Add new SentenceTransformer model

f6ba240 verified 18 days ago

31.9 kB

	---
	base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
	datasets:
	- yahyaabd/allstats-semantic-dataset-v4
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:88250
	- loss:CosineSimilarityLoss
	widget:
	- source_sentence: Laporan ekspor Indonesia Juli 2020
	sentences:
	- Statistik Produksi Kehutanan 2021
	- Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut HS, Juli 2020
	- Statistik Politik 2017
	- source_sentence: Bulan apa yang dicatat data kunjungan wisatawan mancanegara?
	sentences:
	- Indeks Tendensi Bisnis dan Indeks Tendensi Konsumen 2005
	- Data NTP bulan Maret 2022.
	- Kunjungan wisatawan mancanegara pada Oktober 2023 mencapai 978,50 ribu kunjungan,
	naik 33,27 persen (year-on-year)
	- source_sentence: Seberapa besar kenaikan upah nominal harian buruh tani nasional
	Januari 2016?
	sentences:
	- Keadaan Angkatan Kerja di Indonesia Mei 2013
	- Profil Pasar Gorontalo 2020
	- Tingkat pengangguran terbuka (TPT) Agustus 2024 sebesar 5,3%.
	- source_sentence: Ringkasan data statistik Indonesia 1997
	sentences:
	- Statistik Upah 2007
	- Harga konsumen bbrp jenis barang kelompok perumahan 2005
	- Statistik Indonesia 1997
	- source_sentence: Pernikahan usia anak di Indonesia periode 2013-2015
	sentences:
	- Jumlah penduduk Indonesia 2013-2015
	- Indikator Ekonomi Desember 2006
	- Indeks Tendensi Bisnis dan Indeks Tendensi Konsumen 2013
	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 eval
	type: allstats-semantic-mpnet-eval
	metrics:
	- type: pearson_cosine
	value: 0.9714169395957917
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8933550959155299
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: allstats semantic mpnet test
	type: allstats-semantic-mpnet-test
	metrics:
	- type: pearson_cosine
	value: 0.9723087139367028
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8932385415736595
	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-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4) 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-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4)
	<!-- - 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")
	# Run inference
	sentences = [
	'Pernikahan usia anak di Indonesia periode 2013-2015',
	'Jumlah penduduk Indonesia 2013-2015',
	'Indeks Tendensi Bisnis dan Indeks Tendensi Konsumen 2013',
	]
	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-eval` and `allstats-semantic-mpnet-test`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| allstats-semantic-mpnet-eval \| allstats-semantic-mpnet-test \|
	\|:--------------------\|:-----------------------------\|:-----------------------------\|
	\| pearson_cosine \| 0.9714 \| 0.9723 \|
	\| spearman_cosine \| 0.8934 \| 0.8932 \|

	<!--
	## 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-dataset-v4

	* Dataset: [allstats-semantic-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4) at [06c3cf8](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4/tree/06c3cf8715472fba6be04302a12790a6bd80443a)
	* Size: 88,250 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: 4 tokens</li><li>mean: 11.38 tokens</li><li>max: 46 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.48 tokens</li><li>max: 67 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:-----------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------\|
	\| <code>Industri teh Indonesia tahun 2021</code> \| <code>Statistik Transportasi Laut 2014</code> \| <code>0.1</code> \|
	\| <code>Tahun berapa data pertumbuhan ekonomi Indonesia tersebut?</code> \| <code>Nilai Tukar Petani (NTP) November 2023 sebesar 116,73 atau naik 0,82 persen. Harga Gabah Kering Panen di Tingkat Petani turun 1,94 persen dan Harga Beras Premium di Penggilingan turun 0,91 persen.</code> \| <code>0.0</code> \|
	\| <code>Kemiskinan di Indonesia Maret</code> \| <code>2018 Feb Tenaga Kerja</code> \| <code>0.1</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-dataset-v4

