Add new SentenceTransformer model

3e7aa22 verified 23 days ago

43.8 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:25551
	- loss:OnlineContrastiveLoss
	base_model: sentence-transformers/paraphrase-MiniLM-L12-v2
	widget:
	- source_sentence: Berapa gaji ratarata buruhkaryawan di Indonesia lihat dari umur
	dan lapangan pekerjaannya 2019
	sentences:
	- Rasio laju peningkatan konsumsi tanah dengan laju pertumbuhan penduduk
	- Rata-rata UpahGaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Kelompok Umur
	dan lapangan pekerjaan utama, 2019
	- Ringkasan Neraca Arus Dana, Triwulan Pertama, 2005, (Miliar Rupiah)
	- source_sentence: Average monthly net wage/salary of employees by age group and type
	of work (Rupiah), 2018
	sentences:
	- Ringkasan Neraca Arus Dana, Triwulan III, 2014**), (Miliar Rupiah)
	- Nilai Produksi dan Biaya Produksi Rumah Tangga Usaha Peternakan Menurut Jenis
	Ternak, 2014
	- Rekapitulasi Laporan Posisi Keuangan Perusahaan Penyelenggara Program Asuransi
	Wajib dan BPJS Per 31 Desember (miliar rupiah) 2000-2021
	- source_sentence: jumlah pembangunan fasilitas sekolah baru
	sentences:
	- Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi
	yang Ditamatkan dan Lapangan Pekerjaan Utama di 9 Sektor (rupiah), 2017
	- Posisi Kredit Perbankan1dalam Rupiah dan Valuta Asing Menurut Sektor Ekonomi (miliar
	rupiah), 2016-2018
	- Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Hasil Long Form SP2020 Menurut
	Provinsi/Kabupaten/Kota, 2020
	- source_sentence: Data Pendapatan Rata-rata Orang Yang Berusaha Sendiri Per Provinsi,
	Berdasarkan Lapangan Pekerjaan Utama (2020)
	sentences:
	- Nilai Pendapatan Disposabel Menurut Golongan Rumah Tangga (miliar rupiah), 2000,
	2005, dan 2008
	- IHK dan Rata-rata Upah per Bulan Buruh Pertambangan di Bawah Mandor (Supervisor),
	1996-2014 (1996=100)
	- Ringkasan Neraca Arus Dana Tahun 2004 (Miliar Rupiah)
	- source_sentence: Bagaimana perkembangan koperasi di Indonesia, khususnya sekitar
	tayun 2000?
	sentences:
	- Rata-Rata Harian Aliran Sungai, Tinggi Aliran, dan Volume Air di Beberapa Sungai
	yang Daerah Pengalirannya Lebih dari 1.000 km2, 2000-2011
	- Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut
	Golongan Umur dan Jumlah Jam Kerja Seluruhnya, 2008 - 2024
	- IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor),
	1996-2014 (1996=100)
	datasets:
	- yahyaabd/query-hard-pos-neg-doc-pairs-statictable
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	model-index:
	- name: SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L12-v2
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: allstats semantic mini v1 eval
	type: allstats-semantic-mini-v1-eval
	metrics:
	- type: pearson_cosine
	value: 0.8479971660039509
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7745638757528484
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: allstat search mini v1 test
	type: allstat-search-mini-v1-test
	metrics:
	- type: pearson_cosine
	value: 0.8538445733470035
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7767623851780713
	name: Spearman Cosine
	---

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

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L12-v2) on the [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) 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-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L12-v2) <!-- at revision 3f21b01a41e265ecb43cef6afeef20b7e578b637 -->
	- Maximum Sequence Length: 256 tokens
	- Output Dimensionality: 384 dimensions
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable)
	<!-- - 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': 256, '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-search-miniLM-v1")
	# Run inference
	sentences = [
	'Bagaimana perkembangan koperasi di Indonesia, khususnya sekitar tayun 2000?',
	'IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), 1996-2014 (1996=100)',
	'Rata-Rata Harian Aliran Sungai, Tinggi Aliran, dan Volume Air di Beberapa Sungai yang Daerah Pengalirannya Lebih dari 1.000 km2, 2000-2011',
	]
	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-mini-v1-eval` and `allstat-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-mini-v1-eval \| allstat-search-mini-v1-test \|
	\|:--------------------\|:-------------------------------\|:----------------------------\|
	\| pearson_cosine \| 0.848 \| 0.8538 \|
	\| spearman_cosine \| 0.7746 \| 0.7768 \|

