Add new SentenceTransformer model

1e0198b verified 30 days ago

42.8 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:212930
	- loss:CosineSimilarityLoss
	base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
	widget:
	- source_sentence: analisis perekonomian indonesia triwulan i 2007
	sentences:
	- Indikator Ekonomi Februari 2017
	- Perkembangan Harga Produsen Gabah Maret 2021
	- Hasil Survei Komoditas Perikanan Potensi 2021 Profil Rumah Tangga Usaha Budidaya
	Rumput Laut
	- source_sentence: Analisis indikator ekonomi Indonesia September 2023
	sentences:
	- Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan
	Negara Oktober 2011
	- April 2015 Harga Grosir Naik 0,17%
	- Direktori Eksportir Indonesia 2014
	- source_sentence: Ekonomi Indonesia awal 2009
	sentences:
	- Statistik Hotel dan Akomodasi Lainnya di Indonesia 2005
	- Neraca Pemerintahan Pusat Indonesia Triwulanan 2007-2013:2
	- Persentase Rumah Tangga menurut Provinsi dan Sumber Penerangan Listrik PLN, 1993-2022
	- source_sentence: Berapa persen kenaikan ekspor Indonesia pada Oktober 2024 dibandingkan
	September 2024?
	sentences:
	- Impor Indonesia mengalami penurunan sebesar 5% pada bulan Oktober 2024.
	- Statistik Pendidikan 2009
	- Penduduk Jambi Hasil Sensus Penduduk SP2000
	- source_sentence: Berapa persen deflasi ysng terjadi paa Maret 2010?
	sentences:
	- Pada Bulan Maret 2010 Terjadi Deflasi Sebesar 0,14 Persen.
	- Analisis Rumah Tangga Usaha Hortikultura di Indonesia Hasil Sensus Pertanian 2013
	- Inflasi September 2008 sebesar 0,97 persen.
	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 2 dev
	type: allstats-semantic-search-v1-2-dev
	metrics:
	- type: pearson_cosine
	value: 0.9923210175659568
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9293313388011538
	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: 0.9929329799075536
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9283010769773397
	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-v1-2")
	# Run inference
	sentences = [
	'Berapa persen deflasi ysng terjadi paa Maret 2010?',
	'Pada Bulan Maret 2010 Terjadi Deflasi Sebesar 0,14 Persen.',
	'Inflasi September 2008 sebesar 0,97 persen.',
	]
	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-v1-2-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-v1-2-dev \| allstat-semantic-search-v1-test \|
	\|:--------------------\|:----------------------------------\|:--------------------------------\|
	\| pearson_cosine \| 0.9923 \| 0.9929 \|
	\| spearman_cosine \| 0.9293 \| 0.9283 \|

	<!--
	## 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 [c477abf](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/c477abfd5da1a6968bad673a50c71e17b62f39b4)