	* Dataset: [allstats-semantic-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4) at [06c3cf8](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4/tree/06c3cf8715472fba6be04302a12790a6bd80443a)
	* Size: 18,910 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: 11.35 tokens</li><li>max: 33 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.25 tokens</li><li>max: 52 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>nAalisis keuangam deas tshun 019</code> \| <code>Statistik Migrasi Nusa Tenggara Barat Hasil Survei Penduduk Antar Sensus 2015</code> \| <code>0.1</code> \|
	\| <code>Data tanaman buah dan sayur Indonesia tahun 2016</code> \| <code>Statistik Penduduk Lanjut Usia 2010</code> \| <code>0.1</code> \|
	\| <code>Pasar beras di Indonesia tahun 2018</code> \| <code>Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara, April 2021</code> \| <code>0.2</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`: 32
	- `per_device_eval_batch_size`: 32
	- `num_train_epochs`: 8
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `dataloader_num_workers`: 4
	- `load_best_model_at_end`: True
	- `label_smoothing_factor`: 0.05
	- `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`: 8
	- `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`: 0.05
	- `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-mpnet-eval_spearman_cosine \| allstats-semantic-mpnet-test_spearman_cosine \|
	\|:----------:\|:---------:\|:-------------:\|:---------------:\|:--------------------------------------------:\|:--------------------------------------------:\|
	\| 0 \| 0 \| - \| 0.0979 \| 0.6119 \| - \|
	\| 0.0906 \| 250 \| 0.0646 \| 0.0427 \| 0.7249 \| - \|
	\| 0.1813 \| 500 \| 0.039 \| 0.0324 \| 0.7596 \| - \|
	\| 0.2719 \| 750 \| 0.032 \| 0.0271 \| 0.7860 \| - \|
	\| 0.3626 \| 1000 \| 0.0276 \| 0.0255 \| 0.7920 \| - \|
	\| 0.4532 \| 1250 \| 0.0264 \| 0.0230 \| 0.8072 \| - \|
	\| 0.5439 \| 1500 \| 0.0249 \| 0.0222 \| 0.8197 \| - \|
	\| 0.6345 \| 1750 \| 0.0226 \| 0.0210 \| 0.8200 \| - \|
	\| 0.7252 \| 2000 \| 0.0218 \| 0.0209 \| 0.8202 \| - \|
	\| 0.8158 \| 2250 \| 0.0208 \| 0.0201 \| 0.8346 \| - \|
	\| 0.9065 \| 2500 \| 0.0209 \| 0.0211 \| 0.8240 \| - \|
	\| 0.9971 \| 2750 \| 0.0211 \| 0.0190 \| 0.8170 \| - \|
	\| 1.0877 \| 3000 \| 0.0161 \| 0.0182 \| 0.8332 \| - \|
	\| 1.1784 \| 3250 \| 0.0158 \| 0.0179 \| 0.8393 \| - \|