	<!--
	## 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

	#### query-hard-pos-neg-doc-pairs-statictable

	* Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [25756d3](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/25756d36046bf92b56bce1b450fd080853688667)
	* Size: 25,551 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 \| int \|
	\| details \| <ul><li>min: 9 tokens</li><li>mean: 28.64 tokens</li><li>max: 53 tokens</li></ul> \| <ul><li>min: 11 tokens</li><li>mean: 36.67 tokens</li><li>max: 70 tokens</li></ul> \| <ul><li>0: ~65.80%</li><li>1: ~34.20%</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:-----------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------\|:---------------\|
	\| <code>Gaji nominal, indeks upah: nominal & riil pekerja manufaktur non-mandor (2012=100), 2013-2014</code> \| <code>Ringkasan Neraca Arus Dana, Triwulan I, 2007, (Miliar Rupiah)</code> \| <code>0</code> \|
	\| <code>gaji nominal, indeks upah: nominal & riil pekerja manufaktur non-mandor (2012=100), 2013-2014</code> \| <code>Ringkasan Neraca Arus Dana, Triwulan I, 2007, (Miliar Rupiah)</code> \| <code>0</code> \|
	\| <code>GAJI NOMINAL, INDEKS UPAH: NOMINAL & RIIL PEKERJA MANUFAKTUR NON-MANDOR (2012=100), 2013-2014</code> \| <code>Ringkasan Neraca Arus Dana, Triwulan I, 2007, (Miliar Rupiah)</code> \| <code>0</code> \|
	* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

	### Evaluation Dataset

	#### query-hard-pos-neg-doc-pairs-statictable

	* Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [25756d3](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/25756d36046bf92b56bce1b450fd080853688667)
	* Size: 5,463 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 \| int \|
	\| details \| <ul><li>min: 10 tokens</li><li>mean: 29.3 tokens</li><li>max: 62 tokens</li></ul> \| <ul><li>min: 12 tokens</li><li>mean: 37.1 tokens</li><li>max: 69 tokens</li></ul> \| <ul><li>0: ~73.20%</li><li>1: ~26.80%</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------\|:---------------\|
	\| <code>Bagaimana penghasilan wirausahawan di Indonesia bervariasi per provinsi dan jenis pekerjaan utama di tahun 2016?</code> \| <code>Realisasi Penerimaan dan Pengeluaran Pemerintah Desa (Juta Rupiah) di Perkotaan menurut Provinsi, 2000-2012</code> \| <code>0</code> \|
	\| <code>bagaimana penghasilan wirausahawan di indonesia bervariasi per provinsi dan jenis pekerjaan utama di tahun 2016?</code> \| <code>Realisasi Penerimaan dan Pengeluaran Pemerintah Desa (Juta Rupiah) di Perkotaan menurut Provinsi, 2000-2012</code> \| <code>0</code> \|
	\| <code>BAGAIMANA PENGHASILAN WIRAUSAHAWAN DI INDONESIA BERVARIASI PER PROVINSI DAN JENIS PEKERJAAN UTAMA DI TAHUN 2016?</code> \| <code>Realisasi Penerimaan dan Pengeluaran Pemerintah Desa (Juta Rupiah) di Perkotaan menurut Provinsi, 2000-2012</code> \| <code>0</code> \|
	* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

