Add new SentenceTransformer model

0e9928f verified about 2 months ago

34.5 kB

	---
	language:
	- id
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:42138
	- loss:CosineSimilarityLoss
	base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
	widget:
	- source_sentence: Informasi importir Indonesia 2014 (Jilid Kedua)
	sentences:
	- Indikator Konstruksi Triwulan IV-2011
	- Benchmark Indeks Konstruksi (2010=100), 1990-2013
	- Statistik Upah Q-2 2002-Q-2 2004
	- source_sentence: Direktori Perusahaan Penggiling Padi Aceh 2012
	sentences:
	- Direktori Perusahaan Industri Penggilingan Padi Tahun 2012 Provinsi Aceh
	- Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan
	Negara, Agustus 2024
	- Statistik Harga Produsen Pertanian Subsektor Tanaman Pangan, Hortikultura, dan
	Tanaman Perkebunan Rakyat 2022
	- source_sentence: Neraca pemerintahan pusat triwulanan 2015-2021:2
	sentences:
	- Tinjauan Regional Berdasarkan PDRB Kabupaten/Kota 2017- 2021, Buku 1 Pulau Sumatera
	- Statistik Tebu Indonesia 2020
	- Indikator Pasar Tenaga Kerja Indonesia Agustus 2011
	- source_sentence: Data pembangunan kuartal kedua 2014
	sentences:
	- Katalog Publikasi BPS 2018
	- Indikator Konstruksi Triwulan II-2014
	- Produk Domestik Regional Bruto Provinsi-provinsi di Indonesia Menurut Penggunaan
	2004-2008
	- source_sentence: Laporan keuangan pemerintah provinsi periode 2003-2006
	sentences:
	- Statistik Perdagangan Luar Negeri Indonesia Ekspor Menurut Kode ISIC 2013-2014
	- Statistik Keuangan Provinsi 2003-2006
	- Statistik Industri Manufaktur Indonesia 2013
	datasets:
	- yahyaabd/bps-publication-title-pairs
	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: allstat semantic dev
	type: allstat-semantic-dev
	metrics:
	- type: pearson_cosine
	value: 0.970895376756816
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8818659766397927
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: allstat semantic test
	type: allstat-semantic-test
	metrics:
	- type: pearson_cosine
	value: 0.9674313370370765
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8746688546004239
	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 [bps-publication-title-pairs](https://huggingface.co./datasets/yahyaabd/bps-publication-title-pairs) 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:
	- [bps-publication-title-pairs](https://huggingface.co./datasets/yahyaabd/bps-publication-title-pairs)
	- Language: id
	<!-- - 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/allstat-semantic-search-mpnet-base-v2-sts")
	# Run inference
	sentences = [
	'Laporan keuangan pemerintah provinsi periode 2003-2006',
	'Statistik Keuangan Provinsi 2003-2006',
	'Statistik Perdagangan Luar Negeri Indonesia Ekspor Menurut Kode ISIC 2013-2014',
	]
	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: `allstat-semantic-dev` and `allstat-semantic-test`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| allstat-semantic-dev \| allstat-semantic-test \|
	\|:--------------------\|:---------------------\|:----------------------\|
	\| pearson_cosine \| 0.9709 \| 0.9674 \|
	\| spearman_cosine \| 0.8819 \| 0.8747 \|

	<!--
	## 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

	#### bps-publication-title-pairs

	* Dataset: [bps-publication-title-pairs](https://huggingface.co./datasets/yahyaabd/bps-publication-title-pairs) at [833f738](https://huggingface.co./datasets/yahyaabd/bps-publication-title-pairs/tree/833f738c0a143143e2d0e45a8905fee6b262d859)
	* Size: 42,138 training samples
	* Columns: <code>query</code>, <code>doc_title</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| doc_title \| score \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 10.71 tokens</li><li>max: 60 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 12.58 tokens</li><li>max: 52 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.53</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| query \| doc_title \| score \|
	\|:---------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------\|:------------------\|
	\| <code>Hasil riset mobilitas Jabodetabek tahun 2023</code> \| <code>Statistik Komuter Jabodetabek Hasil Survei Komuter Jabodetabek 2023</code> \| <code>0.85</code> \|
	\| <code>Indeks harga konsumen di Indonesia tahun 2017 (82 kota)</code> \| <code>Harga Konsumen Beberapa Barang dan Jasa Kelompok Sandang di 82 Kota di Indonesia 2017</code> \| <code>0.15</code> \|
	\| <code>Laporan sektor bangunan Indonesia Q4 2009</code> \| <code>Indikator Konstruksi Triwulan IV Tahun 2009</code> \| <code>0.91</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

