Add new SentenceTransformer model

9be9cdc verified 24 days ago

28.4 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:244856
	- loss:CosineSimilarityLoss
	base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	widget:
	- source_sentence: Bulan apa inflasi sebesar 0,63 persen terjadi pada tahun 2013?
	sentences:
	- Pada bulan Mei 2013 terjadi inflasi sebesar 0,2 persen
	- Nilai Tukar Petani (NTP) April 2024 sebesar 116,79 atau turun 2,18 persen.
	- Posisi Kredit Perbankan<sup>1</sup>dalam Rupiah dan Valuta Asing Menurut Sektor
	Ekonomi (miliar rupiah), 2016-2018
	- source_sentence: Berapa persen penurunan Nilai Tukar Petani NTP Februari 2017
	sentences:
	- Produksi Tanaman Pangan Angka Ramalan II Tahun 2015
	- Nilai Tukar Petani (NTP) Februari 2017 Sebesar 100,33 Atau Turun 0,58 Persen
	- Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut HS, Juni 2024
	- source_sentence: analisis industri pariwisata indonesia tahun 2013
	sentences:
	- Ringkasan Neraca Arus Dana, Triwulan IV, 2012), (Miliar Rupiah)
	- Pengeluaran Untuk Konsumsi Penduduk Indonesia September 2014
	- Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan
	Negara, Desember 2020
	- source_sentence: Sosial ekonomi Indonesia bulan November 2020
	sentences:
	- Pos Kesehatan Desa
	- Jumlah Wisman Pada Januari 2011 Naik 11,14 Persen dan Penumpang Angkutan Udara
	Domestik Pada Januari 2011 Turun 6,88 Persen
	- Laporan Bulanan Data Sosial Ekonomi September 2017
	- source_sentence: Tahun berapa Rupiah terdepresiasi 0,23 persen terhadap Dolar Amerika?
	sentences:
	- 'Nilai Impor Menurut Negara Asal Utama (Nilai CIF: juta US$), 2000-2023'
	- Ringkasan Neraca Arus Dana Triwulan Pertama, 2002, (Miliar Rupiah)
	- Depresiasi Rupiah terhadap Dolar Amerika pada tahun 2016 sebesar 0,5 persen.
	datasets:
	- yahyaabd/allstats-semantic-search-synthetic-dataset-v2
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	model-index:
	- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: allstats semantic search mini v2 eval
	type: allstats-semantic-search-mini-v2-eval
	metrics:
	- type: pearson_cosine
	value: 0.9838643974678674
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8951406685580494
	name: Spearman Cosine
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: allstat semantic search mini v2 test
	type: allstat-semantic-search-mini-v2-test
	metrics:
	- type: pearson_cosine
	value: 0.98307083670705
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8922084062478435
	name: Spearman Cosine
	---

	# 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 [allstats-semantic-search-synthetic-dataset-v2](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2) 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:
	- [allstats-semantic-search-synthetic-dataset-v2](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2)
	<!-- - 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-semantic-search-mini-model-v2")
	# Run inference
	sentences = [
	'Tahun berapa Rupiah terdepresiasi 0,23 persen terhadap Dolar Amerika?',
	'Depresiasi Rupiah terhadap Dolar Amerika pada tahun 2016 sebesar 0,5 persen.',
	'Ringkasan Neraca Arus Dana Triwulan Pertama, 2002, (Miliar Rupiah)',
	]
	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

	#### Semantic Similarity

	* Datasets: `allstats-semantic-search-mini-v2-eval` and `allstat-semantic-search-mini-v2-test`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| allstats-semantic-search-mini-v2-eval \| allstat-semantic-search-mini-v2-test \|
	\|:--------------------\|:--------------------------------------\|:-------------------------------------\|
	\| pearson_cosine \| 0.9839 \| 0.9831 \|
	\| spearman_cosine \| 0.8951 \| 0.8922 \|

	<!--
	## 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-search-synthetic-dataset-v2

	* Dataset: [allstats-semantic-search-synthetic-dataset-v2](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2) at [c76f31a](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2/tree/c76f31abb3f2d3a2edd9895b9f5e896bf7c84f34)
	* Size: 244,856 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: 3 tokens</li><li>mean: 12.75 tokens</li><li>max: 45 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.81 tokens</li><li>max: 56 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:------------------------------------------------------------------------\|:--------------------------------------------------------------------------------\|:------------------\|
	\| <code>Dtaa harg konsymen edesaan (non-makann) 201</code> \| <code>Statistik Harga Konsumen Perdesaan Kelompok Nonmakanan (Data 2013)</code> \| <code>0.95</code> \|
	\| <code>Bagaimna konidsi keuamgan rymah atngga Indonsia 2020-2022?</code> \| <code>Statistik Perusahaan Perikanan 2007</code> \| <code>0.1</code> \|
	\| <code>Tingkat hunian kamar hotel tahun 2023</code> \| <code>Tingkat Penghunian Kamar Hotel 2023</code> \| <code>0.99</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-search-synthetic-dataset-v2

