Add new SentenceTransformer model

29e591b verified 3 months ago

30.3 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:123640
	- loss:CosineSimilarityLoss
	base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
	widget:
	- source_sentence: data perempuan dan laki-laki di indonesia 2022
	sentences:
	- Statistik Telekomunikasi Indonesia 2012
	- Perkembangan Indeks Produksi Triwulanan Industri Mikro dan Kecil 2023
	- Pada Agustus 2014, Jumlah wisman mencapai 826,8 ribu
	- source_sentence: hasil survei kebutuhan data 2011 di indonesia
	sentences:
	- Analisis Survei Kebutuhan Data 2011
	- Produk Domestik Bruto Indonesia Triwulanan 2007-2011
	- Direktori Perusahaan Air Bersih, Listrik, dan Gas 2022
	- source_sentence: komoditas apa yang produksinya naik 3,24 persen pada tahun 2013?
	sentences:
	- Indikator Ekonomi Juni 2017
	- Produksi jagung naik pada tahun 2013.
	- Statistik Keuangan Pemerintah Desa 2018
	- source_sentence: buku-buku statistik tahun 2007
	sentences:
	- Statistik Keuangan Badan Usaha Milik Negara dan Badan Usaha Milik Daerah 2019
	- Statistik Harga Konsumen Perdesaan Kelompok Makanan 2011
	- Buletin Statistik Perdagangan Luar Negeri Impor Mei 2019
	- source_sentence: analisis kinerja ekspor indonesia feb 2014
	sentences:
	- Kajian Big Data Sinyal Pemulihan Indonesia dari Pandemi Covid-19
	- Laporan Bulanan Data Sosial Ekonomi Januari 2019
	- Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan
	Negara Februari 2014
	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: 0.9866451272402678
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9032950863870964
	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: 0.9876833290128094
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.9063327700749637
	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")
	# Run inference
	sentences = [
	'analisis kinerja ekspor indonesia feb 2014',
	'Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara Februari 2014',
	'Laporan Bulanan Data Sosial Ekonomi 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]
	```

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	## 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 \| 0.9866 \| 0.9877 \|
	\| spearman_cosine \| 0.9033 \| 0.9063 \|

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	## Bias, Risks and Limitations

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	## 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 [d59a245](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1/tree/d59a24585b2ee30e806569dc6a091becd5fcac0c)
	* Size: 123,640 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: 6 tokens</li><li>mean: 10.64 tokens</li><li>max: 34 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.06 tokens</li><li>max: 76 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>Gambaran umum karakteristik usaha di Indonesia</code> \| <code>Statistik Karakteristik Usaha 2022/2023</code> \| <code>0.9</code> \|
	\| <code>Tabel data jumlah sekolah, guru, dan murid MA di bawah Kementerian Agama per provinsi.</code> \| <code>Jumlah Sekolah, Guru, dan Murid Madrasah Aliyah (MA) di Bawah Kementerian Agama Menurut Provinsi, tahun ajaran 2005/2006-2015/2016</code> \| <code>0.96</code> \|