	### 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
	- `load_best_model_at_end`: True
	- `eval_on_start`: 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`: True
	- `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`: True
	- `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-mini-v1-eval_spearman_cosine \| allstat-search-mini-v1-test_spearman_cosine \|
	\|:----------:\|:--------:\|:-------------:\|:---------------:\|:----------------------------------------------:\|:-------------------------------------------:\|
	\| 0 \| 0 \| - \| 1.0797 \| 0.5314 \| - \|
	\| 0.0250 \| 20 \| 1.2823 \| 0.9331 \| 0.5510 \| - \|
	\| 0.0501 \| 40 \| 0.9562 \| 0.6159 \| 0.6492 \| - \|
	\| 0.0751 \| 60 \| 0.5872 \| 0.4629 \| 0.6913 \| - \|
	\| 0.1001 \| 80 \| 0.4101 \| 0.3605 \| 0.7221 \| - \|
	\| 0.1252 \| 100 \| 0.419 \| 0.3919 \| 0.7301 \| - \|
	\| 0.1502 \| 120 \| 0.1517 \| 0.2565 \| 0.7457 \| - \|
	\| 0.1752 \| 140 \| 0.2678 \| 0.2503 \| 0.7484 \| - \|
	\| 0.2003 \| 160 \| 0.225 \| 0.2010 \| 0.7546 \| - \|
	\| 0.2253 \| 180 \| 0.2846 \| 0.3203 \| 0.7420 \| - \|
	\| 0.2503 \| 200 \| 0.2086 \| 0.1981 \| 0.7589 \| - \|
	\| 0.2753 \| 220 \| 0.1255 \| 0.1982 \| 0.7610 \| - \|
	\| 0.3004 \| 240 \| 0.1182 \| 0.2328 \| 0.7583 \| - \|
	\| 0.3254 \| 260 \| 0.1328 \| 0.2218 \| 0.7561 \| - \|
	\| 0.3504 \| 280 \| 0.1228 \| 0.4583 \| 0.7343 \| - \|
	\| 0.3755 \| 300 \| 0.1394 \| 0.1785 \| 0.7705 \| - \|
	\| 0.4005 \| 320 \| 0.2577 \| 0.1800 \| 0.7650 \| - \|
	\| 0.4255 \| 340 \| 0.1903 \| 0.2680 \| 0.7557 \| - \|
	\| 0.4506 \| 360 \| 0.1164 \| 0.1761 \| 0.7616 \| - \|
	\| 0.4756 \| 380 \| 0.0779 \| 0.3318 \| 0.7453 \| - \|
	\| 0.5006 \| 400 \| 0.1563 \| 0.2209 \| 0.7582 \| - \|
	\| 0.5257 \| 420 \| 0.1835 \| 0.1683 \| 0.7662 \| - \|
	\| 0.5507 \| 440 \| 0.1171 \| 0.1537 \| 0.7658 \| - \|
	\| 0.5757 \| 460 \| 0.0973 \| 0.1381 \| 0.7710 \| - \|
	\| 0.6008 \| 480 \| 0.0578 \| 0.2303 \| 0.7618 \| - \|
	\| 0.6258 \| 500 \| 0.1343 \| 0.1431 \| 0.7710 \| - \|
	\| 0.6508 \| 520 \| 0.1274 \| 0.1646 \| 0.7695 \| - \|
	\| 0.6758 \| 540 \| 0.057 \| 0.1775 \| 0.7606 \| - \|
	\| 0.7009 \| 560 \| 0.0392 \| 0.1425 \| 0.7689 \| - \|
	\| 0.7259 \| 580 \| 0.0434 \| 0.1399 \| 0.7712 \| - \|
	\| 0.7509 \| 600 \| 0.1311 \| 0.1747 \| 0.7670 \| - \|
	\| 0.7760 \| 620 \| 0.0475 \| 0.1375 \| 0.7709 \| - \|
	\| 0.8010 \| 640 \| 0.0183 \| 0.1465 \| 0.7685 \| - \|
	\| 0.8260 \| 660 \| 0.024 \| 0.1666 \| 0.7669 \| - \|