	#### bps-publication-title-pairs

	* Dataset: [bps-publication-title-pairs](https://huggingface.co./datasets/yahyaabd/bps-publication-title-pairs) at [833f738](https://huggingface.co./datasets/yahyaabd/bps-publication-title-pairs/tree/833f738c0a143143e2d0e45a8905fee6b262d859)
	* Size: 2,634 evaluation samples
	* Columns: <code>query</code>, <code>doc_title</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| doc_title \| score \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 10.71 tokens</li><li>max: 28 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 12.57 tokens</li><li>max: 39 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.55</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| query \| doc_title \| score \|
	\|:----------------------------------------------------\|:------------------------------------------------------------------\|:-----------------\|
	\| <code>Statistik tebu Indonesia tahun 2018</code> \| <code>Direktori Perusahaan Perkebunan Karet Indonesia 2018</code> \| <code>0.1</code> \|
	\| <code>Data industri makanan dan minuman 2017</code> \| <code>Statistik Upah Buruh Tani di Perdesaan 2018</code> \| <code>0.2</code> \|
	\| <code>Biaya hidup di Gorontalo tahun 2018</code> \| <code>Survei Biaya Hidup (SBH) 2018 Gorontalo</code> \| <code>0.9</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`: 16
	- `per_device_eval_batch_size`: 16
	- `num_train_epochs`: 5
	- `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`: 16
	- `per_device_eval_batch_size`: 16
	- `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`: 5
	- `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 \| allstat-semantic-dev_spearman_cosine \| allstat-semantic-test_spearman_cosine \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|:------------------------------------:\|:-------------------------------------:\|
	\| 0.0380 \| 100 \| 0.0498 \| 0.0301 \| 0.7942 \| - \|
	\| 0.0759 \| 200 \| 0.0274 \| 0.0231 \| 0.8115 \| - \|
	\| 0.1139 \| 300 \| 0.0238 \| 0.0194 \| 0.8151 \| - \|
	\| 0.1519 \| 400 \| 0.0203 \| 0.0181 \| 0.8169 \| - \|
	\| 0.1898 \| 500 \| 0.02 \| 0.0184 \| 0.8188 \| - \|
	\| 0.2278 \| 600 \| 0.0208 \| 0.0170 \| 0.8229 \| - \|
	\| 0.2658 \| 700 \| 0.0182 \| 0.0176 \| 0.8209 \| - \|
	\| 0.3037 \| 800 \| 0.0187 \| 0.0165 \| 0.8260 \| - \|
	\| 0.3417 \| 900 \| 0.0182 \| 0.0169 \| 0.8237 \| - \|