	\| 1.2690 \| 3500 \| 0.0167 \| 0.0189 \| 0.8341 \| - \|
	\| 1.3597 \| 3750 \| 0.0152 \| 0.0168 \| 0.8371 \| - \|
	\| 1.4503 \| 4000 \| 0.0151 \| 0.0165 \| 0.8435 \| - \|
	\| 1.5410 \| 4250 \| 0.0143 \| 0.0156 \| 0.8365 \| - \|
	\| 1.6316 \| 4500 \| 0.0147 \| 0.0157 \| 0.8467 \| - \|
	\| 1.7223 \| 4750 \| 0.0138 \| 0.0155 \| 0.8501 \| - \|
	\| 1.8129 \| 5000 \| 0.0147 \| 0.0154 \| 0.8457 \| - \|
	\| 1.9036 \| 5250 \| 0.0137 \| 0.0152 \| 0.8498 \| - \|
	\| 1.9942 \| 5500 \| 0.0144 \| 0.0143 \| 0.8485 \| - \|
	\| 2.0848 \| 5750 \| 0.0108 \| 0.0139 \| 0.8439 \| - \|
	\| 2.1755 \| 6000 \| 0.01 \| 0.0146 \| 0.8563 \| - \|
	\| 2.2661 \| 6250 \| 0.011 \| 0.0141 \| 0.8558 \| - \|
	\| 2.3568 \| 6500 \| 0.0107 \| 0.0144 \| 0.8497 \| - \|
	\| 2.4474 \| 6750 \| 0.01 \| 0.0138 \| 0.8577 \| - \|
	\| 2.5381 \| 7000 \| 0.0097 \| 0.0136 \| 0.8585 \| - \|
	\| 2.6287 \| 7250 \| 0.0102 \| 0.0135 \| 0.8521 \| - \|
	\| 2.7194 \| 7500 \| 0.0106 \| 0.0133 \| 0.8537 \| - \|
	\| 2.8100 \| 7750 \| 0.0098 \| 0.0133 \| 0.8643 \| - \|
	\| 2.9007 \| 8000 \| 0.0105 \| 0.0138 \| 0.8543 \| - \|
	\| 2.9913 \| 8250 \| 0.009 \| 0.0129 \| 0.8555 \| - \|
	\| 3.0819 \| 8500 \| 0.0071 \| 0.0121 \| 0.8692 \| - \|
	\| 3.1726 \| 8750 \| 0.006 \| 0.0120 \| 0.8709 \| - \|
	\| 3.2632 \| 9000 \| 0.0078 \| 0.0120 \| 0.8660 \| - \|
	\| 3.3539 \| 9250 \| 0.0072 \| 0.0122 \| 0.8656 \| - \|
	\| 3.4445 \| 9500 \| 0.007 \| 0.0123 \| 0.8696 \| - \|
	\| 3.5352 \| 9750 \| 0.0075 \| 0.0117 \| 0.8707 \| - \|
	\| 3.6258 \| 10000 \| 0.0081 \| 0.0115 \| 0.8682 \| - \|
	\| 3.7165 \| 10250 \| 0.0083 \| 0.0116 \| 0.8617 \| - \|
	\| 3.8071 \| 10500 \| 0.0075 \| 0.0116 \| 0.8665 \| - \|
	\| 3.8978 \| 10750 \| 0.0077 \| 0.0119 \| 0.8733 \| - \|
	\| 3.9884 \| 11000 \| 0.008 \| 0.0113 \| 0.8678 \| - \|
	\| 4.0790 \| 11250 \| 0.0051 \| 0.0110 \| 0.8760 \| - \|
	\| 4.1697 \| 11500 \| 0.0052 \| 0.0108 \| 0.8729 \| - \|
	\| 4.2603 \| 11750 \| 0.0056 \| 0.0108 \| 0.8771 \| - \|
	\| 4.3510 \| 12000 \| 0.0052 \| 0.0109 \| 0.8793 \| - \|
	\| 4.4416 \| 12250 \| 0.0049 \| 0.0109 \| 0.8766 \| - \|
	\| 4.5323 \| 12500 \| 0.0055 \| 0.0114 \| 0.8742 \| - \|
	\| 4.6229 \| 12750 \| 0.0061 \| 0.0108 \| 0.8749 \| - \|
	\| 4.7136 \| 13000 \| 0.0058 \| 0.0109 \| 0.8833 \| - \|
	\| 4.8042 \| 13250 \| 0.0049 \| 0.0108 \| 0.8767 \| - \|
