Add new SentenceTransformer model

032378a verified 3 months ago

32.8 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:123637
	- loss:CosineSimilarityLoss
	base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
	widget:
	- source_sentence: Analisis biaya hidup di tiga kota Banten thn 2018
	sentences:
	- Indikator Konstruksi Triwulan I-2007
	- Survei Biaya Hidup (SBH) 2018 Bengkulu
	- Indikator Ekonomi Februari 2002
	- source_sentence: Grafik ekspor hasil minyak Indonesia ke berbagai negara dari tahun
	2000 hingga 2023.
	sentences:
	- Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 dalam Format SNA 1968 (65x65)
	- Harga Produsen Gabah dan Beras Januari 2020
	- Profil Usaha Konstruksi Perorangan Provinsi Papua 2016
	- source_sentence: Tren konstruksi Indonesia tahun 2007 Q4
	sentences:
	- Laporan Bulanan Data Sosial Ekonomi Desember 2018
	- Indeks Unit Value Ekspor Menurut Kode SITC Bulan Februari 2023
	- Inflasi Februari 2008 sebesar 0,5 persen
	- source_sentence: Informasi tentang kepemilikan dan penggunaan AC di rumah tangga
	Indonesia tahun 2013?
	sentences:
	- Data dan Informasi Kemiskinan Kabupaten/Kota Tahun 2014
	- Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur
	dan Jenis Pekerjaan, 2022-2023
	- Indikator Konstruksi, Triwulan II-2022
	- source_sentence: Statistik harga Ternate 2012
	sentences:
	- Statistik Perhubungan 2005
	- Indeks Unit Value Ekspor Menurut Kode SITC Bulan Januari 2019
	- Indikator Ekonomi Agustus 2002
	datasets:
	- yahyaabd/allstats-semantic-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 base v1 eval
	type: allstats-semantic-base-v1-eval
	metrics:
	- type: pearson_cosine
	value: 0.9868927327091045
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9277441071536588
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: allstat semantic base v1 test
	type: allstat-semantic-base-v1-test
	metrics:
	- type: pearson_cosine
	value: 0.9867639981224826
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9256998894451143
	name: Spearman Cosine
	---

	# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 -->
	- Maximum Sequence Length: 128 tokens
	- Output Dimensionality: 768 dimensions
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-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-base-v1-2")
	# Run inference
	sentences = [
	'Statistik harga Ternate 2012',
	'Indikator Ekonomi Agustus 2002',
	'Indeks Unit Value Ekspor Menurut Kode SITC Bulan Januari 2019',
	]
	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-base-v1-eval` and `allstat-semantic-base-v1-test`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| allstats-semantic-base-v1-eval \| allstat-semantic-base-v1-test \|
	\|:--------------------\|:-------------------------------\|:------------------------------\|
	\| pearson_cosine \| 0.9869 \| 0.9868 \|
	\| spearman_cosine \| 0.9277 \| 0.9257 \|

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Dataset

	#### allstats-semantic-synthetic-dataset-v1

	* Dataset: [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1) at [e73718f](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1/tree/e73718fb155f47b2c5cf8c4e00f0690d37bac9fa)
	* Size: 123,637 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: 10.59 tokens</li><li>max: 34 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 14.29 tokens</li><li>max: 56 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:--------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------\|:------------------\|
	\| <code>Analisis upah tenaga kerja ekonomi kreatif</code> \| <code>Upah Tenaga Kerja Ekonomi Kreatif 2011-2016</code> \| <code>0.88</code> \|
	\| <code>cari data persentase rumah tangga yang menggunakan listrik pln menurut provinsi dari 1993 sampai 2022.</code> \| <code>Persentase Rumah Tangga menurut Provinsi dan Sumber Penerangan Listrik PLN, 1993-2022</code> \| <code>0.93</code> \|
	\| <code>apakah ada tabel yang menunjukkan ekspor minyak mentah ke negara tujuan utama tahun 2000-2023?</code> \| <code>IHK dan Rata-rata Upah per Bulan Buruh Peternakan dan Perikanan di Bawah Mandor (Supervisor), 2012-2014 (2012=100)</code> \| <code>0.13</code> \|
	* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
	```json
	{
	"loss_fct": "torch.nn.modules.loss.MSELoss"
	}
	```