	\| 0.3797 \| 1000 \| 0.0187 \| 0.0166 \| 0.8232 \| - \|
	\| 0.4176 \| 1100 \| 0.019 \| 0.0170 \| 0.8261 \| - \|
	\| 0.4556 \| 1200 \| 0.0186 \| 0.0178 \| 0.8206 \| - \|
	\| 0.4935 \| 1300 \| 0.0185 \| 0.0173 \| 0.8190 \| - \|
	\| 0.5315 \| 1400 \| 0.0188 \| 0.0183 \| 0.8172 \| - \|
	\| 0.5695 \| 1500 \| 0.018 \| 0.0166 \| 0.8192 \| - \|
	\| 0.6074 \| 1600 \| 0.0193 \| 0.0168 \| 0.8240 \| - \|
	\| 0.6454 \| 1700 \| 0.016 \| 0.0152 \| 0.8315 \| - \|
	\| 0.6834 \| 1800 \| 0.0178 \| 0.0163 \| 0.8263 \| - \|
	\| 0.7213 \| 1900 \| 0.0174 \| 0.0150 \| 0.8320 \| - \|
	\| 0.7593 \| 2000 \| 0.0172 \| 0.0152 \| 0.8290 \| - \|
	\| 0.7973 \| 2100 \| 0.0156 \| 0.0158 \| 0.8284 \| - \|
	\| 0.8352 \| 2200 \| 0.0164 \| 0.0143 \| 0.8313 \| - \|
	\| 0.8732 \| 2300 \| 0.0169 \| 0.0165 \| 0.8349 \| - \|
	\| 0.9112 \| 2400 \| 0.0147 \| 0.0150 \| 0.8368 \| - \|
	\| 0.9491 \| 2500 \| 0.0163 \| 0.0148 \| 0.8314 \| - \|
	\| 0.9871 \| 2600 \| 0.0149 \| 0.0137 \| 0.8379 \| - \|
	\| 1.0251 \| 2700 \| 0.0117 \| 0.0134 \| 0.8415 \| - \|
	\| 1.0630 \| 2800 \| 0.0124 \| 0.0129 \| 0.8375 \| - \|
	\| 1.1010 \| 2900 \| 0.0109 \| 0.0124 \| 0.8459 \| - \|
	\| 1.1390 \| 3000 \| 0.0109 \| 0.0123 \| 0.8445 \| - \|
	\| 1.1769 \| 3100 \| 0.0107 \| 0.0126 \| 0.8433 \| - \|
	\| 1.2149 \| 3200 \| 0.0105 \| 0.0131 \| 0.8427 \| - \|
	\| 1.2528 \| 3300 \| 0.0117 \| 0.0130 \| 0.8434 \| - \|
	\| 1.2908 \| 3400 \| 0.0107 \| 0.0126 \| 0.8448 \| - \|
	\| 1.3288 \| 3500 \| 0.0116 \| 0.0119 \| 0.8490 \| - \|
	\| 1.3667 \| 3600 \| 0.0114 \| 0.0124 \| 0.8394 \| - \|
	\| 1.4047 \| 3700 \| 0.011 \| 0.0127 \| 0.8408 \| - \|
	\| 1.4427 \| 3800 \| 0.0116 \| 0.0128 \| 0.8400 \| - \|
	\| 1.4806 \| 3900 \| 0.0117 \| 0.0121 \| 0.8451 \| - \|
	\| 1.5186 \| 4000 \| 0.0129 \| 0.0125 \| 0.8443 \| - \|
	\| 1.5566 \| 4100 \| 0.0117 \| 0.0122 \| 0.8464 \| - \|
	\| 1.5945 \| 4200 \| 0.012 \| 0.0117 \| 0.8468 \| - \|
	\| 1.6325 \| 4300 \| 0.011 \| 0.0122 \| 0.8485 \| - \|
	\| 1.6705 \| 4400 \| 0.0121 \| 0.0112 \| 0.8557 \| - \|
	\| 1.7084 \| 4500 \| 0.0119 \| 0.0110 \| 0.8570 \| - \|
	\| 1.7464 \| 4600 \| 0.0105 \| 0.0113 \| 0.8519 \| - \|
	\| 1.7844 \| 4700 \| 0.0101 \| 0.0113 \| 0.8479 \| - \|
	\| 1.8223 \| 4800 \| 0.0111 \| 0.0116 \| 0.8499 \| - \|
	\| 1.8603 \| 4900 \| 0.0108 \| 0.0117 \| 0.8520 \| - \|
	\| 1.8983 \| 5000 \| 0.0111 \| 0.0111 \| 0.8509 \| - \|
	\| 1.9362 \| 5100 \| 0.0112 \| 0.0111 \| 0.8546 \| - \|
