Add new SentenceTransformer model

26c4693 verified 3 months ago

36.6 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:73392
	- loss:CosineSimilarityLoss
	base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
	widget:
	- source_sentence: Berapa persen kenaikan Indeks Harga Perdagangan Besar (IHPB) Umum
	Nasional pada bulan April 2021?
	sentences:
	- Statistik Kriminal 2023
	- Ekonomi Indonesia Triwulan I-2021 turun 0,74 persen (y-on-y)
	- Survei Biaya Hidup (SBH) 2018 Ambon dan Tual
	- source_sentence: Usaha pertanian sampingan di Indonesia tahun 2022
	sentences:
	- Analisis Hasil Survei Dampak Covid-19 Terhadap Pelaku Usaha
	- Direktori Usaha Pertanian Lainnya 2022
	- EksporImpor September 2018
	- source_sentence: Pertumbuhan industri Indonesia 2006-2009
	sentences:
	- Pertumbuhan Produksi IBS Triwulan III 2019 Naik 4,35 Persen
	- Indikator Ekonomi April 2000
	- Perkembangan Indeks Produksi Industri Besar dan Sedang 2006 - 2009
	- source_sentence: 'Sensus ekonomi Kalbar 2016: data usaha'
	sentences:
	- Pertumbuhan ekonomi Indonesia tahun 2022
	- Buletin Statistik Perdagangan Luar Negeri Impor November 2017
	- Data jumlah wisatawan mancanegara 2019
	- source_sentence: Direktori perusahaan pengelola hutan 2015
	sentences:
	- Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan
	Negara, April 2017
	- Direktori Perusahaan Kehutanan 2015
	- Indeks Pembangunan Manusia (IPM) Indonesia tahun 2024 mencapai 75,02, meningkat
	0,63 poin atau 0,85 persen dibandingkan tahun sebelumnya yang sebesar 74,39.
	datasets:
	- yahyaabd/bps-semantic-pairs-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 mpnet v1 eval
	type: allstats-semantic-mpnet-v1-eval
	metrics:
	- type: pearson_cosine
	value: 0.9721680353379998
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8769707416598509
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: allstat semantic mpnet v1 test
	type: allstat-semantic-mpnet-v1-test
	metrics:
	- type: pearson_cosine
	value: 0.9714701009323166
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8696530606326947
	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-semantic-pairs-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-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:
	- [bps-semantic-pairs-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-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-mpnet-v1")
	# Run inference
	sentences = [
	'Direktori perusahaan pengelola hutan 2015',
	'Direktori Perusahaan Kehutanan 2015',
	'Indeks Pembangunan Manusia (IPM) Indonesia tahun 2024 mencapai 75,02, meningkat 0,63 poin atau 0,85 persen dibandingkan tahun sebelumnya yang sebesar 74,39.',
	]
	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-mpnet-v1-eval` and `allstat-semantic-mpnet-v1-test`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| allstats-semantic-mpnet-v1-eval \| allstat-semantic-mpnet-v1-test \|
	\|:--------------------\|:--------------------------------\|:-------------------------------\|
	\| pearson_cosine \| 0.9722 \| 0.9715 \|
	\| spearman_cosine \| 0.877 \| 0.8697 \|

	<!--
	## 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-semantic-pairs-synthetic-dataset-v1