	* Size: 212,930 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: 11.52 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.85 tokens</li><li>max: 70 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>studi tentang kemiskinan urban</code> \| <code>Perkembangan Mingguan Harga Eceran Beberapa Bahan Pokok di Ibukota Provinsi Seluruh Indonesia (Juli-Desember 2018)</code> \| <code>0.1</code> \|
	\| <code>Harga gabah di tingkat produsen bulan September</code> \| <code>Upah Buruh Juli 2020</code> \| <code>0.1</code> \|
	\| <code>Data perusahaan konstruksi di wilayah timur Indonesia thn 2013</code> \| <code>Direktori Perusahaan Konstruksi 2013 Buku 6 Maluku dan Papua</code> \| <code>0.92</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 [c477abf](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/c477abfd5da1a6968bad673a50c71e17b62f39b4)
	* Size: 26,616 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: 11.33 tokens</li><li>max: 53 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.6 tokens</li><li>max: 69 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>Informasi potensi deas di Maluku 011</code> \| <code>Statistik Potensi Desa Provinsi Maluku 2011</code> \| <code>0.87</code> \|
	\| <code>Berapa persen kenaikan jumlah penumpang angkutan udara internasional pada Januari 2024 dibandingkan Desember 2023?</code> \| <code>Kenaikan jumlah penumpang bulan lainnya</code> \| <code>0.0</code> \|
	\| <code>informasi tentang potensi desa jambi tahun 2005</code> \| <code>Statistik Potensi Desa Provinsi Jambi 2005</code> \| <code>0.85</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`: 6
	- `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`: 6
	- `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-v1-2-dev_spearman_cosine \| allstat-semantic-search-v1-test_spearman_cosine \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|:-------------------------------------------------:\|:-----------------------------------------------:\|
	\| 0.0376 \| 250 \| 0.0734 \| 0.0464 \| 0.6927 \| - \|
	\| 0.0751 \| 500 \| 0.043 \| 0.0362 \| 0.7146 \| - \|
	\| 0.1127 \| 750 \| 0.0353 \| 0.0288 \| 0.7364 \| - \|
	\| 0.1503 \| 1000 \| 0.0271 \| 0.0274 \| 0.7571 \| - \|
	\| 0.1878 \| 1250 \| 0.0241 \| 0.0225 \| 0.7738 \| - \|
	\| 0.2254 \| 1500 \| 0.0228 \| 0.0203 \| 0.7699 \| - \|
	\| 0.2630 \| 1750 \| 0.0207 \| 0.0197 \| 0.7881 \| - \|
	\| 0.3005 \| 2000 \| 0.0187 \| 0.0191 \| 0.7900 \| - \|
	\| 0.3381 \| 2250 \| 0.0194 \| 0.0183 \| 0.7794 \| - \|
	\| 0.3757 \| 2500 \| 0.0182 \| 0.0178 \| 0.7870 \| - \|
	\| 0.4132 \| 2750 \| 0.0198 \| 0.0183 \| 0.8009 \| - \|
	\| 0.4508 \| 3000 \| 0.0189 \| 0.0182 \| 0.7912 \| - \|
	\| 0.4884 \| 3250 \| 0.0177 \| 0.0168 \| 0.7963 \| - \|
	\| 0.5259 \| 3500 \| 0.0178 \| 0.0173 \| 0.7920 \| - \|
	\| 0.5635 \| 3750 \| 0.017 \| 0.0183 \| 0.8014 \| - \|
	\| 0.6011 \| 4000 \| 0.0186 \| 0.0180 \| 0.7777 \| - \|
