Add new SentenceTransformer model

ed767c0 verified 21 days ago

31.9 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:25580
	- loss:OnlineContrastiveLoss
	base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	widget:
	- source_sentence: ikhtisar arus kas triwulan 1, 2004 (miliar)
	sentences:
	- Balita (0-59 Bulan) Menurut Status Gizi, Tahun 1998-2005
	- Perbandingan Indeks dan Tingkat Inflasi Desember 2023 Kota-kota di Luar Pulau
	Jawa dan Sumatera dengan Nasional (2018=100)
	- Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan
	dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Tengah, 2018-2023
	- source_sentence: BaIgaimana gambaran neraca arus dana dUi Indonesia pada kuartal
	kedua tahun 2015?
	sentences:
	- Jumlah Sekolah, Guru, dan Murid Sekolah Menengah Pertama (SMP) di Bawah Kementrian
	Pendidikan dan Kebudayaan Menurut Provinsi 2011/2012-2015/2016
	- Ringkasan Neraca Arus Dana Triwulan III Tahun 2003 (Miliar Rupiah)
	- Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan
	dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Tenggara, 2018-2023
	- source_sentence: Berapa persen pengeluaran orang di kotaa untuk makanan vs non-makanan,
	per provinsi, 2018?
	sentences:
	- Ekspor Tanaman Obat, Aromatik, dan Rempah-Rempah menurut Negara Tujuan Utama,
	2012-2023
	- Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Pendidikan Tertinggi
	yang Ditamatkan (ribu rupiah), 2017
	- IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor),
	1996-2014 (1996=100)
	- source_sentence: Negara-negara asal impor crude oil dan produk turunannya tahun
	2002-2023
	sentences:
	- Persentase Pengeluaran Rata-rata per Kapita Sebulan Menurut Kelompok Barang, Indonesia,
	1999, 2002-2023
	- Rata-rata Pendapatan Bersih Berusaha Sendiri menurut Provinsi dan Pendidikan yang
	Ditamatkan (ribu rupiah), 2016
	- Perkembangan Beberapa Agregat Pendapatan dan Pendapatan per Kapita Atas Dasar
	Harga Berlaku, 2010-2016
	- source_sentence: Arus dana Q3 2006
	sentences:
	- Posisi Simpanan Berjangka Rupiah pada Bank Umum dan BPR Menurut Golongan Pemilik
	(miliar rupiah), 2005-2018
	- Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah)
	- Rata-Rata Pengeluaran per Kapita Sebulan di Daerah Perkotaan Menurut Kelompok
	Barang dan Golongan Pengeluaran per Kapita Sebulan, 2000-2012
	datasets:
	- yahyaabd/query-hard-pos-neg-doc-pairs-statictable
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy
	- cosine_accuracy_threshold
	- cosine_f1
	- cosine_f1_threshold
	- cosine_precision
	- cosine_recall
	- cosine_ap
	- cosine_mcc
	model-index:
	- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	results:
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: allstats semantic mini v1 test
	type: allstats-semantic-mini-v1_test
	metrics:
	- type: cosine_accuracy
	value: 0.9739003467786093
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.7543691396713257
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 0.9601560323209808
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.7539516091346741
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 0.9498346196251378
	name: Cosine Precision
	- type: cosine_recall
	value: 0.9707042253521126
	name: Cosine Recall
	- type: cosine_ap
	value: 0.9914629836831814
	name: Cosine Ap
	- type: cosine_mcc
	value: 0.9408766527185352
	name: Cosine Mcc
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: allstats semantic mini v1 dev
	type: allstats-semantic-mini-v1_dev
	metrics:
	- type: cosine_accuracy
	value: 0.9695199853987954
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.7802088856697083
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 0.9531511433351924
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.7691957950592041
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 0.943677526228603
	name: Cosine Precision
	- type: cosine_recall
	value: 0.9628169014084507
	name: Cosine Recall
	- type: cosine_ap
	value: 0.9911428464355772
	name: Cosine Ap
	- type: cosine_mcc
	value: 0.9304692189028425
	name: Cosine Mcc
	---