	### Evaluation Dataset

	#### allstats-semantic-synthetic-dataset-v1

	* Dataset: [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1) at [e73718f](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1/tree/e73718fb155f47b2c5cf8c4e00f0690d37bac9fa)
	* Size: 26,494 evaluation samples
	* Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| doc \| label \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 10.66 tokens</li><li>max: 31 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 13.94 tokens</li><li>max: 70 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>SBH Aceh 2018: Meulaboh, Banda Aceh, Lhokseumawe</code> \| <code>Survei Biaya Hidup (SBH) 2018 Meulaboh, Banda Aceh, dan Lhokseumawe</code> \| <code>0.9</code> \|
	\| <code>ekspor produk indonesia juli 2018 per negara</code> \| <code>Direktori Perusahaan Pertambangan Besar 2013</code> \| <code>0.07</code> \|
	\| <code>peternakan sapi di jawa tengah 2011</code> \| <code>Laporan Bulanan Data Sosial Ekonomi Juli 2024</code> \| <code>0.07</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`: 0.1
	- `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`: 0.1
	- `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-base-v1-eval_spearman_cosine \| allstat-semantic-base-v1-test_spearman_cosine \|
	\|:-----------:\|:---------:\|:-------------:\|:---------------:\|:----------------------------------------------:\|:---------------------------------------------:\|
	\| 0 \| 0 \| - \| 0.0942 \| 0.6574 \| - \|
	\| 0.2588 \| 500 \| 0.0449 \| 0.0262 \| 0.7353 \| - \|
	\| 0.5176 \| 1000 \| 0.0232 \| 0.0185 \| 0.7592 \| - \|
	\| 0.7764 \| 1500 \| 0.0172 \| 0.0154 \| 0.7760 \| - \|
	\| 1.0352 \| 2000 \| 0.0153 \| 0.0137 \| 0.7905 \| - \|
	\| 1.2940 \| 2500 \| 0.0124 \| 0.0130 \| 0.7920 \| - \|
	\| 1.5528 \| 3000 \| 0.0119 \| 0.0120 \| 0.8048 \| - \|
	\| 1.8116 \| 3500 \| 0.0121 \| 0.0121 \| 0.8021 \| - \|
	\| 2.0704 \| 4000 \| 0.0114 \| 0.0112 \| 0.8018 \| - \|
	\| 2.3292 \| 4500 \| 0.0093 \| 0.0117 \| 0.7996 \| - \|
	\| 2.5880 \| 5000 \| 0.0097 \| 0.0105 \| 0.8133 \| - \|
	\| 2.8468 \| 5500 \| 0.0092 \| 0.0103 \| 0.8137 \| - \|
	\| 3.1056 \| 6000 \| 0.0085 \| 0.0094 \| 0.8247 \| - \|
	\| 3.3644 \| 6500 \| 0.0068 \| 0.0090 \| 0.8326 \| - \|
	\| 3.6232 \| 7000 \| 0.0073 \| 0.0092 \| 0.8273 \| - \|
	\| 3.8820 \| 7500 \| 0.007 \| 0.0084 \| 0.8404 \| - \|
