| 1.2774 \| 8500 \| 0.009 \| 0.0104 \| 0.8370 \| - \|
	\| 1.3150 \| 8750 \| 0.0101 \| 0.0102 \| 0.8434 \| - \|
	\| 1.3526 \| 9000 \| 0.01 \| 0.0097 \| 0.8450 \| - \|
	\| 1.3901 \| 9250 \| 0.0097 \| 0.0103 \| 0.8286 \| - \|
	\| 1.4277 \| 9500 \| 0.0092 \| 0.0096 \| 0.8393 \| - \|
	\| 1.4653 \| 9750 \| 0.0093 \| 0.0089 \| 0.8480 \| - \|
	\| 1.5029 \| 10000 \| 0.0088 \| 0.0090 \| 0.8439 \| - \|
	\| 1.5404 \| 10250 \| 0.0087 \| 0.0089 \| 0.8569 \| - \|
	\| 1.5780 \| 10500 \| 0.0082 \| 0.0088 \| 0.8488 \| - \|
	\| 1.6156 \| 10750 \| 0.009 \| 0.0089 \| 0.8493 \| - \|
	\| 1.6531 \| 11000 \| 0.0086 \| 0.0086 \| 0.8499 \| - \|
	\| 1.6907 \| 11250 \| 0.0076 \| 0.0083 \| 0.8600 \| - \|
	\| 1.7283 \| 11500 \| 0.0076 \| 0.0081 \| 0.8621 \| - \|
	\| 1.7659 \| 11750 \| 0.0079 \| 0.0081 \| 0.8611 \| - \|
	\| 1.8034 \| 12000 \| 0.0082 \| 0.0085 \| 0.8540 \| - \|
	\| 1.8410 \| 12250 \| 0.0074 \| 0.0081 \| 0.8620 \| - \|
	\| 1.8786 \| 12500 \| 0.007 \| 0.0080 \| 0.8639 \| - \|
	\| 1.9161 \| 12750 \| 0.0071 \| 0.0083 \| 0.8450 \| - \|
	\| 1.9537 \| 13000 \| 0.007 \| 0.0076 \| 0.8585 \| - \|
	\| 1.9913 \| 13250 \| 0.0072 \| 0.0074 \| 0.8640 \| - \|
	\| 2.0289 \| 13500 \| 0.0055 \| 0.0069 \| 0.8699 \| - \|
	\| 2.0664 \| 13750 \| 0.0056 \| 0.0068 \| 0.8673 \| - \|
	\| 2.1040 \| 14000 \| 0.0052 \| 0.0066 \| 0.8723 \| - \|
	\| 2.1416 \| 14250 \| 0.0059 \| 0.0069 \| 0.8644 \| - \|
	\| 2.1791 \| 14500 \| 0.0055 \| 0.0068 \| 0.8670 \| - \|
	\| 2.2167 \| 14750 \| 0.005 \| 0.0065 \| 0.8723 \| - \|
	\| 2.2543 \| 15000 \| 0.0053 \| 0.0066 \| 0.8766 \| - \|
	\| 2.2919 \| 15250 \| 0.0057 \| 0.0065 \| 0.8782 \| - \|
	\| 2.3294 \| 15500 \| 0.0053 \| 0.0064 \| 0.8749 \| - \|
	\| 2.3670 \| 15750 \| 0.0056 \| 0.0070 \| 0.8708 \| - \|
	\| 2.4046 \| 16000 \| 0.0058 \| 0.0065 \| 0.8731 \| - \|
	\| 2.4421 \| 16250 \| 0.0047 \| 0.0064 \| 0.8793 \| - \|
	\| 2.4797 \| 16500 \| 0.0049 \| 0.0063 \| 0.8801 \| - \|
	\| 2.5173 \| 16750 \| 0.0051 \| 0.0063 \| 0.8782 \| - \|
	\| 2.5549 \| 17000 \| 0.0053 \| 0.0060 \| 0.8799 \| - \|
	\| 2.5924 \| 17250 \| 0.0051 \| 0.0059 \| 0.8825 \| - \|
	\| 2.6300 \| 17500 \| 0.0048 \| 0.0060 \| 0.8761 \| - \|
	\| 2.6676 \| 17750 \| 0.0055 \| 0.0055 \| 0.8773 \| - \|
	\| 2.7051 \| 18000 \| 0.0045 \| 0.0053 \| 0.8833 \| - \|
	\| 2.7427 \| 18250 \| 0.0041 \| 0.0053 \| 0.8868 \| - \|
	\| 2.7803 \| 18500 \| 0.0051 \| 0.0054 \| 0.8811 \| - \|