	* Dataset: [allstats-semantic-search-synthetic-dataset-v2](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2) at [c76f31a](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2/tree/c76f31abb3f2d3a2edd9895b9f5e896bf7c84f34)
	* Size: 52,469 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: 3 tokens</li><li>mean: 13.04 tokens</li><li>max: 43 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 15.01 tokens</li><li>max: 54 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.52</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:---------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------\|:------------------\|
	\| <code>Bulan apa NTP mengalami kenaikan 0,25 persen?</code> \| <code>Jumlah Wisatawan Mancanegara Bulan Agustus 2009 Turun 4,49 Persen Dibandingkan Bulan Sebelumnya.</code> \| <code>0.0</code> \|
	\| <code>Sebutksn keempa komositi tang disebutkn besert persentae mrajin persagangannya.</code> \| <code>Marjin Perdagangan Minyak Goreng 3,86 Persen, Terigu 5,92 Persen, Garam 23,74 Persen, Dan Susu Bubuk 13,02 Persen</code> \| <code>1.0</code> \|
	\| <code>Data kemiskinan per kabupaten/kota tahun 2007</code> \| <code>Data dan Informasi Kemiskinan 2007 Buku 2: Kabupaten/Kota</code> \| <code>0.87</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`: 8
	- `warmup_ratio`: 0.1
	- `fp16`: 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`: 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`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: False
	- `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-search-mini-v2-eval_spearman_cosine \| allstat-semantic-search-mini-v2-test_spearman_cosine \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|:-----------------------------------------------------:\|:----------------------------------------------------:\|
	\| 0.1307 \| 500 \| 0.0963 \| 0.0657 \| 0.6836 \| - \|
	\| 0.2614 \| 1000 \| 0.0558 \| 0.0428 \| 0.7480 \| - \|
	\| 0.3921 \| 1500 \| 0.0403 \| 0.0335 \| 0.7665 \| - \|
	\| 0.5227 \| 2000 \| 0.0324 \| 0.0285 \| 0.7744 \| - \|
	\| 0.6534 \| 2500 \| 0.0284 \| 0.0255 \| 0.7987 \| - \|
	\| 0.7841 \| 3000 \| 0.0246 \| 0.0225 \| 0.7883 \| - \|
	\| 0.9148 \| 3500 \| 0.0217 \| 0.0217 \| 0.7964 \| - \|
	\| 1.0455 \| 4000 \| 0.0193 \| 0.0187 \| 0.8111 \| - \|
	\| 1.1762 \| 4500 \| 0.017 \| 0.0174 \| 0.8086 \| - \|
	\| 1.3068 \| 5000 \| 0.0163 \| 0.0170 \| 0.8157 \| - \|
	\| 1.4375 \| 5500 \| 0.0157 \| 0.0161 \| 0.8000 \| - \|
	\| 1.5682 \| 6000 \| 0.015 \| 0.0156 \| 0.8133 \| - \|
	\| 1.6989 \| 6500 \| 0.0146 \| 0.0146 \| 0.8194 \| - \|
	\| 1.8296 \| 7000 \| 0.014 \| 0.0140 \| 0.8103 \| - \|
	\| 1.9603 \| 7500 \| 0.013 \| 0.0132 \| 0.8205 \| - \|
	\| 2.0910 \| 8000 \| 0.0111 \| 0.0126 \| 0.8353 \| - \|
