Add new SentenceTransformer model

c4b55cc verified 8 months ago

26.1 kB

	---
	base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	datasets:
	- yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini
	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:70280
	- loss:CosineSimilarityLoss
	widget:
	- source_sentence: Data SBH tahun 2012 di Mamuju
	sentences:
	- Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Harmonized System November
	2013
	- SBH 2012 - Mamuju
	- IHK di 66 Kota di Indonesia 2013
	- source_sentence: Statistik konstruksi tahun 2020
	sentences:
	- Indeks Ketimpangan Gender 2022
	- Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Menurut Provinsi, 1971-2020
	- Perkembangan Beberapa Indikator Utama sosial-Ekonomi Indonesia Edisi Februari
	2016
	- source_sentence: Berapa besar inflasi pada bulan Oktober 2008?
	sentences:
	- Tinjauan Ekonomi Regional Indonesia Berdasarkan Data PDRB 2004-2008 Buku 2
	- Statistik Sumber Daya Laut dan Pesisir 2020
	- Inflasi September 2008 sebesar 0,97 persen.
	- source_sentence: 'Sektor konstruksi Indonesia: data statistik 1990-2013'
	sentences:
	- Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Provinsi dan
	Lapangan Pekerjaan Utama, 2023
	- Direktori Perusahaan Kehutanan 2019
	- Sensus Ekonomi 2006 Hasil Pendaftaran Perusahaan Sumatera Selatan
	- source_sentence: Perdagangan luar negeri, impor, Oktober 2020
	sentences:
	- Indikator Ekonomi September 2005
	- Statistik Potensi Desa Provinsi DI Yogyakarta 2005
	- Indikator Ekonomi November 1999
	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: 0.9617082550278393
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8518022238549516
	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: 0.9604638064122318
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8480797444308495
	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-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini) 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-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini)
	<!-- - 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-v2-2")
	# Run inference
	sentences = [
	'Perdagangan luar negeri, impor, Oktober 2020',
	'Indikator Ekonomi November 1999',
	'Indikator Ekonomi September 2005',
	]
	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]
	```

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	## 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 \| 0.9617 \| 0.9605 \|
	\| spearman_cosine \| 0.8518 \| 0.8481 \|

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	## Bias, Risks and Limitations

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	## Training Details

	### Training Dataset

	#### allstats-semantic-search-synthetic-dataset-v2-mini

	* Dataset: [allstats-semantic-search-synthetic-dataset-v2-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini) at [8222b01](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini/tree/8222b01e37490603bc838a6368bc2946a6455a7c)
	* Size: 70,280 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: 10.92 tokens</li><li>max: 50 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.68 tokens</li><li>max: 59 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>Statistik perusahaan pembudidaya tanaman kehutanan 2018</code> \| <code>Statistik Perusahaan Pembudidaya Tanaman Kehutanan 2018</code> \| <code>0.97</code> \|
	\| <code>Berapa persen pertumbuhan PDB Indonesia pada Triwulan III Tahun 2002?</code> \| <code>Inflasi Bulan November 2002 Sebesar 1,85 %</code> \| <code>0.0</code> \|
	\| <code>Perdagangan luar negeri Indonesia, impor 2019, jilid 2</code> \| <code>Pendataan Sapi Potong Sapi Perah (PSPK 2011) Sulawesi Barat</code> \| <code>0.06</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-mini

	* Dataset: [allstats-semantic-search-synthetic-dataset-v2-mini](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini) at [8222b01](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v2-mini/tree/8222b01e37490603bc838a6368bc2946a6455a7c)
	* Size: 15,060 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: 10.96 tokens</li><li>max: 48 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.74 tokens</li><li>max: 70 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>Review PDRB daerah di Pulau Sumatera 2010-2013</code> \| <code>Statistik Pendidikan 2006</code> \| <code>0.12</code> \|