	* Dataset: [bps-semantic-pairs-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-synthetic-dataset-v1) at [6656af9](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-synthetic-dataset-v1/tree/6656af9b517b88dc1445ccd85e5fa78bd07b08d1)
	* Size: 73,392 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: 11.28 tokens</li><li>max: 34 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 14.71 tokens</li><li>max: 58 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:-----------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------\|
	\| <code>Data bisnis Kalbar sensus 2016</code> \| <code>Indikator Ekonomi Oktober 2012</code> \| <code>0.1</code> \|
	\| <code>Informasi tentang pola pengeluaran masyarakat Bengkulu berdasarkan kelompok pendapatan?</code> \| <code>Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Bengkulu, 2018-2023</code> \| <code>0.88</code> \|
	\| <code>Laopran keuagnan lmebaga non proft 20112-013</code> \| <code>Neraca Lembaga Non Profit yang Melayani Rumah Tangga 2011-2013</code> \| <code>0.93</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-semantic-pairs-synthetic-dataset-v1

	* Dataset: [bps-semantic-pairs-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-synthetic-dataset-v1) at [6656af9](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-synthetic-dataset-v1/tree/6656af9b517b88dc1445ccd85e5fa78bd07b08d1)
	* Size: 15,726 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: 4 tokens</li><li>mean: 11.52 tokens</li><li>max: 37 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 14.38 tokens</li><li>max: 61 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:-----------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------\|:------------------\|
	\| <code>Data transportasi bulan Februari 2021</code> \| <code>Tenaga Kerja Februari 2023</code> \| <code>0.08</code> \|
	\| <code>Sebear berspa prrsen eknaikan Inseks Hraga Predagangan eBsar (IHB) Umym Nasiona di aMret 202?</code> \| <code>Maret 2020, Indeks Harga Perdagangan Besar (IHPB) Umum Nasional naik 0,10 persen</code> \| <code>1.0</code> \|
	\| <code>Data ekspor dan moda transportasi tahun 2018-2019</code> \| <code>Indikator Pasar Tenaga Kerja Indonesia Agustus 2012</code> \| <code>0.08</code> \|
	* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
	```json
	{
	"loss_fct": "torch.nn.modules.loss.MSELoss"
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `num_train_epochs`: 24
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `dataloader_num_workers`: 4
	- `load_best_model_at_end`: True
	- `label_smoothing_factor`: 0.01
	- `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`: 64
	- `per_device_eval_batch_size`: 64
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 5e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 24
	- `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`: 4
	- `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`: 0.01
	- `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-mpnet-v1-eval_spearman_cosine \| allstat-semantic-mpnet-v1-test_spearman_cosine \|
	\|:-----------:\|:---------:\|:-------------:\|:---------------:\|:-----------------------------------------------:\|:----------------------------------------------:\|
	\| 0 \| 0 \| - \| 0.1031 \| 0.6244 \| - \|
	\| 0.2180 \| 250 \| 0.064 \| 0.0413 \| 0.6958 \| - \|
	\| 0.4359 \| 500 \| 0.0381 \| 0.0305 \| 0.7301 \| - \|