	\| 2.2216 \| 8500 \| 0.0102 \| 0.0123 \| 0.8407 \| - \|
	\| 2.3523 \| 9000 \| 0.0101 \| 0.0118 \| 0.8389 \| - \|
	\| 2.4830 \| 9500 \| 0.01 \| 0.0115 \| 0.8444 \| - \|
	\| 2.6137 \| 10000 \| 0.0097 \| 0.0111 \| 0.8456 \| - \|
	\| 2.7444 \| 10500 \| 0.0097 \| 0.0105 \| 0.8524 \| - \|
	\| 2.8751 \| 11000 \| 0.0091 \| 0.0102 \| 0.8526 \| - \|
	\| 3.0058 \| 11500 \| 0.0088 \| 0.0100 \| 0.8561 \| - \|
	\| 3.1364 \| 12000 \| 0.0069 \| 0.0095 \| 0.8619 \| - \|
	\| 3.2671 \| 12500 \| 0.0071 \| 0.0094 \| 0.8534 \| - \|
	\| 3.3978 \| 13000 \| 0.0068 \| 0.0092 \| 0.8648 \| - \|
	\| 3.5285 \| 13500 \| 0.0069 \| 0.0093 \| 0.8638 \| - \|
	\| 3.6592 \| 14000 \| 0.0071 \| 0.0091 \| 0.8548 \| - \|
	\| 3.7899 \| 14500 \| 0.0065 \| 0.0085 \| 0.8711 \| - \|
	\| 3.9205 \| 15000 \| 0.0064 \| 0.0084 \| 0.8622 \| - \|
	\| 4.0512 \| 15500 \| 0.0061 \| 0.0080 \| 0.8675 \| - \|
	\| 4.1819 \| 16000 \| 0.0051 \| 0.0082 \| 0.8673 \| - \|
	\| 4.3126 \| 16500 \| 0.0052 \| 0.0080 \| 0.8659 \| - \|
	\| 4.4433 \| 17000 \| 0.0053 \| 0.0078 \| 0.8669 \| - \|
	\| 4.5740 \| 17500 \| 0.0053 \| 0.0077 \| 0.8690 \| - \|
	\| 4.7047 \| 18000 \| 0.005 \| 0.0076 \| 0.8758 \| - \|
	\| 4.8353 \| 18500 \| 0.0048 \| 0.0074 \| 0.8700 \| - \|
	\| 4.9660 \| 19000 \| 0.0049 \| 0.0072 \| 0.8785 \| - \|
	\| 5.0967 \| 19500 \| 0.0041 \| 0.0070 \| 0.8795 \| - \|
	\| 5.2274 \| 20000 \| 0.0039 \| 0.0071 \| 0.8803 \| - \|
	\| 5.3581 \| 20500 \| 0.0039 \| 0.0071 \| 0.8843 \| - \|
	\| 5.4888 \| 21000 \| 0.0041 \| 0.0070 \| 0.8818 \| - \|
	\| 5.6194 \| 21500 \| 0.0039 \| 0.0069 \| 0.8812 \| - \|
	\| 5.7501 \| 22000 \| 0.0038 \| 0.0068 \| 0.8868 \| - \|
	\| 5.8808 \| 22500 \| 0.0038 \| 0.0067 \| 0.8831 \| - \|
	\| 6.0115 \| 23000 \| 0.0037 \| 0.0066 \| 0.8869 \| - \|
	\| 6.1422 \| 23500 \| 0.003 \| 0.0065 \| 0.8888 \| - \|
	\| 6.2729 \| 24000 \| 0.0031 \| 0.0064 \| 0.8879 \| - \|
	\| 6.4036 \| 24500 \| 0.0032 \| 0.0064 \| 0.8881 \| - \|
	\| 6.5342 \| 25000 \| 0.003 \| 0.0062 \| 0.8919 \| - \|
	\| 6.6649 \| 25500 \| 0.0031 \| 0.0062 \| 0.8919 \| - \|
	\| 6.7956 \| 26000 \| 0.0031 \| 0.0061 \| 0.8910 \| - \|
	\| 6.9263 \| 26500 \| 0.003 \| 0.0061 \| 0.8911 \| - \|
	\| 7.0570 \| 27000 \| 0.0028 \| 0.0061 \| 0.8925 \| - \|
	\| 7.1877 \| 27500 \| 0.0025 \| 0.0061 \| 0.8922 \| - \|
	\| 7.3183 \| 28000 \| 0.0026 \| 0.0060 \| 0.8944 \| - \|
	\| 7.4490 \| 28500 \| 0.0026 \| 0.0061 \| 0.8953 \| - \|
	\| 7.5797 \| 29000 \| 0.0026 \| 0.0060 \| 0.8948 \| - \|
	\| 7.7104 \| 29500 \| 0.0025 \| 0.0060 \| 0.8941 \| - \|
	\| 7.8411 \| 30000 \| 0.0025 \| 0.0059 \| 0.8950 \| - \|
	\| 7.9718 \| 30500 \| 0.0025 \| 0.0059 \| 0.8951 \| - \|
	\| 8.0 \| 30608 \| - \| - \| - \| 0.8922 \|


	### 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.
	-->