	# 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 [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-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:
	- [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': 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-search-miniLM-v1-4")
	# Run inference
	sentences = [
	'Arus dana Q3 2006',
	'Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah)',
	'Rata-Rata Pengeluaran per Kapita Sebulan di Daerah Perkotaan Menurut Kelompok Barang dan Golongan Pengeluaran per Kapita Sebulan, 2000-2012',
	]
	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

	#### Binary Classification

	* Datasets: `allstats-semantic-mini-v1_test` and `allstats-semantic-mini-v1_dev`
	* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

	\| Metric \| allstats-semantic-mini-v1_test \| allstats-semantic-mini-v1_dev \|
	\|:--------------------------\|:-------------------------------\|:------------------------------\|
	\| cosine_accuracy \| 0.9739 \| 0.9695 \|
	\| cosine_accuracy_threshold \| 0.7544 \| 0.7802 \|
	\| cosine_f1 \| 0.9602 \| 0.9532 \|
	\| cosine_f1_threshold \| 0.754 \| 0.7692 \|
	\| cosine_precision \| 0.9498 \| 0.9437 \|
	\| cosine_recall \| 0.9707 \| 0.9628 \|
	\| cosine_ap \| 0.9915 \| 0.9911 \|
	\| cosine_mcc \| 0.9409 \| 0.9305 \|