	\| 0.6386 \| 4250 \| 0.0187 \| 0.0167 \| 0.7976 \| - \|
	\| 0.6762 \| 4500 \| 0.015 \| 0.0154 \| 0.8194 \| - \|
	\| 0.7137 \| 4750 \| 0.0158 \| 0.0157 \| 0.8062 \| - \|
	\| 0.7513 \| 5000 \| 0.0152 \| 0.0148 \| 0.8117 \| - \|
	\| 0.7889 \| 5250 \| 0.0148 \| 0.0149 \| 0.8115 \| - \|
	\| 0.8264 \| 5500 \| 0.0146 \| 0.0141 \| 0.8175 \| - \|
	\| 0.8640 \| 5750 \| 0.0154 \| 0.0144 \| 0.7951 \| - \|
	\| 0.9016 \| 6000 \| 0.0155 \| 0.0152 \| 0.8163 \| - \|
	\| 0.9391 \| 6250 \| 0.0145 \| 0.0136 \| 0.8216 \| - \|
	\| 0.9767 \| 6500 \| 0.0149 \| 0.0149 \| 0.8140 \| - \|
	\| 1.0143 \| 6750 \| 0.0132 \| 0.0132 \| 0.8179 \| - \|
	\| 1.0518 \| 7000 \| 0.0108 \| 0.0124 \| 0.8232 \| - \|
	\| 1.0894 \| 7250 \| 0.0109 \| 0.0120 \| 0.8330 \| - \|
	\| 1.1270 \| 7500 \| 0.0112 \| 0.0132 \| 0.8219 \| - \|
	\| 1.1645 \| 7750 \| 0.0116 \| 0.0124 \| 0.8226 \| - \|
	\| 1.2021 \| 8000 \| 0.0121 \| 0.0120 \| 0.8151 \| - \|
	\| 1.2397 \| 8250 \| 0.0109 \| 0.0119 \| 0.8384 \| - \|
	\| 1.2772 \| 8500 \| 0.0103 \| 0.0114 \| 0.8415 \| - \|
	\| 1.3148 \| 8750 \| 0.0105 \| 0.0116 \| 0.8191 \| - \|
	\| 1.3524 \| 9000 \| 0.0104 \| 0.0122 \| 0.8292 \| - \|
	\| 1.3899 \| 9250 \| 0.0108 \| 0.0117 \| 0.8292 \| - \|
	\| 1.4275 \| 9500 \| 0.011 \| 0.0118 \| 0.8339 \| - \|
	\| 1.4651 \| 9750 \| 0.0105 \| 0.0106 \| 0.8367 \| - \|
	\| 1.5026 \| 10000 \| 0.0093 \| 0.0098 \| 0.8467 \| - \|
	\| 1.5402 \| 10250 \| 0.0105 \| 0.0101 \| 0.8334 \| - \|
	\| 1.5778 \| 10500 \| 0.0102 \| 0.0106 \| 0.8324 \| - \|
	\| 1.6153 \| 10750 \| 0.01 \| 0.0097 \| 0.8472 \| - \|
	\| 1.6529 \| 11000 \| 0.0106 \| 0.0098 \| 0.8378 \| - \|
	\| 1.6905 \| 11250 \| 0.0088 \| 0.0095 \| 0.8531 \| - \|
	\| 1.7280 \| 11500 \| 0.0085 \| 0.0095 \| 0.8409 \| - \|
	\| 1.7656 \| 11750 \| 0.0089 \| 0.0091 \| 0.8431 \| - \|
	\| 1.8032 \| 12000 \| 0.0083 \| 0.0088 \| 0.8524 \| - \|
	\| 1.8407 \| 12250 \| 0.0082 \| 0.0088 \| 0.8591 \| - \|
	\| 1.8783 \| 12500 \| 0.0078 \| 0.0092 \| 0.8478 \| - \|
	\| 1.9159 \| 12750 \| 0.009 \| 0.0085 \| 0.8480 \| - \|
	\| 1.9534 \| 13000 \| 0.0082 \| 0.0089 \| 0.8465 \| - \|
	\| 1.9910 \| 13250 \| 0.0076 \| 0.0085 \| 0.8564 \| - \|
	\| 2.0285 \| 13500 \| 0.0059 \| 0.0082 \| 0.8602 \| - \|
	\| 2.0661 \| 13750 \| 0.0073 \| 0.0081 \| 0.8558 \| - \|
	\| 2.1037 \| 14000 \| 0.0075 \| 0.0081 \| 0.8492 \| - \|
	\| 2.1412 \| 14250 \| 0.0066 \| 0.0077 \| 0.8520 \| - \|
	\| 2.1788 \| 14500 \| 0.0066 \| 0.0076 \| 0.8599 \| - \|
	\| 2.2164 \| 14750 \| 0.007 \| 0.0080 \| 0.8589 \| - \|
	\| 2.2539 \| 15000 \| 0.0065 \| 0.0076 \| 0.8552 \| - \|
	\| 2.2915 \| 15250 \| 0.0071 \| 0.0075 \| 0.8604 \| - \|
	\| 2.3291 \| 15500 \| 0.0062 \| 0.0073 \| 0.8714 \| - \|
	\| 2.3666 \| 15750 \| 0.0058 \| 0.0069 \| 0.8714 \| - \|
	\| 2.4042 \| 16000 \| 0.0066 \| 0.0072 \| 0.8570 \| - \|
	\| 2.4418 \| 16250 \| 0.0058 \| 0.0069 \| 0.8757 \| - \|