	\| 4.8949 \| 13500 \| 0.0046 \| 0.0108 \| 0.8839 \| - \|
	\| 4.9855 \| 13750 \| 0.0052 \| 0.0104 \| 0.8790 \| - \|
	\| 5.0761 \| 14000 \| 0.0041 \| 0.0102 \| 0.8826 \| - \|
	\| 5.1668 \| 14250 \| 0.004 \| 0.0103 \| 0.8775 \| - \|
	\| 5.2574 \| 14500 \| 0.0036 \| 0.0102 \| 0.8855 \| - \|
	\| 5.3481 \| 14750 \| 0.0037 \| 0.0104 \| 0.8841 \| - \|
	\| 5.4387 \| 15000 \| 0.0036 \| 0.0101 \| 0.8860 \| - \|
	\| 5.5294 \| 15250 \| 0.0043 \| 0.0104 \| 0.8852 \| - \|
	\| 5.6200 \| 15500 \| 0.004 \| 0.0100 \| 0.8856 \| - \|
	\| 5.7107 \| 15750 \| 0.0043 \| 0.0101 \| 0.8842 \| - \|
	\| 5.8013 \| 16000 \| 0.0043 \| 0.0099 \| 0.8835 \| - \|
	\| 5.8920 \| 16250 \| 0.0041 \| 0.0099 \| 0.8852 \| - \|
	\| 5.9826 \| 16500 \| 0.0036 \| 0.0101 \| 0.8866 \| - \|
	\| 6.0732 \| 16750 \| 0.0031 \| 0.0100 \| 0.8881 \| - \|
	\| 6.1639 \| 17000 \| 0.0031 \| 0.0098 \| 0.8880 \| - \|
	\| 6.2545 \| 17250 \| 0.0027 \| 0.0098 \| 0.8886 \| - \|
	\| 6.3452 \| 17500 \| 0.0032 \| 0.0097 \| 0.8868 \| - \|
	\| 6.4358 \| 17750 \| 0.0027 \| 0.0097 \| 0.8876 \| - \|
	\| 6.5265 \| 18000 \| 0.0031 \| 0.0097 \| 0.8893 \| - \|
	\| 6.6171 \| 18250 \| 0.0032 \| 0.0096 \| 0.8903 \| - \|
	\| 6.7078 \| 18500 \| 0.003 \| 0.0096 \| 0.8898 \| - \|
	\| 6.7984 \| 18750 \| 0.0029 \| 0.0098 \| 0.8907 \| - \|
	\| 6.8891 \| 19000 \| 0.003 \| 0.0096 \| 0.8896 \| - \|
	\| 6.9797 \| 19250 \| 0.0026 \| 0.0096 \| 0.8913 \| - \|
	\| 7.0703 \| 19500 \| 0.0024 \| 0.0096 \| 0.8921 \| - \|
	\| 7.1610 \| 19750 \| 0.0021 \| 0.0097 \| 0.8920 \| - \|
	\| 7.2516 \| 20000 \| 0.0023 \| 0.0096 \| 0.8910 \| - \|
	\| 7.3423 \| 20250 \| 0.002 \| 0.0096 \| 0.8920 \| - \|
	\| 7.4329 \| 20500 \| 0.0022 \| 0.0096 \| 0.8924 \| - \|
	\| 7.5236 \| 20750 \| 0.002 \| 0.0097 \| 0.8917 \| - \|
	\| 7.6142 \| 21000 \| 0.0024 \| 0.0096 \| 0.8923 \| - \|
	\| 7.7049 \| 21250 \| 0.0025 \| 0.0095 \| 0.8928 \| - \|
	\| 7.7955 \| 21500 \| 0.0022 \| 0.0095 \| 0.8931 \| - \|
	\| 7.8861 \| 21750 \| 0.0023 \| 0.0095 \| 0.8932 \| - \|
	\| 7.9768 \| 22000 \| 0.0022 \| 0.0095 \| 0.8934 \| - \|
	\| 8.0 \| 22064 \| - \| - \| - \| 0.8932 \|

	* The bold row denotes the saved checkpoint.

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.3.1
	- Transformers: 4.48.0
	- PyTorch: 2.4.1+cu121
	- Accelerate: 0.34.2
	- 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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