	<!--
	## 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 [7b28b96](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/7b28b964daa3073a4d012d1ffca46ecd4f26bb5f)
	* Size: 25,580 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: 7 tokens</li><li>mean: 20.14 tokens</li><li>max: 55 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 24.9 tokens</li><li>max: 47 tokens</li></ul> \| <ul><li>0: ~70.80%</li><li>1: ~29.20%</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:-------------------------------------------------------------------------\|:----------------------------------------------\|:---------------\|
	\| <code>Status pekerjaan utama penduduk usia 15+ yang bekerja, 2020</code> \| <code>Jumlah Penghuni Lapas per Kanwil</code> \| <code>0</code> \|
	\| <code>status pekerjaan utama penduduk usia 15+ yang bekerja, 2020</code> \| <code>Jumlah Penghuni Lapas per Kanwil</code> \| <code>0</code> \|
	\| <code>STATUS PEKERJAAN UTAMA PENDUDUK USIA 15+ YANG BEKERJA, 2020</code> \| <code>Jumlah Penghuni Lapas per Kanwil</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 [7b28b96](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/7b28b964daa3073a4d012d1ffca46ecd4f26bb5f)
	* Size: 5,479 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: 7 tokens</li><li>mean: 20.78 tokens</li><li>max: 52 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 26.28 tokens</li><li>max: 43 tokens</li></ul> \| <ul><li>0: ~71.50%</li><li>1: ~28.50%</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:-----------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------\|:---------------\|
	\| <code>Bagaimana perbandingan PNS pria dan wanita di berbagai golongan tahun 2014?</code> \| <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017</code> \| <code>0</code> \|
	\| <code>bagaimana perbandingan pns pria dan wanita di berbagai golongan tahun 2014?</code> \| <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017</code> \| <code>0</code> \|
	\| <code>BAGAIMANA PERBANDINGAN PNS PRIA DAN WANITA DI BERBAGAI GOLONGAN TAHUN 2014?</code> \| <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017</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`: 2
	- `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`: 2
	- `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
	\| Epoch \| Step \| Training Loss \| Validation Loss \| allstats-semantic-mini-v1_test_cosine_ap \| allstats-semantic-mini-v1_dev_cosine_ap \|
	\|:--------:\|:--------:\|:-------------:\|:---------------:\|:----------------------------------------:\|:---------------------------------------:\|
	\| -1 \| -1 \| - \| - \| 0.8789 \| - \|
	\| 0 \| 0 \| - \| 1.0484 \| - \| 0.8789 \|
	\| 0.025 \| 20 \| 0.9076 \| 0.7143 \| - \| 0.8976 \|
	\| 0.05 \| 40 \| 0.4666 \| 0.4744 \| - \| 0.9234 \|
	\| 0.075 \| 60 \| 0.4514 \| 0.3208 \| - \| 0.9542 \|
	\| 0.1 \| 80 \| 0.3153 \| 0.2520 \| - \| 0.9666 \|
	\| 0.125 \| 100 \| 0.1726 \| 0.2074 \| - \| 0.9725 \|
	\| 0.15 \| 120 \| 0.1056 \| 0.1860 \| - \| 0.9750 \|
	\| 0.175 \| 140 \| 0.1414 \| 0.2540 \| - \| 0.9674 \|
	\| 0.2 \| 160 \| 0.1091 \| 0.2077 \| - \| 0.9747 \|
	\| 0.225 \| 180 \| 0.108 \| 0.2333 \| - \| 0.9690 \|
	\| 0.25 \| 200 \| 0.1672 \| 0.1618 \| - \| 0.9771 \|
	\| 0.275 \| 220 \| 0.1086 \| 0.1804 \| - \| 0.9775 \|
	\| 0.3 \| 240 \| 0.083 \| 0.1805 \| - \| 0.9760 \|
	\| 0.325 \| 260 \| 0.083 \| 0.1674 \| - \| 0.9709 \|
	\| 0.35 \| 280 \| 0.1197 \| 0.1735 \| - \| 0.9734 \|
	\| 0.375 \| 300 \| 0.0811 \| 0.1272 \| - \| 0.9805 \|
	\| 0.4 \| 320 \| 0.049 \| 0.1491 \| - \| 0.9791 \|
	\| 0.425 \| 340 \| 0.0373 \| 0.1651 \| - \| 0.9721 \|
