Add new SentenceTransformer model

490f8ec verified 2 months ago

39.6 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:212940
	- loss:CosineSimilarityLoss
	base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	widget:
	- source_sentence: Ringkasan data strategis BPS 2012
	sentences:
	- Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Provinsi dan
	Jenis Pekerjaan Utama, 2021
	- Laporan Perekonomian Indonesia 2007
	- Statistik Potensi Desa Provinsi Banten 2008
	- source_sentence: tahun berapa ekspor naik 2,37% dan impor naik 30,30%?
	sentences:
	- Bulan November 2006 Ekspor Naik 2,37 % dan Impor Naik 30,30 %
	- Indeks Harga Konsumen per Kelompok di 82 Kota <sup>1</sup> (2012=100)
	- 'Februari 2022: Tingkat Pengangguran Terbuka (TPT) sebesar 5,83 persen dan Rata-rata
	upah buruh sebesar 2,89 juta rupiah per bulan'
	- source_sentence: akses air bersih di indonesia (2005-2009)
	sentences:
	- Desember 2016, Rupiah Terapresiasi 0,74 Persen Terhadap Dolar Amerika
	- Statistik Air Bersih 2005-2009
	- Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi
	yang Ditamatkan dan Lapangan Pekerjaan Utama di 17 Sektor (rupiah), 2018
	- source_sentence: Tinjauan Regional Berdasarkan PDRB Kabupaten/Kota 2014-2018, Buku
	2 Pulau Jawa dan Bali
	sentences:
	- Profil Migran Hasil Susenas 2011-2012
	- Statistik Gas Kota 2004-2008
	- Jumlah kunjungan wisman ke Indonesia melalui pintu masuk utama pada Juni 2022
	mencapai 345,44 ribu kunjungan dan Jumlah penumpang angkutan udara internasional
	pada Juni 2022 naik 23,28 persen
	- source_sentence: perubahan nilai tukar petani bulan mei 2017
	sentences:
	- Perkembangan Nilai Tukar Petani Mei 2017
	- NTP Naik 0,15%, Harga Gabah Kualitas GKG Naik 0,98%
	- Statistik Restoran/Rumah Makan Tahun 2014
	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-MiniLM-L12-v2
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: allstats semantic search mini v1 ev
	type: allstats-semantic-search-mini-v1-ev
	metrics:
	- type: pearson_cosine
	value: 0.9941642060264452
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9500670338760151
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: allstat semantic search mini v1 test
	type: allstat-semantic-search-mini-v1-test
	metrics:
	- type: pearson_cosine
	value: 0.9944714588734742
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9512629234712933
	name: Spearman Cosine
	---

	# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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 384-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-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 8d6b950845285729817bf8e1af1861502c2fed0c -->
	- Maximum Sequence Length: 128 tokens
	- Output Dimensionality: 384 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: BertModel