	\| 1.9742 \| 5200 \| 0.0104 \| 0.0115 \| 0.8507 \| - \|
	\| 2.0121 \| 5300 \| 0.0095 \| 0.0105 \| 0.8553 \| - \|
	\| 2.0501 \| 5400 \| 0.0077 \| 0.0106 \| 0.8562 \| - \|
	\| 2.0881 \| 5500 \| 0.007 \| 0.0104 \| 0.8575 \| - \|
	\| 2.1260 \| 5600 \| 0.0075 \| 0.0101 \| 0.8619 \| - \|
	\| 2.1640 \| 5700 \| 0.0077 \| 0.0104 \| 0.8568 \| - \|
	\| 2.2020 \| 5800 \| 0.0073 \| 0.0103 \| 0.8588 \| - \|
	\| 2.2399 \| 5900 \| 0.0076 \| 0.0101 \| 0.8598 \| - \|
	\| 2.2779 \| 6000 \| 0.0072 \| 0.0101 \| 0.8602 \| - \|
	\| 2.3159 \| 6100 \| 0.0076 \| 0.0104 \| 0.8589 \| - \|
	\| 2.3538 \| 6200 \| 0.007 \| 0.0101 \| 0.8592 \| - \|
	\| 2.3918 \| 6300 \| 0.0084 \| 0.0104 \| 0.8547 \| - \|
	\| 2.4298 \| 6400 \| 0.0077 \| 0.0102 \| 0.8594 \| - \|
	\| 2.4677 \| 6500 \| 0.008 \| 0.0102 \| 0.8606 \| - \|
	\| 2.5057 \| 6600 \| 0.0075 \| 0.0101 \| 0.8596 \| - \|
	\| 2.5437 \| 6700 \| 0.0072 \| 0.0105 \| 0.8587 \| - \|
	\| 2.5816 \| 6800 \| 0.0079 \| 0.0105 \| 0.8588 \| - \|
	\| 2.6196 \| 6900 \| 0.0078 \| 0.0098 \| 0.8605 \| - \|
	\| 2.6576 \| 7000 \| 0.0075 \| 0.0100 \| 0.8593 \| - \|
	\| 2.6955 \| 7100 \| 0.008 \| 0.0097 \| 0.8649 \| - \|
	\| 2.7335 \| 7200 \| 0.0074 \| 0.0100 \| 0.8602 \| - \|
	\| 2.7715 \| 7300 \| 0.0069 \| 0.0098 \| 0.8628 \| - \|
	\| 2.8094 \| 7400 \| 0.008 \| 0.0097 \| 0.8615 \| - \|
	\| 2.8474 \| 7500 \| 0.007 \| 0.0097 \| 0.8639 \| - \|
	\| 2.8853 \| 7600 \| 0.0071 \| 0.0093 \| 0.8642 \| - \|
	\| 2.9233 \| 7700 \| 0.0077 \| 0.0102 \| 0.8605 \| - \|
	\| 2.9613 \| 7800 \| 0.008 \| 0.0094 \| 0.8623 \| - \|
	\| 2.9992 \| 7900 \| 0.0076 \| 0.0094 \| 0.8658 \| - \|
	\| 3.0372 \| 8000 \| 0.005 \| 0.0091 \| 0.8673 \| - \|
	\| 3.0752 \| 8100 \| 0.005 \| 0.0088 \| 0.8688 \| - \|
	\| 3.1131 \| 8200 \| 0.0051 \| 0.0088 \| 0.8705 \| - \|
	\| 3.1511 \| 8300 \| 0.0052 \| 0.0089 \| 0.8701 \| - \|
	\| 3.1891 \| 8400 \| 0.0047 \| 0.0088 \| 0.8711 \| - \|
	\| 3.2270 \| 8500 \| 0.0046 \| 0.0086 \| 0.8723 \| - \|
	\| 3.2650 \| 8600 \| 0.0051 \| 0.0086 \| 0.8733 \| - \|
	\| 3.3030 \| 8700 \| 0.0053 \| 0.0088 \| 0.8736 \| - \|
	\| 3.3409 \| 8800 \| 0.0049 \| 0.0086 \| 0.8733 \| - \|
	\| 3.3789 \| 8900 \| 0.0051 \| 0.0087 \| 0.8721 \| - \|
	\| 3.4169 \| 9000 \| 0.0051 \| 0.0086 \| 0.8716 \| - \|
	\| 3.4548 \| 9100 \| 0.005 \| 0.0087 \| 0.8717 \| - \|
	\| 3.4928 \| 9200 \| 0.0055 \| 0.0088 \| 0.8709 \| - \|
	\| 3.5308 \| 9300 \| 0.0046 \| 0.0085 \| 0.8738 \| - \|