	\| 0.45 \| 360 \| 0.1116 \| 0.1742 \| - \| 0.9756 \|
	\| 0.475 \| 380 \| 0.0665 \| 0.1175 \| - \| 0.9837 \|
	\| 0.5 \| 400 \| 0.0698 \| 0.1165 \| - \| 0.9841 \|
	\| 0.525 \| 420 \| 0.1316 \| 0.1353 \| - \| 0.9817 \|
	\| 0.55 \| 440 \| 0.0753 \| 0.1276 \| - \| 0.9824 \|
	\| 0.575 \| 460 \| 0.0411 \| 0.1353 \| - \| 0.9801 \|
	\| 0.6 \| 480 \| 0.0099 \| 0.1292 \| - \| 0.9811 \|
	\| 0.625 \| 500 \| 0.0473 \| 0.1118 \| - \| 0.9836 \|
	\| 0.65 \| 520 \| 0.0201 \| 0.1083 \| - \| 0.9836 \|
	\| 0.675 \| 540 \| 0.0519 \| 0.1089 \| - \| 0.9856 \|
	\| 0.7 \| 560 \| 0.0652 \| 0.1003 \| - \| 0.9875 \|
	\| 0.725 \| 580 \| 0.0594 \| 0.1201 \| - \| 0.9872 \|
	\| 0.75 \| 600 \| 0.0536 \| 0.0896 \| - \| 0.9893 \|
	\| 0.775 \| 620 \| 0.0479 \| 0.0855 \| - \| 0.9874 \|
	\| 0.8 \| 640 \| 0.0301 \| 0.0948 \| - \| 0.9876 \|
	\| 0.825 \| 660 \| 0.014 \| 0.0993 \| - \| 0.9883 \|
	\| 0.85 \| 680 \| 0.0199 \| 0.0930 \| - \| 0.9884 \|
	\| 0.875 \| 700 \| 0.0375 \| 0.0765 \| - \| 0.9918 \|
	\| 0.9 \| 720 \| 0.0 \| 0.0805 \| - \| 0.9916 \|
	\| 0.925 \| 740 \| 0.0243 \| 0.0816 \| - \| 0.9916 \|
	\| 0.95 \| 760 \| 0.0209 \| 0.0935 \| - \| 0.9896 \|
	\| 0.975 \| 780 \| 0.02 \| 0.0831 \| - \| 0.9897 \|
	\| 1.0 \| 800 \| 0.0376 \| 0.0849 \| - \| 0.9890 \|
	\| 1.025 \| 820 \| 0.0113 \| 0.0960 \| - \| 0.9883 \|
	\| 1.05 \| 840 \| 0.01 \| 0.1131 \| - \| 0.9868 \|
	\| 1.075 \| 860 \| 0.0294 \| 0.1069 \| - \| 0.9861 \|
	\| 1.1 \| 880 \| 0.0367 \| 0.0921 \| - \| 0.9899 \|
	\| 1.125 \| 900 \| 0.0 \| 0.0910 \| - \| 0.9898 \|
	\| 1.15 \| 920 \| 0.0163 \| 0.1122 \| - \| 0.9871 \|
	\| 1.175 \| 940 \| 0.0072 \| 0.1204 \| - \| 0.9852 \|
	\| 1.2 \| 960 \| 0.0175 \| 0.1047 \| - \| 0.9872 \|
	\| 1.225 \| 980 \| 0.0065 \| 0.0992 \| - \| 0.9882 \|
	\| 1.25 \| 1000 \| 0.0104 \| 0.0932 \| - \| 0.9890 \|
	\| 1.275 \| 1020 \| 0.0281 \| 0.0866 \| - \| 0.9897 \|
	\| 1.3 \| 1040 \| 0.0169 \| 0.0874 \| - \| 0.9899 \|
	\| 1.325 \| 1060 \| 0.0069 \| 0.0910 \| - \| 0.9904 \|
	\| 1.35 \| 1080 \| 0.0 \| 0.0983 \| - \| 0.9898 \|
	\| 1.375 \| 1100 \| 0.0 \| 0.0985 \| - \| 0.9897 \|
	\| 1.4 \| 1120 \| 0.0146 \| 0.0919 \| - \| 0.9904 \|
	\| 1.425 \| 1140 \| 0.0075 \| 0.0852 \| - \| 0.9908 \|
	\| 1.45 \| 1160 \| 0.014 \| 0.0845 \| - \| 0.9908 \|
	\| 1.475 \| 1180 \| 0.0065 \| 0.0816 \| - \| 0.9907 \|
	\| 1.5 \| 1200 \| 0.0 \| 0.0811 \| - \| 0.9907 \|
	\| 1.525 \| 1220 \| 0.0103 \| 0.0785 \| - \| 0.9910 \|
	\| 1.55 \| 1240 \| 0.013 \| 0.0721 \| - \| 0.9915 \|
	\| 1.575 \| 1260 \| 0.0066 \| 0.0793 \| - \| 0.9910 \|
	\| 1.6 \| 1280 \| 0.0 \| 0.0810 \| - \| 0.9909 \|
	\| 1.625 \| 1300 \| 0.0239 \| 0.0803 \| - \| 0.9912 \|
	\| 1.65 \| 1320 \| 0.0155 \| 0.0816 \| - \| 0.9908 \|
	\| 1.675 \| 1340 \| 0.009 \| 0.0859 \| - \| 0.9904 \|
	\| 1.7 \| 1360 \| 0.0065 \| 0.0855 \| - \| 0.9900 \|
	\| 1.725 \| 1380 \| 0.0 \| 0.0866 \| - \| 0.9899 \|
	\| 1.75 \| 1400 \| 0.0127 \| 0.0865 \| - \| 0.9907 \|
	\| 1.775 \| 1420 \| 0.0064 \| 0.0819 \| - \| 0.9909 \|
	\| 1.8 \| 1440 \| 0.0 \| 0.0828 \| - \| 0.9910 \|
	\| 1.825 \| 1460 \| 0.0081 \| 0.0818 \| - \| 0.9912 \|
	\| 1.85 \| 1480 \| 0.0068 \| 0.0875 \| - \| 0.9909 \|
	\| 1.875 \| 1500 \| 0.0 \| 0.0886 \| - \| 0.9909 \|
	\| 1.9 \| 1520 \| 0.011 \| 0.0846 \| - \| 0.9911 \|
	\| 1.925 \| 1540 \| 0.0 \| 0.0843 \| - \| 0.9911 \|
	\| 1.95 \| 1560 \| 0.0 \| 0.0843 \| - \| 0.9911 \|
	\| 1.975 \| 1580 \| 0.0 \| 0.0843 \| - \| 0.9911 \|
	\| 2.0 \| 1600 \| 0.0162 \| 0.0850 \| - \| 0.9911 \|
	\| -1 \| -1 \| - \| - \| 0.9915 \| - \|

	* The bold row denotes the saved checkpoint.

	### 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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