	(1): Pooling({'word_embedding_dimension': 384, '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-mini-v1")
	# Run inference
	sentences = [
	'perubahan nilai tukar petani bulan mei 2017',
	'Perkembangan Nilai Tukar Petani Mei 2017',
	'Statistik Restoran/Rumah Makan Tahun 2014',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 384]

	# 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-mini-v1-ev` and `allstat-semantic-search-mini-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-mini-v1-ev \| allstat-semantic-search-mini-v1-test \|
	\|:--------------------\|:------------------------------------\|:-------------------------------------\|
	\| pearson_cosine \| 0.9942 \| 0.9945 \|
	\| spearman_cosine \| 0.9501 \| 0.9513 \|

	<!--
	## 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 [b13c0a7](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/b13c0a7412396a836cfbb887e140f183f3a6d65e)
	* Size: 212,940 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: 11.46 tokens</li><li>max: 34 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 14.47 tokens</li><li>max: 54 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.05</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:---------------------------------------------------------------\|:-----------------------------------------------------------------------\|:------------------\|
	\| <code>aDta industri besar dan sedang Indonesia 2008</code> \| <code>Statistik Industri Besar dan Sedang Indonesia 2008</code> \| <code>0.9</code> \|
	\| <code>profil bisnis konstruksi individu jawa barat 2022</code> \| <code>Statistik Industri Manufaktur Indonesia 2015 - Bahan Baku</code> \| <code>0.15</code> \|
	\| <code>data statistik ekonomi indonesia</code> \| <code>Nilai Tukar Valuta Asing di Indonesia 2014</code> \| <code>0.08</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 [b13c0a7](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/b13c0a7412396a836cfbb887e140f183f3a6d65e)
	* Size: 26,618 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: 11.38 tokens</li><li>max: 34 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.63 tokens</li><li>max: 55 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:-------------------------------------------------------------------\|:---------------------------------------------------------------------------\|:------------------\|
	\| <code>tahun berapa ekspor naik 2,37% dan impor naik 30,30%?</code> \| <code>Bulan November 2006 Ekspor Naik 2,37 % dan Impor Naik 30,30 %</code> \| <code>1.0</code> \|
	\| <code>Berapa produksi padi pada tahun 2023?</code> \| <code>Produksi padi tahun lainnya</code> \| <code>0.0</code> \|
	\| <code>data statistik solus per aqua 2015</code> \| <code>Statistik Solus Per Aqua (SPA) 2015</code> \| <code>0.97</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`: 64