	\| <code>bagaimana kinerja sektor konstruksi indonesia di triwulan ketiga tahun 2008?</code> \| <code>Statistik Restoran/Rumah Makan 2007</code> \| <code>0.09</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 [d59a245](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1/tree/d59a24585b2ee30e806569dc6a091becd5fcac0c)
	* 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: 10.48 tokens</li><li>max: 34 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 13.86 tokens</li><li>max: 58 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>Harga barang konsumsi Indonesia 2022: data per kota</code> \| <code>Harga Konsumen Beberapa Barang Kelompok Makanan, Minuman, dan Tembakau 90 Kota di Indonesia 2022</code> \| <code>0.92</code> \|
	\| <code>data biaya hidup bali 2018</code> \| <code>Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara, Maret 2018</code> \| <code>0.1</code> \|
	\| <code>ekspor barang indonesia november 2011: data lengkap</code> \| <code>Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara Februari 2013</code> \| <code>0.12</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`: 10
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `load_best_model_at_end`: 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`: 10
	- `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`: False
	- `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 \|
	\|:----------:\|:---------:\|:-------------:\|:---------------:\|:----------------------------------------------:\|:---------------------------------------------:\|
	\| 0.1294 \| 500 \| 0.0454 \| 0.0267 \| 0.7374 \| - \|
	\| 0.2588 \| 1000 \| 0.0243 \| 0.0205 \| 0.7527 \| - \|
	\| 0.3882 \| 1500 \| 0.0199 \| 0.0169 \| 0.7720 \| - \|
	\| 0.5176 \| 2000 \| 0.0186 \| 0.0164 \| 0.7733 \| - \|
	\| 0.6470 \| 2500 \| 0.0179 \| 0.0158 \| 0.7806 \| - \|
	\| 0.7764 \| 3000 \| 0.0158 \| 0.0155 \| 0.7826 \| - \|
	\| 0.9058 \| 3500 \| 0.0159 \| 0.0155 \| 0.7771 \| - \|
	\| 1.0352 \| 4000 \| 0.0155 \| 0.0143 \| 0.7847 \| - \|
	\| 1.1646 \| 4500 \| 0.0133 \| 0.0141 \| 0.7935 \| - \|
	\| 1.2940 \| 5000 \| 0.0128 \| 0.0132 \| 0.7986 \| - \|
	\| 1.4234 \| 5500 \| 0.0121 \| 0.0120 \| 0.8148 \| - \|
	\| 1.5528 \| 6000 \| 0.012 \| 0.0118 \| 0.8030 \| - \|
	\| 1.6822 \| 6500 \| 0.0118 \| 0.0121 \| 0.8132 \| - \|
	\| 1.8116 \| 7000 \| 0.0119 \| 0.0109 \| 0.8130 \| - \|
	\| 1.9410 \| 7500 \| 0.0107 \| 0.0108 \| 0.8132 \| - \|
	\| 2.0704 \| 8000 \| 0.009 \| 0.0098 \| 0.8181 \| - \|
	\| 2.1998 \| 8500 \| 0.0082 \| 0.0099 \| 0.8221 \| - \|
	\| 2.3292 \| 9000 \| 0.008 \| 0.0100 \| 0.8221 \| - \|
	\| 2.4586 \| 9500 \| 0.008 \| 0.0095 \| 0.8302 \| - \|
	\| 2.5880 \| 10000 \| 0.0083 \| 0.0090 \| 0.8284 \| - \|
	\| 2.7174 \| 10500 \| 0.0084 \| 0.0093 \| 0.8261 \| - \|
	\| 2.8468 \| 11000 \| 0.0084 \| 0.0089 \| 0.8283 \| - \|
	\| 2.9762 \| 11500 \| 0.0083 \| 0.0093 \| 0.8259 \| - \|
	\| 3.1056 \| 12000 \| 0.0056 \| 0.0083 \| 0.8362 \| - \|
	\| 3.2350 \| 12500 \| 0.006 \| 0.0081 \| 0.8357 \| - \|
	\| 3.3644 \| 13000 \| 0.0057 \| 0.0078 \| 0.8381 \| - \|