	\| <code>Analisis data angkatan kerja Agustus 2021</code> \| <code>Booklet Survei Angkatan Kerja Nasional Agustus 2021</code> \| <code>0.9</code> \|
	\| <code>Berapa persen inflasi yang terjadi pada Juli 2015?</code> \| <code>Inflasi pada bulan lain tidak disebutkan</code> \| <code>0.0</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
	- `bf16`: 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`: True
	- `fp16`: False
	- `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`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `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
	- `eval_use_gather_object`: 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 \|
	\|:-------:\|:-----:\|:-------------:\|:---------------:\|:-----------------------------------------------------:\|:----------------------------------------------------:\|
	\| 0.4550 \| 500 \| 0.0643 \| 0.0413 \| 0.6996 \| - \|
	\| 0.9099 \| 1000 \| 0.0348 \| 0.0280 \| 0.7533 \| - \|
	\| 1.3649 \| 1500 \| 0.0254 \| 0.0238 \| 0.7737 \| - \|
	\| 1.8198 \| 2000 \| 0.0223 \| 0.0205 \| 0.7831 \| - \|
	\| 2.2748 \| 2500 \| 0.0181 \| 0.0197 \| 0.7894 \| - \|
	\| 2.7298 \| 3000 \| 0.0173 \| 0.0184 \| 0.7876 \| - \|
	\| 3.1847 \| 3500 \| 0.0152 \| 0.0170 \| 0.7954 \| - \|
	\| 3.6397 \| 4000 \| 0.0123 \| 0.0175 \| 0.7970 \| - \|
	\| 4.0946 \| 4500 \| 0.0125 \| 0.0163 \| 0.8118 \| - \|
	\| 4.5496 \| 5000 \| 0.01 \| 0.0161 \| 0.8047 \| - \|
	\| 5.0045 \| 5500 \| 0.0103 \| 0.0157 \| 0.8126 \| - \|
	\| 5.4595 \| 6000 \| 0.0079 \| 0.0150 \| 0.8224 \| - \|
	\| 5.9145 \| 6500 \| 0.0087 \| 0.0156 \| 0.8219 \| - \|
	\| 6.3694 \| 7000 \| 0.0071 \| 0.0152 \| 0.8145 \| - \|
	\| 6.8244 \| 7500 \| 0.0068 \| 0.0153 \| 0.8172 \| - \|
	\| 7.2793 \| 8000 \| 0.0061 \| 0.0147 \| 0.8216 \| - \|
	\| 7.7343 \| 8500 \| 0.0062 \| 0.0146 \| 0.8267 \| - \|
	\| 8.1893 \| 9000 \| 0.0055 \| 0.0145 \| 0.8325 \| - \|
	\| 8.6442 \| 9500 \| 0.005 \| 0.0146 \| 0.8335 \| - \|
	\| 9.0992 \| 10000 \| 0.0052 \| 0.0143 \| 0.8356 \| - \|
	\| 9.5541 \| 10500 \| 0.0043 \| 0.0144 \| 0.8313 \| - \|
	\| 10.0091 \| 11000 \| 0.0051 \| 0.0144 \| 0.8362 \| - \|
	\| 10.4641 \| 11500 \| 0.004 \| 0.0145 \| 0.8376 \| - \|
	\| 10.9190 \| 12000 \| 0.0039 \| 0.0142 \| 0.8331 \| - \|
	\| 11.3740 \| 12500 \| 0.0034 \| 0.0141 \| 0.8397 \| - \|
	\| 11.8289 \| 13000 \| 0.0033 \| 0.0140 \| 0.8398 \| - \|
	\| 12.2839 \| 13500 \| 0.0032 \| 0.0143 \| 0.8411 \| - \|
	\| 12.7389 \| 14000 \| 0.003 \| 0.0141 \| 0.8407 \| - \|
	\| 13.1938 \| 14500 \| 0.0031 \| 0.0141 \| 0.8379 \| - \|
	\| 13.6488 \| 15000 \| 0.0026 \| 0.0141 \| 0.8419 \| - \|
	\| 14.1037 \| 15500 \| 0.0028 \| 0.0141 \| 0.8442 \| - \|
	\| 14.5587 \| 16000 \| 0.0023 \| 0.0138 \| 0.8455 \| - \|
	\| 15.0136 \| 16500 \| 0.0025 \| 0.0147 \| 0.8359 \| - \|
	\| 15.4686 \| 17000 \| 0.0021 \| 0.0141 \| 0.8459 \| - \|
	\| 15.9236 \| 17500 \| 0.0023 \| 0.0140 \| 0.8433 \| - \|
	\| 16.3785 \| 18000 \| 0.002 \| 0.0139 \| 0.8465 \| - \|
	\| 16.8335 \| 18500 \| 0.002 \| 0.0139 \| 0.8461 \| - \|
	\| 17.2884 \| 19000 \| 0.0018 \| 0.0139 \| 0.8482 \| - \|
	\| 17.7434 \| 19500 \| 0.0018 \| 0.0138 \| 0.8477 \| - \|
	\| 18.1984 \| 20000 \| 0.0017 \| 0.0138 \| 0.8503 \| - \|
	\| 18.6533 \| 20500 \| 0.0016 \| 0.0136 \| 0.8493 \| - \|
	\| 19.1083 \| 21000 \| 0.0016 \| 0.0139 \| 0.8501 \| - \|
	\| 19.5632 \| 21500 \| 0.0015 \| 0.0138 \| 0.8478 \| - \|
	\| 20.0182 \| 22000 \| 0.0015 \| 0.0139 \| 0.8501 \| - \|
	\| 20.4732 \| 22500 \| 0.0013 \| 0.0139 \| 0.8508 \| - \|
	\| 20.9281 \| 23000 \| 0.0015 \| 0.0139 \| 0.8511 \| - \|
	\| 21.3831 \| 23500 \| 0.0013 \| 0.0139 \| 0.8517 \| - \|
	\| 21.8380 \| 24000 \| 0.0013 \| 0.0139 \| 0.8512 \| - \|
	\| 22.2930 \| 24500 \| 0.0012 \| 0.0139 \| 0.8512 \| - \|
	\| 22.7480 \| 25000 \| 0.0012 \| 0.0138 \| 0.8520 \| - \|
	\| 23.2029 \| 25500 \| 0.0012 \| 0.0139 \| 0.8520 \| - \|
	\| 23.6579 \| 26000 \| 0.0011 \| 0.0139 \| 0.8518 \| - \|
	\| 24.0 \| 26376 \| - \| - \| - \| 0.8481 \|


	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.3.1
	- Transformers: 4.44.2
	- PyTorch: 2.4.1+cu121
	- Accelerate: 0.34.2
	- Datasets: 3.2.0
	- Tokenizers: 0.19.1

	## 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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