	\| 4.1408 \| 8000 \| 0.0061 \| 0.0083 \| 0.8381 \| - \|
	\| 4.3996 \| 8500 \| 0.0057 \| 0.0082 \| 0.8382 \| - \|
	\| 4.6584 \| 9000 \| 0.0056 \| 0.0074 \| 0.8458 \| - \|
	\| 4.9172 \| 9500 \| 0.0057 \| 0.0073 \| 0.8468 \| - \|
	\| 5.1760 \| 10000 \| 0.0045 \| 0.0071 \| 0.8508 \| - \|
	\| 5.4348 \| 10500 \| 0.0041 \| 0.0069 \| 0.8579 \| - \|
	\| 5.6936 \| 11000 \| 0.0047 \| 0.0069 \| 0.8471 \| - \|
	\| 5.9524 \| 11500 \| 0.0046 \| 0.0067 \| 0.8554 \| - \|
	\| 6.2112 \| 12000 \| 0.0034 \| 0.0062 \| 0.8616 \| - \|
	\| 6.4700 \| 12500 \| 0.0034 \| 0.0063 \| 0.8636 \| - \|
	\| 6.7288 \| 13000 \| 0.0036 \| 0.0062 \| 0.8649 \| - \|
	\| 6.9876 \| 13500 \| 0.0037 \| 0.0063 \| 0.8641 \| - \|
	\| 7.2464 \| 14000 \| 0.0027 \| 0.0059 \| 0.8691 \| - \|
	\| 7.5052 \| 14500 \| 0.0027 \| 0.0060 \| 0.8733 \| - \|
	\| 7.7640 \| 15000 \| 0.0031 \| 0.0060 \| 0.8748 \| - \|
	\| 8.0228 \| 15500 \| 0.0028 \| 0.0058 \| 0.8736 \| - \|
	\| 8.2816 \| 16000 \| 0.0023 \| 0.0055 \| 0.8785 \| - \|
	\| 8.5404 \| 16500 \| 0.0025 \| 0.0054 \| 0.8801 \| - \|
	\| 8.7992 \| 17000 \| 0.0024 \| 0.0058 \| 0.8809 \| - \|
	\| 9.0580 \| 17500 \| 0.0026 \| 0.0058 \| 0.8811 \| - \|
	\| 9.3168 \| 18000 \| 0.002 \| 0.0055 \| 0.8824 \| - \|
	\| 9.5756 \| 18500 \| 0.002 \| 0.0053 \| 0.8859 \| - \|
	\| 9.8344 \| 19000 \| 0.0021 \| 0.0053 \| 0.8851 \| - \|
	\| 10.0932 \| 19500 \| 0.0019 \| 0.0055 \| 0.8904 \| - \|
	\| 10.3520 \| 20000 \| 0.0016 \| 0.0052 \| 0.8946 \| - \|
	\| 10.6108 \| 20500 \| 0.0017 \| 0.0057 \| 0.8884 \| - \|
	\| 10.8696 \| 21000 \| 0.0019 \| 0.0055 \| 0.8889 \| - \|
	\| 11.1284 \| 21500 \| 0.0016 \| 0.0052 \| 0.8942 \| - \|
	\| 11.3872 \| 22000 \| 0.0014 \| 0.0053 \| 0.8961 \| - \|
	\| 11.6460 \| 22500 \| 0.0016 \| 0.0053 \| 0.8928 \| - \|
	\| 11.9048 \| 23000 \| 0.0017 \| 0.0051 \| 0.8947 \| - \|
	\| 12.1636 \| 23500 \| 0.0013 \| 0.0050 \| 0.9015 \| - \|
	\| 12.4224 \| 24000 \| 0.0012 \| 0.0059 \| 0.8886 \| - \|
	\| 12.6812 \| 24500 \| 0.0014 \| 0.0051 \| 0.9030 \| - \|
	\| 12.9400 \| 25000 \| 0.0014 \| 0.0051 \| 0.9012 \| - \|
	\| 13.1988 \| 25500 \| 0.0011 \| 0.0050 \| 0.9037 \| - \|
	\| 13.4576 \| 26000 \| 0.0011 \| 0.0050 \| 0.9053 \| - \|
	\| 13.7164 \| 26500 \| 0.0011 \| 0.0049 \| 0.9060 \| - \|
	\| 13.9752 \| 27000 \| 0.0011 \| 0.0049 \| 0.9086 \| - \|
	\| 14.2340 \| 27500 \| 0.001 \| 0.0048 \| 0.9063 \| - \|
	\| 14.4928 \| 28000 \| 0.001 \| 0.0051 \| 0.9056 \| - \|