	\| 0.8511 \| 680 \| 0.0249 \| 0.1728 \| 0.7656 \| - \|
	\| 0.8761 \| 700 \| 0.041 \| 0.1624 \| 0.7711 \| - \|
	\| 0.9011 \| 720 \| 0.0835 \| 0.1397 \| 0.7716 \| - \|
	\| 0.9262 \| 740 \| 0.0404 \| 0.1507 \| 0.7693 \| - \|
	\| 0.9512 \| 760 \| 0.0141 \| 0.1369 \| 0.7723 \| - \|
	\| 0.9762 \| 780 \| 0.0513 \| 0.1555 \| 0.7687 \| - \|
	\| 1.0013 \| 800 \| 0.0387 \| 0.1306 \| 0.7717 \| - \|
	\| 1.0263 \| 820 \| 0.0393 \| 0.1420 \| 0.7707 \| - \|
	\| 1.0513 \| 840 \| 0.0153 \| 0.1656 \| 0.7700 \| - \|
	\| 1.0763 \| 860 \| 0.0263 \| 0.1525 \| 0.7694 \| - \|
	\| 1.1014 \| 880 \| 0.0503 \| 0.1947 \| 0.7638 \| - \|
	\| 1.1264 \| 900 \| 0.0215 \| 0.2202 \| 0.7615 \| - \|
	\| 1.1514 \| 920 \| 0.0217 \| 0.1542 \| 0.7696 \| - \|
	\| 1.1765 \| 940 \| 0.007 \| 0.1394 \| 0.7713 \| - \|
	\| 1.2015 \| 960 \| 0.018 \| 0.1573 \| 0.7706 \| - \|
	\| 1.2265 \| 980 \| 0.0446 \| 0.1504 \| 0.7686 \| - \|
	\| 1.2516 \| 1000 \| 0.026 \| 0.1573 \| 0.7661 \| - \|
	\| 1.2766 \| 1020 \| 0.0098 \| 0.1429 \| 0.7683 \| - \|
	\| 1.3016 \| 1040 \| 0.0196 \| 0.1374 \| 0.7702 \| - \|
	\| 1.3267 \| 1060 \| 0.021 \| 0.1594 \| 0.7685 \| - \|
	\| 1.3517 \| 1080 \| 0.0499 \| 0.1378 \| 0.7724 \| - \|
	\| 1.3767 \| 1100 \| 0.0165 \| 0.1335 \| 0.7729 \| - \|
	\| 1.4018 \| 1120 \| 0.0294 \| 0.1451 \| 0.7713 \| - \|
	\| 1.4268 \| 1140 \| 0.0114 \| 0.1338 \| 0.7717 \| - \|
	\| 1.4518 \| 1160 \| 0.0192 \| 0.1327 \| 0.7719 \| - \|
	\| 1.4768 \| 1180 \| 0.0335 \| 0.1618 \| 0.7646 \| - \|
	\| 1.5019 \| 1200 \| 0.0546 \| 0.1389 \| 0.7711 \| - \|
	\| 1.5269 \| 1220 \| 0.0069 \| 0.1239 \| 0.7738 \| - \|
	\| 1.5519 \| 1240 \| 0.0094 \| 0.1180 \| 0.7739 \| - \|
	\| 1.5770 \| 1260 \| 0.0074 \| 0.1238 \| 0.7733 \| - \|
	\| 1.6020 \| 1280 \| 0.0557 \| 0.1428 \| 0.7720 \| - \|
	\| 1.6270 \| 1300 \| 0.056 \| 0.1159 \| 0.7751 \| - \|
	\| 1.6521 \| 1320 \| 0.0 \| 0.1244 \| 0.7758 \| - \|
	\| 1.6771 \| 1340 \| 0.0066 \| 0.1185 \| 0.7735 \| - \|
	\| 1.7021 \| 1360 \| 0.0178 \| 0.1016 \| 0.7757 \| - \|
	\| 1.7272 \| 1380 \| 0.0156 \| 0.0939 \| 0.7776 \| - \|
	\| 1.7522 \| 1400 \| 0.0 \| 0.1138 \| 0.7761 \| - \|
	\| 1.7772 \| 1420 \| 0.0436 \| 0.0980 \| 0.7775 \| - \|
	\| 1.8023 \| 1440 \| 0.0626 \| 0.1096 \| 0.7763 \| - \|
	\| 1.8273 \| 1460 \| 0.0222 \| 0.0968 \| 0.7774 \| - \|
	\| 1.8523 \| 1480 \| 0.0101 \| 0.1021 \| 0.7762 \| - \|
	\| 1.8773 \| 1500 \| 0.0171 \| 0.1076 \| 0.7754 \| - \|
	\| 1.9024 \| 1520 \| 0.0064 \| 0.1279 \| 0.7730 \| - \|
	\| 1.9274 \| 1540 \| 0.0068 \| 0.1237 \| 0.7729 \| - \|