	\| 3.4938 \| 13500 \| 0.006 \| 0.0081 \| 0.8399 \| - \|
	\| 3.6232 \| 14000 \| 0.0058 \| 0.0078 \| 0.8420 \| - \|
	\| 3.7526 \| 14500 \| 0.0068 \| 0.0078 \| 0.8303 \| - \|
	\| 3.8820 \| 15000 \| 0.0056 \| 0.0072 \| 0.8502 \| - \|
	\| 4.0114 \| 15500 \| 0.0054 \| 0.0073 \| 0.8483 \| - \|
	\| 4.1408 \| 16000 \| 0.004 \| 0.0068 \| 0.8565 \| - \|
	\| 4.2702 \| 16500 \| 0.0042 \| 0.0069 \| 0.8493 \| - \|
	\| 4.3996 \| 17000 \| 0.0043 \| 0.0069 \| 0.8507 \| - \|
	\| 4.5290 \| 17500 \| 0.0045 \| 0.0069 \| 0.8536 \| - \|
	\| 4.6584 \| 18000 \| 0.0042 \| 0.0064 \| 0.8602 \| - \|
	\| 4.7878 \| 18500 \| 0.0043 \| 0.0065 \| 0.8537 \| - \|
	\| 4.9172 \| 19000 \| 0.0039 \| 0.0062 \| 0.8623 \| - \|
	\| 5.0466 \| 19500 \| 0.0041 \| 0.0065 \| 0.8601 \| - \|
	\| 5.1760 \| 20000 \| 0.0032 \| 0.0060 \| 0.8643 \| - \|
	\| 5.3054 \| 20500 \| 0.0032 \| 0.0064 \| 0.8657 \| - \|
	\| 5.4348 \| 21000 \| 0.0032 \| 0.0062 \| 0.8669 \| - \|
	\| 5.5642 \| 21500 \| 0.0031 \| 0.0065 \| 0.8633 \| - \|
	\| 5.6936 \| 22000 \| 0.003 \| 0.0059 \| 0.8682 \| - \|
	\| 5.8230 \| 22500 \| 0.0032 \| 0.0057 \| 0.8713 \| - \|
	\| 5.9524 \| 23000 \| 0.0032 \| 0.0057 \| 0.8688 \| - \|
	\| 6.0818 \| 23500 \| 0.0026 \| 0.0055 \| 0.8772 \| - \|
	\| 6.2112 \| 24000 \| 0.0023 \| 0.0056 \| 0.8708 \| - \|
	\| 6.3406 \| 24500 \| 0.0029 \| 0.0056 \| 0.8734 \| - \|
	\| 6.4700 \| 25000 \| 0.0027 \| 0.0054 \| 0.8748 \| - \|
	\| 6.5994 \| 25500 \| 0.0022 \| 0.0054 \| 0.8827 \| - \|
	\| 6.7288 \| 26000 \| 0.0021 \| 0.0053 \| 0.8823 \| - \|
	\| 6.8582 \| 26500 \| 0.0021 \| 0.0053 \| 0.8832 \| - \|
	\| 6.9876 \| 27000 \| 0.0025 \| 0.0052 \| 0.8839 \| - \|
	\| 7.1170 \| 27500 \| 0.002 \| 0.0051 \| 0.8887 \| - \|
	\| 7.2464 \| 28000 \| 0.0017 \| 0.0050 \| 0.8869 \| - \|
	\| 7.3758 \| 28500 \| 0.0019 \| 0.0052 \| 0.8845 \| - \|
	\| 7.5052 \| 29000 \| 0.0017 \| 0.0051 \| 0.8897 \| - \|
	\| 7.6346 \| 29500 \| 0.0017 \| 0.0051 \| 0.8920 \| - \|
	\| 7.7640 \| 30000 \| 0.0018 \| 0.0050 \| 0.8889 \| - \|
	\| 7.8934 \| 30500 \| 0.0019 \| 0.0050 \| 0.8931 \| - \|
	\| 8.0228 \| 31000 \| 0.002 \| 0.0049 \| 0.8889 \| - \|
	\| 8.1522 \| 31500 \| 0.0014 \| 0.0049 \| 0.8912 \| - \|
	\| 8.2816 \| 32000 \| 0.0013 \| 0.0049 \| 0.8922 \| - \|
	\| 8.4110 \| 32500 \| 0.0014 \| 0.0049 \| 0.8947 \| - \|
	\| 8.5404 \| 33000 \| 0.0014 \| 0.0049 \| 0.8960 \| - \|
	\| 8.6698 \| 33500 \| 0.0014 \| 0.0049 \| 0.8972 \| - \|
	\| 8.7992 \| 34000 \| 0.0014 \| 0.0048 \| 0.8982 \| - \|
	\| 8.9286 \| 34500 \| 0.0013 \| 0.0048 \| 0.9003 \| - \|
	\| 9.0580 \| 35000 \| 0.0014 \| 0.0048 \| 0.9001 \| - \|
	\| 9.1874 \| 35500 \| 0.0012 \| 0.0048 \| 0.8995 \| - \|
	\| 9.3168 \| 36000 \| 0.0011 \| 0.0048 \| 0.9008 \| - \|
	\| 9.4462 \| 36500 \| 0.001 \| 0.0047 \| 0.9015 \| - \|
	\| 9.5756 \| 37000 \| 0.0011 \| 0.0047 \| 0.9026 \| - \|
	\| 9.7050 \| 37500 \| 0.0011 \| 0.0047 \| 0.9027 \| - \|
	\| 9.8344 \| 38000 \| 0.001 \| 0.0047 \| 0.9035 \| - \|
	\| 9.9638 \| 38500 \| 0.0011 \| 0.0047 \| 0.9033 \| - \|
	\| 10.0 \| 38640 \| - \| - \| - \| 0.9063 \|

	* 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",
	}
	```

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