	- `per_device_eval_batch_size`: 64
	- `num_train_epochs`: 20
	- `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`: 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`: 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`: 20
	- `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-mini-v1-ev_spearman_cosine \| allstat-semantic-search-mini-v1-test_spearman_cosine \|
	\|:-------:\|:-----:\|:-------------:\|:---------------:\|:---------------------------------------------------:\|:----------------------------------------------------:\|
	\| 0.1502 \| 500 \| 0.0794 \| 0.0524 \| 0.6869 \| - \|
	\| 0.3005 \| 1000 \| 0.0465 \| 0.0364 \| 0.7262 \| - \|
	\| 0.4507 \| 1500 \| 0.0339 \| 0.0267 \| 0.7638 \| - \|
	\| 0.6010 \| 2000 \| 0.0263 \| 0.0222 \| 0.7804 \| - \|
	\| 0.7512 \| 2500 \| 0.0228 \| 0.0197 \| 0.7883 \| - \|
	\| 0.9014 \| 3000 \| 0.0201 \| 0.0193 \| 0.7894 \| - \|
	\| 1.0517 \| 3500 \| 0.018 \| 0.0166 \| 0.8000 \| - \|
	\| 1.2019 \| 4000 \| 0.0156 \| 0.0154 \| 0.7927 \| - \|
	\| 1.3522 \| 4500 \| 0.0148 \| 0.0146 \| 0.8211 \| - \|
	\| 1.5024 \| 5000 \| 0.014 \| 0.0137 \| 0.8137 \| - \|
	\| 1.6526 \| 5500 \| 0.014 \| 0.0132 \| 0.8160 \| - \|
	\| 1.8029 \| 6000 \| 0.0132 \| 0.0125 \| 0.8309 \| - \|
	\| 1.9531 \| 6500 \| 0.0127 \| 0.0117 \| 0.8221 \| - \|
	\| 2.1034 \| 7000 \| 0.0115 \| 0.0111 \| 0.8269 \| - \|
	\| 2.2536 \| 7500 \| 0.0106 \| 0.0135 \| 0.8157 \| - \|
	\| 2.4038 \| 8000 \| 0.0101 \| 0.0104 \| 0.8423 \| - \|
	\| 2.5541 \| 8500 \| 0.0098 \| 0.0100 \| 0.8329 \| - \|
	\| 2.7043 \| 9000 \| 0.0093 \| 0.0095 \| 0.8415 \| - \|
	\| 2.8546 \| 9500 \| 0.0085 \| 0.0089 \| 0.8517 \| - \|
	\| 3.0048 \| 10000 \| 0.0082 \| 0.0086 \| 0.8537 \| - \|
	\| 3.1550 \| 10500 \| 0.0066 \| 0.0083 \| 0.8508 \| - \|
	\| 3.3053 \| 11000 \| 0.0073 \| 0.0082 \| 0.8450 \| - \|
	\| 3.4555 \| 11500 \| 0.0071 \| 0.0083 \| 0.8574 \| - \|
	\| 3.6058 \| 12000 \| 0.0071 \| 0.0082 \| 0.8486 \| - \|
	\| 3.7560 \| 12500 \| 0.0068 \| 0.0079 \| 0.8610 \| - \|
	\| 3.9062 \| 13000 \| 0.0065 \| 0.0072 \| 0.8649 \| - \|
	\| 4.0565 \| 13500 \| 0.0062 \| 0.0069 \| 0.8602 \| - \|
	\| 4.2067 \| 14000 \| 0.0052 \| 0.0068 \| 0.8680 \| - \|
	\| 4.3570 \| 14500 \| 0.0052 \| 0.0066 \| 0.8639 \| - \|
	\| 4.5072 \| 15000 \| 0.0051 \| 0.0069 \| 0.8664 \| - \|
	\| 4.6575 \| 15500 \| 0.0051 \| 0.0061 \| 0.8782 \| - \|
	\| 4.8077 \| 16000 \| 0.0052 \| 0.0061 \| 0.8721 \| - \|
	\| 4.9579 \| 16500 \| 0.0051 \| 0.0058 \| 0.8781 \| - \|
	\| 5.1082 \| 17000 \| 0.0044 \| 0.0058 \| 0.8788 \| - \|
	\| 5.2584 \| 17500 \| 0.0039 \| 0.0056 \| 0.8803 \| - \|
	\| 5.4087 \| 18000 \| 0.0042 \| 0.0056 \| 0.8807 \| - \|
	\| 5.5589 \| 18500 \| 0.0041 \| 0.0055 \| 0.8818 \| - \|
	\| 5.7091 \| 19000 \| 0.004 \| 0.0051 \| 0.8865 \| - \|
	\| 5.8594 \| 19500 \| 0.0042 \| 0.0052 \| 0.8848 \| - \|