	\| 14.7516 \| 28500 \| 0.001 \| 0.0051 \| 0.9079 \| - \|
	\| 15.0104 \| 29000 \| 0.0011 \| 0.0049 \| 0.9080 \| - \|
	\| 15.2692 \| 29500 \| 0.0008 \| 0.0048 \| 0.9126 \| - \|
	\| 15.5280 \| 30000 \| 0.0008 \| 0.0049 \| 0.9112 \| - \|
	\| 15.7867 \| 30500 \| 0.0008 \| 0.0049 \| 0.9123 \| - \|
	\| 16.0455 \| 31000 \| 0.0008 \| 0.0048 \| 0.9133 \| - \|
	\| 16.3043 \| 31500 \| 0.0006 \| 0.0048 \| 0.9103 \| - \|
	\| 16.5631 \| 32000 \| 0.0007 \| 0.0049 \| 0.9144 \| - \|
	\| 16.8219 \| 32500 \| 0.0008 \| 0.0048 \| 0.9143 \| - \|
	\| 17.0807 \| 33000 \| 0.0007 \| 0.0048 \| 0.9159 \| - \|
	\| 17.3395 \| 33500 \| 0.0007 \| 0.0047 \| 0.9174 \| - \|
	\| 17.5983 \| 34000 \| 0.0006 \| 0.0048 \| 0.9175 \| - \|
	\| 17.8571 \| 34500 \| 0.0007 \| 0.0047 \| 0.9163 \| - \|
	\| 18.1159 \| 35000 \| 0.0006 \| 0.0046 \| 0.9195 \| - \|
	\| 18.3747 \| 35500 \| 0.0006 \| 0.0047 \| 0.9190 \| - \|
	\| 18.6335 \| 36000 \| 0.0006 \| 0.0047 \| 0.9192 \| - \|
	\| 18.8923 \| 36500 \| 0.0006 \| 0.0047 \| 0.9204 \| - \|
	\| 19.1511 \| 37000 \| 0.0005 \| 0.0047 \| 0.9219 \| - \|
	\| 19.4099 \| 37500 \| 0.0004 \| 0.0046 \| 0.9218 \| - \|
	\| 19.6687 \| 38000 \| 0.0005 \| 0.0047 \| 0.9221 \| - \|
	\| 19.9275 \| 38500 \| 0.0005 \| 0.0046 \| 0.9230 \| - \|
	\| 20.1863 \| 39000 \| 0.0005 \| 0.0046 \| 0.9233 \| - \|
	\| 20.4451 \| 39500 \| 0.0004 \| 0.0046 \| 0.9240 \| - \|
	\| 20.7039 \| 40000 \| 0.0005 \| 0.0047 \| 0.9234 \| - \|
	\| 20.9627 \| 40500 \| 0.0004 \| 0.0047 \| 0.9241 \| - \|
	\| 21.2215 \| 41000 \| 0.0004 \| 0.0046 \| 0.9253 \| - \|
	\| 21.4803 \| 41500 \| 0.0004 \| 0.0046 \| 0.9259 \| - \|
	\| 21.7391 \| 42000 \| 0.0004 \| 0.0046 \| 0.9262 \| - \|
	\| 21.9979 \| 42500 \| 0.0004 \| 0.0046 \| 0.9263 \| - \|
	\| 22.2567 \| 43000 \| 0.0003 \| 0.0046 \| 0.9266 \| - \|
	\| 22.5155 \| 43500 \| 0.0003 \| 0.0046 \| 0.9266 \| - \|
	\| 22.7743 \| 44000 \| 0.0003 \| 0.0046 \| 0.9273 \| - \|
	\| 23.0331 \| 44500 \| 0.0003 \| 0.0046 \| 0.9273 \| - \|
	\| 23.2919 \| 45000 \| 0.0003 \| 0.0046 \| 0.9274 \| - \|
	\| 23.5507 \| 45500 \| 0.0003 \| 0.0046 \| 0.9277 \| - \|
	\| 23.8095 \| 46000 \| 0.0003 \| 0.0046 \| 0.9277 \| - \|
	\| 24.0 \| 46368 \| - \| - \| - \| 0.9257 \|

	* The bold row denotes the saved checkpoint.

	### 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",
	}
	```

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->