	\| 1.9524 \| 1560 \| 0.0066 \| 0.1229 \| 0.7733 \| - \|
	\| 1.9775 \| 1580 \| 0.0 \| 0.1263 \| 0.7731 \| - \|
	\| 2.0025 \| 1600 \| 0.0065 \| 0.1152 \| 0.7746 \| - \|
	\| 2.0275 \| 1620 \| 0.0147 \| 0.1021 \| 0.7773 \| - \|
	\| 2.0526 \| 1640 \| 0.0 \| 0.1021 \| 0.7773 \| - \|
	\| 2.0776 \| 1660 \| 0.0209 \| 0.1017 \| 0.7774 \| - \|
	\| 2.1026 \| 1680 \| 0.0 \| 0.0993 \| 0.7773 \| - \|
	\| 2.1277 \| 1700 \| 0.0067 \| 0.0922 \| 0.7784 \| - \|
	\| 2.1527 \| 1720 \| 0.0333 \| 0.1158 \| 0.7749 \| - \|
	\| 2.1777 \| 1740 \| 0.0 \| 0.1397 \| 0.7721 \| - \|
	\| 2.2028 \| 1760 \| 0.0158 \| 0.1248 \| 0.7751 \| - \|
	\| 2.2278 \| 1780 \| 0.0201 \| 0.1021 \| 0.7767 \| - \|
	\| 2.2528 \| 1800 \| 0.0 \| 0.1029 \| 0.7768 \| - \|
	\| 2.2778 \| 1820 \| 0.0107 \| 0.1007 \| 0.7767 \| - \|
	\| 2.3029 \| 1840 \| 0.0156 \| 0.0923 \| 0.7767 \| - \|
	\| 2.3279 \| 1860 \| 0.0 \| 0.1012 \| 0.7754 \| - \|
	\| 2.3529 \| 1880 \| 0.0131 \| 0.1184 \| 0.7731 \| - \|
	\| 2.3780 \| 1900 \| 0.0072 \| 0.1113 \| 0.7752 \| - \|
	\| 2.4030 \| 1920 \| 0.0337 \| 0.0952 \| 0.7775 \| - \|
	\| 2.4280 \| 1940 \| 0.0068 \| 0.1086 \| 0.7754 \| - \|
	\| 2.4531 \| 1960 \| 0.0 \| 0.1194 \| 0.7740 \| - \|
	\| 2.4781 \| 1980 \| 0.0176 \| 0.1184 \| 0.7747 \| - \|
	\| 2.5031 \| 2000 \| 0.0188 \| 0.1123 \| 0.7745 \| - \|
	\| 2.5282 \| 2020 \| 0.0 \| 0.1138 \| 0.7742 \| - \|
	\| 2.5532 \| 2040 \| 0.0 \| 0.1141 \| 0.7742 \| - \|
	\| 2.5782 \| 2060 \| 0.0269 \| 0.1126 \| 0.7743 \| - \|
	\| 2.6033 \| 2080 \| 0.0193 \| 0.1470 \| 0.7707 \| - \|
	\| 2.6283 \| 2100 \| 0.0074 \| 0.1333 \| 0.7726 \| - \|
	\| 2.6533 \| 2120 \| 0.0253 \| 0.1004 \| 0.7756 \| - \|
	\| 2.6783 \| 2140 \| 0.0 \| 0.0980 \| 0.7758 \| - \|
	\| 2.7034 \| 2160 \| 0.0 \| 0.0984 \| 0.7758 \| - \|
	\| 2.7284 \| 2180 \| 0.0 \| 0.0984 \| 0.7758 \| - \|
	\| 2.7534 \| 2200 \| 0.0 \| 0.0984 \| 0.7758 \| - \|
	\| 2.7785 \| 2220 \| 0.007 \| 0.0971 \| 0.7766 \| - \|
	\| 2.8035 \| 2240 \| 0.0 \| 0.0998 \| 0.7766 \| - \|
	\| 2.8285 \| 2260 \| 0.015 \| 0.0988 \| 0.7760 \| - \|
	\| 2.8536 \| 2280 \| 0.0 \| 0.1020 \| 0.7757 \| - \|
	\| 2.8786 \| 2300 \| 0.0 \| 0.1023 \| 0.7756 \| - \|
	\| 2.9036 \| 2320 \| 0.0 \| 0.1023 \| 0.7756 \| - \|
	\| 2.9287 \| 2340 \| 0.0 \| 0.1023 \| 0.7756 \| - \|
	\| 2.9537 \| 2360 \| 0.0075 \| 0.1043 \| 0.7751 \| - \|
	\| 2.9787 \| 2380 \| 0.0067 \| 0.1125 \| 0.7749 \| - \|
	\| 3.0038 \| 2400 \| 0.0 \| 0.1083 \| 0.7752 \| - \|
	\| 3.0288 \| 2420 \| 0.0 \| 0.1083 \| 0.7752 \| - \|