	\| 6.0096 \| 20000 \| 0.0039 \| 0.0050 \| 0.8859 \| - \|
	\| 6.1599 \| 20500 \| 0.0032 \| 0.0049 \| 0.8882 \| - \|
	\| 6.3101 \| 21000 \| 0.0034 \| 0.0048 \| 0.8924 \| - \|
	\| 6.4603 \| 21500 \| 0.0033 \| 0.0049 \| 0.8943 \| - \|
	\| 6.6106 \| 22000 \| 0.0033 \| 0.0051 \| 0.8862 \| - \|
	\| 6.7608 \| 22500 \| 0.0036 \| 0.0046 \| 0.8946 \| - \|
	\| 6.9111 \| 23000 \| 0.0034 \| 0.0045 \| 0.8968 \| - \|
	\| 7.0613 \| 23500 \| 0.0027 \| 0.0042 \| 0.9026 \| - \|
	\| 7.2115 \| 24000 \| 0.0026 \| 0.0042 \| 0.9010 \| - \|
	\| 7.3618 \| 24500 \| 0.0026 \| 0.0044 \| 0.9000 \| - \|
	\| 7.5120 \| 25000 \| 0.0029 \| 0.0043 \| 0.8946 \| - \|
	\| 7.6623 \| 25500 \| 0.0028 \| 0.0041 \| 0.9044 \| - \|
	\| 7.8125 \| 26000 \| 0.0027 \| 0.0040 \| 0.9065 \| - \|
	\| 7.9627 \| 26500 \| 0.0028 \| 0.0039 \| 0.9025 \| - \|
	\| 8.1130 \| 27000 \| 0.0022 \| 0.0037 \| 0.9064 \| - \|
	\| 8.2632 \| 27500 \| 0.0021 \| 0.0037 \| 0.9094 \| - \|
	\| 8.4135 \| 28000 \| 0.0023 \| 0.0037 \| 0.9079 \| - \|
	\| 8.5637 \| 28500 \| 0.0022 \| 0.0038 \| 0.9018 \| - \|
	\| 8.7139 \| 29000 \| 0.0023 \| 0.0038 \| 0.9082 \| - \|
	\| 8.8642 \| 29500 \| 0.0024 \| 0.0035 \| 0.9127 \| - \|
	\| 9.0144 \| 30000 \| 0.0022 \| 0.0034 \| 0.9143 \| - \|
	\| 9.1647 \| 30500 \| 0.0018 \| 0.0034 \| 0.9151 \| - \|
	\| 9.3149 \| 31000 \| 0.002 \| 0.0034 \| 0.9159 \| - \|
	\| 9.4651 \| 31500 \| 0.0019 \| 0.0033 \| 0.9159 \| - \|
	\| 9.6154 \| 32000 \| 0.0019 \| 0.0033 \| 0.9162 \| - \|
	\| 9.7656 \| 32500 \| 0.0021 \| 0.0033 \| 0.9180 \| - \|
	\| 9.9159 \| 33000 \| 0.0019 \| 0.0030 \| 0.9204 \| - \|
	\| 10.0661 \| 33500 \| 0.0018 \| 0.0030 \| 0.9216 \| - \|
	\| 10.2163 \| 34000 \| 0.0016 \| 0.0030 \| 0.9212 \| - \|
	\| 10.3666 \| 34500 \| 0.0015 \| 0.0030 \| 0.9206 \| - \|
	\| 10.5168 \| 35000 \| 0.0016 \| 0.0032 \| 0.9227 \| - \|
	\| 10.6671 \| 35500 \| 0.0017 \| 0.0029 \| 0.9220 \| - \|
	\| 10.8173 \| 36000 \| 0.0016 \| 0.0031 \| 0.9255 \| - \|
	\| 10.9675 \| 36500 \| 0.0018 \| 0.0029 \| 0.9241 \| - \|
	\| 11.1178 \| 37000 \| 0.0013 \| 0.0030 \| 0.9261 \| - \|
	\| 11.2680 \| 37500 \| 0.0013 \| 0.0029 \| 0.9264 \| - \|
	\| 11.4183 \| 38000 \| 0.0015 \| 0.0030 \| 0.9269 \| - \|
	\| 11.5685 \| 38500 \| 0.0014 \| 0.0028 \| 0.9272 \| - \|
	\| 11.7188 \| 39000 \| 0.0014 \| 0.0029 \| 0.9277 \| - \|
	\| 11.8690 \| 39500 \| 0.0014 \| 0.0028 \| 0.9288 \| - \|
	\| 12.0192 \| 40000 \| 0.0014 \| 0.0028 \| 0.9300 \| - \|
	\| 12.1695 \| 40500 \| 0.0011 \| 0.0027 \| 0.9327 \| - \|
	\| 12.3197 \| 41000 \| 0.0012 \| 0.0028 \| 0.9323 \| - \|
	\| 12.4700 \| 41500 \| 0.0013 \| 0.0028 \| 0.9324 \| - \|
	\| 12.6202 \| 42000 \| 0.0014 \| 0.0027 \| 0.9327 \| - \|
	\| 12.7704 \| 42500 \| 0.0013 \| 0.0027 \| 0.9323 \| - \|
	\| 12.9207 \| 43000 \| 0.0013 \| 0.0026 \| 0.9337 \| - \|
	\| 13.0709 \| 43500 \| 0.0011 \| 0.0025 \| 0.9345 \| - \|
	\| 13.2212 \| 44000 \| 0.0011 \| 0.0026 \| 0.9353 \| - \|