	\| 0.6539 \| 750 \| 0.0284 \| 0.0243 \| 0.7651 \| - \|
	\| 0.8718 \| 1000 \| 0.025 \| 0.0213 \| 0.7656 \| - \|
	\| 1.0898 \| 1250 \| 0.0207 \| 0.0201 \| 0.7822 \| - \|
	\| 1.3078 \| 1500 \| 0.0188 \| 0.0194 \| 0.7805 \| - \|
	\| 1.5257 \| 1750 \| 0.0182 \| 0.0177 \| 0.7918 \| - \|
	\| 1.7437 \| 2000 \| 0.0177 \| 0.0168 \| 0.8098 \| - \|
	\| 1.9616 \| 2250 \| 0.0173 \| 0.0173 \| 0.7979 \| - \|
	\| 2.1796 \| 2500 \| 0.0151 \| 0.0174 \| 0.8010 \| - \|
	\| 2.3976 \| 2750 \| 0.014 \| 0.0163 \| 0.8005 \| - \|
	\| 2.6155 \| 3000 \| 0.0142 \| 0.0159 \| 0.8027 \| - \|
	\| 2.8335 \| 3250 \| 0.0137 \| 0.0154 \| 0.8074 \| - \|
	\| 3.0514 \| 3500 \| 0.013 \| 0.0146 \| 0.8173 \| - \|
	\| 3.2694 \| 3750 \| 0.0099 \| 0.0138 \| 0.8179 \| - \|
	\| 3.4874 \| 4000 \| 0.0105 \| 0.0135 \| 0.8138 \| - \|
	\| 3.7053 \| 4250 \| 0.0109 \| 0.0145 \| 0.8138 \| - \|
	\| 3.9233 \| 4500 \| 0.011 \| 0.0145 \| 0.8244 \| - \|
	\| 4.1412 \| 4750 \| 0.0086 \| 0.0132 \| 0.8327 \| - \|
	\| 4.3592 \| 5000 \| 0.0077 \| 0.0129 \| 0.8307 \| - \|
	\| 4.5772 \| 5250 \| 0.0081 \| 0.0124 \| 0.8380 \| - \|
	\| 4.7951 \| 5500 \| 0.0087 \| 0.0128 \| 0.8358 \| - \|
	\| 5.0131 \| 5750 \| 0.0076 \| 0.0135 \| 0.8280 \| - \|
	\| 5.2310 \| 6000 \| 0.0061 \| 0.0122 \| 0.8399 \| - \|
	\| 5.4490 \| 6250 \| 0.0062 \| 0.0119 \| 0.8344 \| - \|
	\| 5.6670 \| 6500 \| 0.007 \| 0.0113 \| 0.8432 \| - \|
	\| 5.8849 \| 6750 \| 0.0069 \| 0.0117 \| 0.8353 \| - \|
	\| 6.1029 \| 7000 \| 0.0056 \| 0.0117 \| 0.8333 \| - \|
	\| 6.3208 \| 7250 \| 0.0047 \| 0.0114 \| 0.8438 \| - \|
	\| 6.5388 \| 7500 \| 0.0059 \| 0.0114 \| 0.8429 \| - \|
	\| 6.7568 \| 7750 \| 0.0054 \| 0.0113 \| 0.8452 \| - \|
	\| 6.9747 \| 8000 \| 0.0059 \| 0.0118 \| 0.8477 \| - \|
	\| 7.1927 \| 8250 \| 0.0045 \| 0.0109 \| 0.8474 \| - \|
	\| 7.4106 \| 8500 \| 0.0042 \| 0.0111 \| 0.8532 \| - \|
	\| 7.6286 \| 8750 \| 0.0045 \| 0.0114 \| 0.8385 \| - \|
	\| 7.8466 \| 9000 \| 0.005 \| 0.0111 \| 0.8502 \| - \|
	\| 8.0645 \| 9250 \| 0.0045 \| 0.0111 \| 0.8496 \| - \|
	\| 8.2825 \| 9500 \| 0.0035 \| 0.0109 \| 0.8490 \| - \|
	\| 8.5004 \| 9750 \| 0.0038 \| 0.0112 \| 0.8519 \| - \|
	\| 8.7184 \| 10000 \| 0.0038 \| 0.0112 \| 0.8463 \| - \|
	\| 8.9364 \| 10250 \| 0.0039 \| 0.0109 \| 0.8556 \| - \|
	\| 9.1543 \| 10500 \| 0.0035 \| 0.0110 \| 0.8534 \| - \|
	\| 9.3723 \| 10750 \| 0.003 \| 0.0111 \| 0.8525 \| - \|
	\| 9.5902 \| 11000 \| 0.0039 \| 0.0108 \| 0.8593 \| - \|
	\| 9.8082 \| 11250 \| 0.0038 \| 0.0112 \| 0.8537 \| - \|
	\| 10.0262 \| 11500 \| 0.0033 \| 0.0108 \| 0.8553 \| - \|
	\| 10.2441 \| 11750 \| 0.0023 \| 0.0104 \| 0.8601 \| - \|
	\| 10.4621 \| 12000 \| 0.0025 \| 0.0104 \| 0.8571 \| - \|
	\| 10.6800 \| 12250 \| 0.0026 \| 0.0106 \| 0.8594 \| - \|
	\| 10.8980 \| 12500 \| 0.0026 \| 0.0106 \| 0.8627 \| - \|
	\| 11.1160 \| 12750 \| 0.0024 \| 0.0105 \| 0.8623 \| - \|
	\| 11.3339 \| 13000 \| 0.002 \| 0.0104 \| 0.8614 \| - \|
	\| 11.5519 \| 13250 \| 0.0021 \| 0.0103 \| 0.8622 \| - \|
	\| 11.7698 \| 13500 \| 0.0025 \| 0.0106 \| 0.8580 \| - \|
	\| 11.9878 \| 13750 \| 0.0023 \| 0.0108 \| 0.8613 \| - \|
	\| 12.2058 \| 14000 \| 0.0019 \| 0.0106 \| 0.8618 \| - \|
	\| 12.4237 \| 14250 \| 0.0017 \| 0.0104 \| 0.8641 \| - \|
	\| 12.6417 \| 14500 \| 0.0019 \| 0.0103 \| 0.8620 \| - \|
	\| 12.8596 \| 14750 \| 0.002 \| 0.0104 \| 0.8649 \| - \|
	\| 13.0776 \| 15000 \| 0.002 \| 0.0102 \| 0.8620 \| - \|