	\| 3.5687 \| 9400 \| 0.0052 \| 0.0085 \| 0.8738 \| - \|
	\| 3.6067 \| 9500 \| 0.0052 \| 0.0089 \| 0.8706 \| - \|
	\| 3.6446 \| 9600 \| 0.0049 \| 0.0085 \| 0.8722 \| - \|
	\| 3.6826 \| 9700 \| 0.0051 \| 0.0088 \| 0.8720 \| - \|
	\| 3.7206 \| 9800 \| 0.0046 \| 0.0088 \| 0.8721 \| - \|
	\| 3.7585 \| 9900 \| 0.0051 \| 0.0083 \| 0.8757 \| - \|
	\| 3.7965 \| 10000 \| 0.005 \| 0.0084 \| 0.8744 \| - \|
	\| 3.8345 \| 10100 \| 0.005 \| 0.0084 \| 0.8754 \| - \|
	\| 3.8724 \| 10200 \| 0.0054 \| 0.0087 \| 0.8737 \| - \|
	\| 3.9104 \| 10300 \| 0.0054 \| 0.0083 \| 0.8757 \| - \|
	\| 3.9484 \| 10400 \| 0.005 \| 0.0082 \| 0.8754 \| - \|
	\| 3.9863 \| 10500 \| 0.0049 \| 0.0083 \| 0.8746 \| - \|
	\| 4.0243 \| 10600 \| 0.0041 \| 0.0081 \| 0.8757 \| - \|
	\| 4.0623 \| 10700 \| 0.0034 \| 0.0082 \| 0.8760 \| - \|
	\| 4.1002 \| 10800 \| 0.003 \| 0.0083 \| 0.8751 \| - \|
	\| 4.1382 \| 10900 \| 0.0033 \| 0.0082 \| 0.8770 \| - \|
	\| 4.1762 \| 11000 \| 0.0034 \| 0.0083 \| 0.8772 \| - \|
	\| 4.2141 \| 11100 \| 0.0033 \| 0.0082 \| 0.8773 \| - \|
	\| 4.2521 \| 11200 \| 0.0031 \| 0.0082 \| 0.8787 \| - \|
	\| 4.2901 \| 11300 \| 0.0033 \| 0.0080 \| 0.8805 \| - \|
	\| 4.3280 \| 11400 \| 0.0029 \| 0.0082 \| 0.8787 \| - \|
	\| 4.3660 \| 11500 \| 0.0035 \| 0.0079 \| 0.8796 \| - \|
	\| 4.4039 \| 11600 \| 0.0034 \| 0.0079 \| 0.8799 \| - \|
	\| 4.4419 \| 11700 \| 0.0032 \| 0.0079 \| 0.8794 \| - \|
	\| 4.4799 \| 11800 \| 0.0035 \| 0.0079 \| 0.8807 \| - \|
	\| 4.5178 \| 11900 \| 0.0035 \| 0.0080 \| 0.8798 \| - \|
	\| 4.5558 \| 12000 \| 0.0031 \| 0.0079 \| 0.8806 \| - \|
	\| 4.5938 \| 12100 \| 0.0034 \| 0.0078 \| 0.8812 \| - \|
	\| 4.6317 \| 12200 \| 0.0031 \| 0.0078 \| 0.8811 \| - \|
	\| 4.6697 \| 12300 \| 0.0032 \| 0.0078 \| 0.8813 \| - \|
	\| 4.7077 \| 12400 \| 0.0032 \| 0.0079 \| 0.8809 \| - \|
	\| 4.7456 \| 12500 \| 0.0032 \| 0.0078 \| 0.8815 \| - \|
	\| 4.7836 \| 12600 \| 0.0034 \| 0.0077 \| 0.8818 \| - \|
	\| 4.8216 \| 12700 \| 0.0035 \| 0.0078 \| 0.8817 \| - \|
	\| 4.8595 \| 12800 \| 0.0032 \| 0.0078 \| 0.8818 \| - \|
	\| 4.8975 \| 12900 \| 0.0032 \| 0.0078 \| 0.8818 \| - \|
	\| 4.9355 \| 13000 \| 0.0032 \| 0.0078 \| 0.8820 \| - \|
	\| 4.9734 \| 13100 \| 0.0031 \| 0.0078 \| 0.8819 \| - \|
	\| 5.0 \| 13170 \| - \| - \| - \| 0.8747 \|

	</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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