	\| 2.4793 \| 16500 \| 0.0059 \| 0.0067 \| 0.8726 \| - \|
	\| 2.5169 \| 16750 \| 0.0057 \| 0.0067 \| 0.8663 \| - \|
	\| 2.5545 \| 17000 \| 0.0058 \| 0.0068 \| 0.8703 \| - \|
	\| 2.5920 \| 17250 \| 0.0058 \| 0.0068 \| 0.8765 \| - \|
	\| 2.6296 \| 17500 \| 0.006 \| 0.0067 \| 0.8729 \| - \|
	\| 2.6672 \| 17750 \| 0.0057 \| 0.0067 \| 0.8689 \| - \|
	\| 2.7047 \| 18000 \| 0.0055 \| 0.0065 \| 0.8750 \| - \|
	\| 2.7423 \| 18250 \| 0.0056 \| 0.0066 \| 0.8734 \| - \|
	\| 2.7799 \| 18500 \| 0.0053 \| 0.0062 \| 0.8745 \| - \|
	\| 2.8174 \| 18750 \| 0.0053 \| 0.0062 \| 0.8814 \| - \|
	\| 2.8550 \| 19000 \| 0.0048 \| 0.0063 \| 0.8839 \| - \|
	\| 2.8926 \| 19250 \| 0.005 \| 0.0063 \| 0.8741 \| - \|
	\| 2.9301 \| 19500 \| 0.0063 \| 0.0061 \| 0.8752 \| - \|
	\| 2.9677 \| 19750 \| 0.0052 \| 0.0059 \| 0.8790 \| - \|
	\| 3.0053 \| 20000 \| 0.0049 \| 0.0058 \| 0.8825 \| - \|
	\| 3.0428 \| 20250 \| 0.0042 \| 0.0059 \| 0.8787 \| - \|
	\| 3.0804 \| 20500 \| 0.0043 \| 0.0056 \| 0.8839 \| - \|
	\| 3.1180 \| 20750 \| 0.0036 \| 0.0058 \| 0.8870 \| - \|
	\| 3.1555 \| 21000 \| 0.004 \| 0.0056 \| 0.8825 \| - \|
	\| 3.1931 \| 21250 \| 0.0041 \| 0.0056 \| 0.8884 \| - \|
	\| 3.2307 \| 21500 \| 0.004 \| 0.0054 \| 0.8872 \| - \|
	\| 3.2682 \| 21750 \| 0.0044 \| 0.0052 \| 0.8838 \| - \|
	\| 3.3058 \| 22000 \| 0.0036 \| 0.0053 \| 0.8904 \| - \|
	\| 3.3434 \| 22250 \| 0.0036 \| 0.0054 \| 0.8898 \| - \|
	\| 3.3809 \| 22500 \| 0.0037 \| 0.0051 \| 0.8938 \| - \|
	\| 3.4185 \| 22750 \| 0.0036 \| 0.0051 \| 0.8953 \| - \|
	\| 3.4560 \| 23000 \| 0.0036 \| 0.0051 \| 0.8935 \| - \|
	\| 3.4936 \| 23250 \| 0.004 \| 0.0049 \| 0.8955 \| - \|
	\| 3.5312 \| 23500 \| 0.0033 \| 0.0051 \| 0.8912 \| - \|
	\| 3.5687 \| 23750 \| 0.0037 \| 0.0048 \| 0.8995 \| - \|
	\| 3.6063 \| 24000 \| 0.0037 \| 0.0048 \| 0.8887 \| - \|
	\| 3.6439 \| 24250 \| 0.0037 \| 0.0048 \| 0.8921 \| - \|
	\| 3.6814 \| 24500 \| 0.0034 \| 0.0046 \| 0.9001 \| - \|
	\| 3.7190 \| 24750 \| 0.0041 \| 0.0048 \| 0.9008 \| - \|
	\| 3.7566 \| 25000 \| 0.0037 \| 0.0048 \| 0.8928 \| - \|
	\| 3.7941 \| 25250 \| 0.0038 \| 0.0049 \| 0.8949 \| - \|
	\| 3.8317 \| 25500 \| 0.0037 \| 0.0045 \| 0.9029 \| - \|
	\| 3.8693 \| 25750 \| 0.0034 \| 0.0057 \| 0.8962 \| - \|
	\| 3.9068 \| 26000 \| 0.0035 \| 0.0047 \| 0.8963 \| - \|
	\| 3.9444 \| 26250 \| 0.0039 \| 0.0044 \| 0.9026 \| - \|
	\| 3.9820 \| 26500 \| 0.0034 \| 0.0044 \| 0.8994 \| - \|
	\| 4.0195 \| 26750 \| 0.0029 \| 0.0042 \| 0.9039 \| - \|
	\| 4.0571 \| 27000 \| 0.0025 \| 0.0040 \| 0.9047 \| - \|
	\| 4.0947 \| 27250 \| 0.0027 \| 0.0041 \| 0.9033 \| - \|
	\| 4.1322 \| 27500 \| 0.0027 \| 0.0041 \| 0.9034 \| - \|
	\| 4.1698 \| 27750 \| 0.0025 \| 0.0040 \| 0.9040 \| - \|
	\| 4.2074 \| 28000 \| 0.0033 \| 0.0041 \| 0.9079 \| - \|
	\| 4.2449 \| 28250 \| 0.0027 \| 0.0040 \| 0.9078 \| - \|