	\| 13.3714 \| 44500 \| 0.0011 \| 0.0025 \| 0.9360 \| - \|
	\| 13.5216 \| 45000 \| 0.001 \| 0.0026 \| 0.9347 \| - \|
	\| 13.6719 \| 45500 \| 0.0011 \| 0.0025 \| 0.9364 \| - \|
	\| 13.8221 \| 46000 \| 0.0011 \| 0.0025 \| 0.9373 \| - \|
	\| 13.9724 \| 46500 \| 0.0011 \| 0.0025 \| 0.9374 \| - \|
	\| 14.1226 \| 47000 \| 0.001 \| 0.0024 \| 0.9390 \| - \|
	\| 14.2728 \| 47500 \| 0.001 \| 0.0024 \| 0.9389 \| - \|
	\| 14.4231 \| 48000 \| 0.001 \| 0.0024 \| 0.9388 \| - \|
	\| 14.5733 \| 48500 \| 0.001 \| 0.0025 \| 0.9394 \| - \|
	\| 14.7236 \| 49000 \| 0.0009 \| 0.0024 \| 0.9413 \| - \|
	\| 14.8738 \| 49500 \| 0.0009 \| 0.0024 \| 0.9415 \| - \|
	\| 15.0240 \| 50000 \| 0.0009 \| 0.0024 \| 0.9419 \| - \|
	\| 15.1743 \| 50500 \| 0.0009 \| 0.0024 \| 0.9421 \| - \|
	\| 15.3245 \| 51000 \| 0.0009 \| 0.0025 \| 0.9414 \| - \|
	\| 15.4748 \| 51500 \| 0.0008 \| 0.0024 \| 0.9422 \| - \|
	\| 15.625 \| 52000 \| 0.0009 \| 0.0024 \| 0.9423 \| - \|
	\| 15.7752 \| 52500 \| 0.0008 \| 0.0023 \| 0.9436 \| - \|
	\| 15.9255 \| 53000 \| 0.0009 \| 0.0023 \| 0.9442 \| - \|
	\| 16.0757 \| 53500 \| 0.0008 \| 0.0023 \| 0.9449 \| - \|
	\| 16.2260 \| 54000 \| 0.0008 \| 0.0023 \| 0.9451 \| - \|
	\| 16.3762 \| 54500 \| 0.0008 \| 0.0023 \| 0.9448 \| - \|
	\| 16.5264 \| 55000 \| 0.0008 \| 0.0023 \| 0.9446 \| - \|
	\| 16.6767 \| 55500 \| 0.0008 \| 0.0023 \| 0.9455 \| - \|
	\| 16.8269 \| 56000 \| 0.0008 \| 0.0023 \| 0.9458 \| - \|
	\| 16.9772 \| 56500 \| 0.0008 \| 0.0023 \| 0.9458 \| - \|
	\| 17.1274 \| 57000 \| 0.0007 \| 0.0023 \| 0.9469 \| - \|
	\| 17.2776 \| 57500 \| 0.0007 \| 0.0023 \| 0.9470 \| - \|
	\| 17.4279 \| 58000 \| 0.0007 \| 0.0023 \| 0.9469 \| - \|
	\| 17.5781 \| 58500 \| 0.0007 \| 0.0022 \| 0.9478 \| - \|
	\| 17.7284 \| 59000 \| 0.0007 \| 0.0022 \| 0.9480 \| - \|
	\| 17.8786 \| 59500 \| 0.0007 \| 0.0023 \| 0.9479 \| - \|
	\| 18.0288 \| 60000 \| 0.0007 \| 0.0022 \| 0.9480 \| - \|
	\| 18.1791 \| 60500 \| 0.0006 \| 0.0022 \| 0.9484 \| - \|
	\| 18.3293 \| 61000 \| 0.0006 \| 0.0022 \| 0.9485 \| - \|
	\| 18.4796 \| 61500 \| 0.0007 \| 0.0022 \| 0.9490 \| - \|
	\| 18.6298 \| 62000 \| 0.0007 \| 0.0022 \| 0.9492 \| - \|
	\| 18.7800 \| 62500 \| 0.0007 \| 0.0022 \| 0.9493 \| - \|
	\| 18.9303 \| 63000 \| 0.0007 \| 0.0022 \| 0.9493 \| - \|
	\| 19.0805 \| 63500 \| 0.0006 \| 0.0022 \| 0.9493 \| - \|
	\| 19.2308 \| 64000 \| 0.0006 \| 0.0022 \| 0.9495 \| - \|
	\| 19.3810 \| 64500 \| 0.0006 \| 0.0022 \| 0.9497 \| - \|
	\| 19.5312 \| 65000 \| 0.0006 \| 0.0022 \| 0.9498 \| - \|
	\| 19.6815 \| 65500 \| 0.0006 \| 0.0022 \| 0.9498 \| - \|
	\| 19.8317 \| 66000 \| 0.0006 \| 0.0022 \| 0.9500 \| - \|
	\| 19.9820 \| 66500 \| 0.0006 \| 0.0022 \| 0.9501 \| - \|
	\| 20.0 \| 66560 \| - \| - \| - \| 0.9513 \|

	</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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