	\| 2.8179 \| 18750 \| 0.004 \| 0.0052 \| 0.8881 \| - \|
	\| 2.8554 \| 19000 \| 0.0043 \| 0.0053 \| 0.8764 \| - \|
	\| 2.8930 \| 19250 \| 0.0047 \| 0.0051 \| 0.8874 \| - \|
	\| 2.9306 \| 19500 \| 0.0038 \| 0.0051 \| 0.8922 \| - \|
	\| 2.9681 \| 19750 \| 0.0047 \| 0.0050 \| 0.8821 \| - \|
	\| 3.0057 \| 20000 \| 0.0037 \| 0.0048 \| 0.8911 \| - \|
	\| 3.0433 \| 20250 \| 0.0031 \| 0.0048 \| 0.8911 \| - \|
	\| 3.0809 \| 20500 \| 0.0032 \| 0.0046 \| 0.8934 \| - \|
	\| 3.1184 \| 20750 \| 0.0034 \| 0.0046 \| 0.8942 \| - \|
	\| 3.1560 \| 21000 \| 0.0028 \| 0.0045 \| 0.8976 \| - \|
	\| 3.1936 \| 21250 \| 0.0034 \| 0.0045 \| 0.8932 \| - \|
	\| 3.2311 \| 21500 \| 0.003 \| 0.0044 \| 0.8959 \| - \|
	\| 3.2687 \| 21750 \| 0.0033 \| 0.0044 \| 0.8961 \| - \|
	\| 3.3063 \| 22000 \| 0.0029 \| 0.0043 \| 0.8995 \| - \|
	\| 3.3439 \| 22250 \| 0.0029 \| 0.0044 \| 0.8978 \| - \|
	\| 3.3814 \| 22500 \| 0.0027 \| 0.0043 \| 0.8998 \| - \|
	\| 3.4190 \| 22750 \| 0.003 \| 0.0043 \| 0.9019 \| - \|
	\| 3.4566 \| 23000 \| 0.0027 \| 0.0042 \| 0.8982 \| - \|
	\| 3.4941 \| 23250 \| 0.0027 \| 0.0042 \| 0.9014 \| - \|
	\| 3.5317 \| 23500 \| 0.0034 \| 0.0042 \| 0.9025 \| - \|
	\| 3.5693 \| 23750 \| 0.003 \| 0.0041 \| 0.9027 \| - \|
	\| 3.6069 \| 24000 \| 0.0029 \| 0.0041 \| 0.9003 \| - \|
	\| 3.6444 \| 24250 \| 0.0027 \| 0.0040 \| 0.9023 \| - \|
	\| 3.6820 \| 24500 \| 0.0027 \| 0.0040 \| 0.9035 \| - \|
	\| 3.7196 \| 24750 \| 0.0033 \| 0.0040 \| 0.9042 \| - \|
	\| 3.7571 \| 25000 \| 0.0028 \| 0.0039 \| 0.9053 \| - \|
	\| 3.7947 \| 25250 \| 0.0027 \| 0.0039 \| 0.9049 \| - \|
	\| 3.8323 \| 25500 \| 0.0033 \| 0.0039 \| 0.9057 \| - \|
	\| 3.8699 \| 25750 \| 0.0025 \| 0.0039 \| 0.9075 \| - \|
	\| 3.9074 \| 26000 \| 0.003 \| 0.0039 \| 0.9068 \| - \|
	\| 3.9450 \| 26250 \| 0.0026 \| 0.0039 \| 0.9073 \| - \|
	\| 3.9826 \| 26500 \| 0.0023 \| 0.0038 \| 0.9072 \| - \|
	\| 4.0 \| 26616 \| - \| - \| - \| 0.9074 \|

	</details>

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.3.1
	- Transformers: 4.47.1
	- PyTorch: 2.2.2+cu121
	- Accelerate: 1.2.1
	- Datasets: 3.2.0
	- Tokenizers: 0.21.0

	## Citation

	### BibTeX

	#### Sentence Transformers
	```bibtex
	@inproceedings{reimers-2019-sentence-bert,
	title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2019",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/1908.10084",
	}
	```

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