	\| 3.0538 \| 2440 \| 0.0 \| 0.1083 \| 0.7752 \| - \|
	\| 3.0788 \| 2460 \| 0.0063 \| 0.1018 \| 0.7755 \| - \|
	\| 3.1039 \| 2480 \| 0.0 \| 0.1012 \| 0.7756 \| - \|
	\| 3.1289 \| 2500 \| 0.0162 \| 0.092 \| 0.7768 \| - \|
	\| 3.1539 \| 2520 \| 0.01 \| 0.0941 \| 0.7768 \| - \|
	\| 3.1790 \| 2540 \| 0.0069 \| 0.0946 \| 0.7761 \| - \|
	\| 3.2040 \| 2560 \| 0.0 \| 0.0956 \| 0.7759 \| - \|
	\| 3.2290 \| 2580 \| 0.0 \| 0.0956 \| 0.7758 \| - \|
	\| 3.2541 \| 2600 \| 0.0 \| 0.0956 \| 0.7758 \| - \|
	\| 3.2791 \| 2620 \| 0.0 \| 0.0956 \| 0.7758 \| - \|
	\| 3.3041 \| 2640 \| 0.0131 \| 0.0981 \| 0.7756 \| - \|
	\| 3.3292 \| 2660 \| 0.0195 \| 0.1142 \| 0.7748 \| - \|
	\| 3.3542 \| 2680 \| 0.0 \| 0.1172 \| 0.7746 \| - \|
	\| 3.3792 \| 2700 \| 0.0065 \| 0.1186 \| 0.7748 \| - \|
	\| 3.4043 \| 2720 \| 0.0169 \| 0.1184 \| 0.7750 \| - \|
	\| 3.4293 \| 2740 \| 0.0 \| 0.1175 \| 0.7749 \| - \|
	\| 3.4543 \| 2760 \| 0.0 \| 0.1165 \| 0.7748 \| - \|
	\| 3.4793 \| 2780 \| 0.0105 \| 0.1173 \| 0.7747 \| - \|
	\| 3.5044 \| 2800 \| 0.0066 \| 0.1123 \| 0.7751 \| - \|
	\| 3.5294 \| 2820 \| 0.0 \| 0.1103 \| 0.7753 \| - \|
	\| 3.5544 \| 2840 \| 0.0 \| 0.1106 \| 0.7753 \| - \|
	\| 3.5795 \| 2860 \| 0.0139 \| 0.1158 \| 0.7745 \| - \|
	\| 3.6045 \| 2880 \| 0.0 \| 0.1183 \| 0.7741 \| - \|
	\| 3.6295 \| 2900 \| 0.0 \| 0.1181 \| 0.7741 \| - \|
	\| 3.6546 \| 2920 \| 0.0 \| 0.1179 \| 0.7741 \| - \|
	\| 3.6796 \| 2940 \| 0.0 \| 0.1179 \| 0.7741 \| - \|
	\| 3.7046 \| 2960 \| 0.0119 \| 0.1172 \| 0.7742 \| - \|
	\| 3.7297 \| 2980 \| 0.0068 \| 0.1183 \| 0.7742 \| - \|
	\| 3.7547 \| 3000 \| 0.0 \| 0.1193 \| 0.7741 \| - \|
	\| 3.7797 \| 3020 \| 0.0 \| 0.1193 \| 0.7741 \| - \|
	\| 3.8048 \| 3040 \| 0.0 \| 0.1193 \| 0.7741 \| - \|
	\| 3.8298 \| 3060 \| 0.0 \| 0.1191 \| 0.7741 \| - \|
	\| 3.8548 \| 3080 \| 0.0 \| 0.1193 \| 0.7741 \| - \|
	\| 3.8798 \| 3100 \| 0.0 \| 0.1193 \| 0.7741 \| - \|
	\| 3.9049 \| 3120 \| 0.0131 \| 0.1165 \| 0.7745 \| - \|
	\| 3.9299 \| 3140 \| 0.0 \| 0.1159 \| 0.7745 \| - \|
	\| 3.9549 \| 3160 \| 0.0 \| 0.1158 \| 0.7746 \| - \|
	\| 3.9800 \| 3180 \| 0.0 \| 0.1153 \| 0.7746 \| - \|
	\| -1 \| -1 \| - \| - \| - \| 0.7768 \|

	* The bold row denotes the saved checkpoint.
	</details>

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.4.0
	- Transformers: 4.48.1
	- PyTorch: 2.5.1+cu124
	- Accelerate: 1.3.0
	- 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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