	\| 13.2956 \| 15250 \| 0.0014 \| 0.0103 \| 0.8631 \| - \|
	\| 13.5135 \| 15500 \| 0.0018 \| 0.0104 \| 0.8635 \| - \|
	\| 13.7315 \| 15750 \| 0.0018 \| 0.0102 \| 0.8661 \| - \|
	\| 13.9494 \| 16000 \| 0.0018 \| 0.0104 \| 0.8683 \| - \|
	\| 14.1674 \| 16250 \| 0.0014 \| 0.0104 \| 0.8691 \| - \|
	\| 14.3854 \| 16500 \| 0.0014 \| 0.0103 \| 0.8668 \| - \|
	\| 14.6033 \| 16750 \| 0.0015 \| 0.0102 \| 0.8673 \| - \|
	\| 14.8213 \| 17000 \| 0.0016 \| 0.0102 \| 0.8679 \| - \|
	\| 15.0392 \| 17250 \| 0.0016 \| 0.0101 \| 0.8688 \| - \|
	\| 15.2572 \| 17500 \| 0.0012 \| 0.0102 \| 0.8676 \| - \|
	\| 15.4752 \| 17750 \| 0.0012 \| 0.0102 \| 0.8712 \| - \|
	\| 15.6931 \| 18000 \| 0.0014 \| 0.0102 \| 0.8702 \| - \|
	\| 15.9111 \| 18250 \| 0.0013 \| 0.0101 \| 0.8718 \| - \|
	\| 16.1290 \| 18500 \| 0.0011 \| 0.0100 \| 0.8727 \| - \|
	\| 16.3470 \| 18750 \| 0.001 \| 0.0101 \| 0.8729 \| - \|
	\| 16.5650 \| 19000 \| 0.0012 \| 0.0099 \| 0.8714 \| - \|
	\| 16.7829 \| 19250 \| 0.0011 \| 0.0101 \| 0.8723 \| - \|
	\| 17.0009 \| 19500 \| 0.0012 \| 0.0101 \| 0.8679 \| - \|
	\| 17.2188 \| 19750 \| 0.0009 \| 0.0103 \| 0.8706 \| - \|
	\| 17.4368 \| 20000 \| 0.0009 \| 0.0101 \| 0.8722 \| - \|
	\| 17.6548 \| 20250 \| 0.0009 \| 0.0100 \| 0.8710 \| - \|
	\| 17.8727 \| 20500 \| 0.001 \| 0.0101 \| 0.8719 \| - \|
	\| 18.0907 \| 20750 \| 0.0009 \| 0.0100 \| 0.8728 \| - \|
	\| 18.3086 \| 21000 \| 0.0009 \| 0.0100 \| 0.8738 \| - \|
	\| 18.5266 \| 21250 \| 0.0008 \| 0.0100 \| 0.8720 \| - \|
	\| 18.7446 \| 21500 \| 0.0009 \| 0.0100 \| 0.8731 \| - \|
	\| 18.9625 \| 21750 \| 0.0009 \| 0.0098 \| 0.8738 \| - \|
	\| 19.1805 \| 22000 \| 0.0007 \| 0.0100 \| 0.8750 \| - \|
	\| 19.3984 \| 22250 \| 0.0007 \| 0.0099 \| 0.8730 \| - \|
	\| 19.6164 \| 22500 \| 0.0007 \| 0.0100 \| 0.8753 \| - \|
	\| 19.8344 \| 22750 \| 0.0007 \| 0.0099 \| 0.8753 \| - \|
	\| 20.0523 \| 23000 \| 0.0008 \| 0.0100 \| 0.8755 \| - \|
	\| 20.2703 \| 23250 \| 0.0006 \| 0.0100 \| 0.8747 \| - \|
	\| 20.4882 \| 23500 \| 0.0006 \| 0.0101 \| 0.8753 \| - \|
	\| 20.7062 \| 23750 \| 0.0007 \| 0.0101 \| 0.8738 \| - \|
	\| 20.9241 \| 24000 \| 0.0007 \| 0.0101 \| 0.8750 \| - \|
	\| 21.1421 \| 24250 \| 0.0006 \| 0.0101 \| 0.8760 \| - \|
	\| 21.3601 \| 24500 \| 0.0006 \| 0.0101 \| 0.8753 \| - \|
	\| 21.5780 \| 24750 \| 0.0006 \| 0.0101 \| 0.8759 \| - \|
	\| 21.7960 \| 25000 \| 0.0006 \| 0.0100 \| 0.8759 \| - \|
	\| 22.0139 \| 25250 \| 0.0006 \| 0.0100 \| 0.8762 \| - \|
	\| 22.2319 \| 25500 \| 0.0005 \| 0.0100 \| 0.8767 \| - \|
	\| 22.4499 \| 25750 \| 0.0005 \| 0.0100 \| 0.8772 \| - \|
	\| 22.6678 \| 26000 \| 0.0005 \| 0.0099 \| 0.8771 \| - \|
	\| 22.8858 \| 26250 \| 0.0005 \| 0.0100 \| 0.8769 \| - \|
	\| 23.1037 \| 26500 \| 0.0005 \| 0.0100 \| 0.8770 \| - \|
	\| 23.3217 \| 26750 \| 0.0005 \| 0.0100 \| 0.8769 \| - \|
	\| 23.5397 \| 27000 \| 0.0004 \| 0.0100 \| 0.8769 \| - \|
	\| 23.7576 \| 27250 \| 0.0005 \| 0.0100 \| 0.8769 \| - \|
	\| 23.9756 \| 27500 \| 0.0005 \| 0.0100 \| 0.8770 \| - \|
	\| 24.0 \| 27528 \| - \| - \| - \| 0.8697 \|

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

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.3.1
	- Transformers: 4.47.1
	- PyTorch: 2.5.1+cu124
	- 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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