	\| 4.2825 \| 28500 \| 0.0024 \| 0.0040 \| 0.9059 \| - \|
	\| 4.3201 \| 28750 \| 0.0026 \| 0.0040 \| 0.9084 \| - \|
	\| 4.3576 \| 29000 \| 0.0021 \| 0.0039 \| 0.9101 \| - \|
	\| 4.3952 \| 29250 \| 0.0024 \| 0.0040 \| 0.9081 \| - \|
	\| 4.4328 \| 29500 \| 0.0024 \| 0.0039 \| 0.9128 \| - \|
	\| 4.4703 \| 29750 \| 0.0027 \| 0.0039 \| 0.9067 \| - \|
	\| 4.5079 \| 30000 \| 0.003 \| 0.0038 \| 0.9120 \| - \|
	\| 4.5455 \| 30250 \| 0.0024 \| 0.0037 \| 0.9140 \| - \|
	\| 4.5830 \| 30500 \| 0.0025 \| 0.0037 \| 0.9116 \| - \|
	\| 4.6206 \| 30750 \| 0.0023 \| 0.0037 \| 0.9124 \| - \|
	\| 4.6582 \| 31000 \| 0.0026 \| 0.0036 \| 0.9161 \| - \|
	\| 4.6957 \| 31250 \| 0.0021 \| 0.0036 \| 0.9155 \| - \|
	\| 4.7333 \| 31500 \| 0.0025 \| 0.0035 \| 0.9147 \| - \|
	\| 4.7708 \| 31750 \| 0.0023 \| 0.0035 \| 0.9171 \| - \|
	\| 4.8084 \| 32000 \| 0.0024 \| 0.0035 \| 0.9153 \| - \|
	\| 4.8460 \| 32250 \| 0.002 \| 0.0035 \| 0.9153 \| - \|
	\| 4.8835 \| 32500 \| 0.0025 \| 0.0034 \| 0.9173 \| - \|
	\| 4.9211 \| 32750 \| 0.0018 \| 0.0035 \| 0.9180 \| - \|
	\| 4.9587 \| 33000 \| 0.0021 \| 0.0035 \| 0.9201 \| - \|
	\| 4.9962 \| 33250 \| 0.0019 \| 0.0035 \| 0.9205 \| - \|
	\| 5.0338 \| 33500 \| 0.0016 \| 0.0034 \| 0.9223 \| - \|
	\| 5.0714 \| 33750 \| 0.0016 \| 0.0034 \| 0.9217 \| - \|
	\| 5.1089 \| 34000 \| 0.0015 \| 0.0033 \| 0.9208 \| - \|
	\| 5.1465 \| 34250 \| 0.002 \| 0.0034 \| 0.9234 \| - \|
	\| 5.1841 \| 34500 \| 0.0017 \| 0.0033 \| 0.9212 \| - \|
	\| 5.2216 \| 34750 \| 0.002 \| 0.0033 \| 0.9212 \| - \|
	\| 5.2592 \| 35000 \| 0.0015 \| 0.0032 \| 0.9241 \| - \|
	\| 5.2968 \| 35250 \| 0.002 \| 0.0031 \| 0.9232 \| - \|
	\| 5.3343 \| 35500 \| 0.0017 \| 0.0031 \| 0.9251 \| - \|
	\| 5.3719 \| 35750 \| 0.0015 \| 0.0031 \| 0.9256 \| - \|
	\| 5.4095 \| 36000 \| 0.0018 \| 0.0031 \| 0.9246 \| - \|
	\| 5.4470 \| 36250 \| 0.0015 \| 0.0030 \| 0.9257 \| - \|
	\| 5.4846 \| 36500 \| 0.0017 \| 0.0030 \| 0.9261 \| - \|
	\| 5.5222 \| 36750 \| 0.0018 \| 0.0030 \| 0.9251 \| - \|
	\| 5.5597 \| 37000 \| 0.0016 \| 0.0030 \| 0.9270 \| - \|
	\| 5.5973 \| 37250 \| 0.0016 \| 0.0029 \| 0.9275 \| - \|
	\| 5.6349 \| 37500 \| 0.0017 \| 0.0029 \| 0.9283 \| - \|
	\| 5.6724 \| 37750 \| 0.0015 \| 0.0029 \| 0.9277 \| - \|
	\| 5.7100 \| 38000 \| 0.0017 \| 0.0029 \| 0.9286 \| - \|
	\| 5.7476 \| 38250 \| 0.0015 \| 0.0029 \| 0.9284 \| - \|
	\| 5.7851 \| 38500 \| 0.0015 \| 0.0029 \| 0.9286 \| - \|
	\| 5.8227 \| 38750 \| 0.0014 \| 0.0029 \| 0.9287 \| - \|
	\| 5.8603 \| 39000 \| 0.0015 \| 0.0028 \| 0.9290 \| - \|
	\| 5.8978 \| 39250 \| 0.0014 \| 0.0028 \| 0.9291 \| - \|
	\| 5.9354 \| 39500 \| 0.0014 \| 0.0028 \| 0.9293 \| - \|
	\| 5.9730 \| 39750 \| 0.0015 \| 0.0028 \| 0.9293 \| - \|
	\| 6.0 \| 39930 \| - \| - \| - \| 0.9283 \|

	</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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