Add new SentenceTransformer model

c32d12a verified 7 months ago

20.3 kB

	---
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:23478
	- loss:ContrastiveLoss
	base_model: denaya/indoSBERT-large
	widget:
	- source_sentence: 'Pekerja anak Indonesia: Buku panduan 2022 (pr & pasca pandemi)'
	sentences:
	- Statistik Perusahaan Hak Pengusahaan Hutan 2010
	- Statistik Kriminal 2016
	- ' Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan
	Negara, November 2020'
	- source_sentence: Jumlah pascr tradisional, pusat perbelanjaan, dan toko modern tahun
	2019
	sentences:
	- Profil Pasar Tradisional, Pusat Perbelanjaan, dan Toko Modern 2019
	- Laporan Perekonomian Indonesia 2008
	- Profil Industri Mikro dan Kecil 2006
	- source_sentence: Survei biay ahidup (SBH) di Ternate tahun 2012
	sentences:
	- Laporan Bulanan Data Sosial Ekonomi Januari 2016
	- Keadaan Angkatan kerja di Indonesia Agustus 2009
	- Statistik Perdagangan Luar Negeri Indonesia Impor 2023 Buku I
	- source_sentence: Direktori perwsahaan air minum, listrik, dan gas di kota tahun
	2009
	sentences:
	- Statistik Indonesia 1991
	- Direktori Perusahaan Air Minum Listrik dan Gas Kota 2009
	- Direktori Eksportir Indonesia 2015
	- source_sentence: Studi efisiensi industri manufaktr
	sentences:
	- Statistik Indonesia 2019
	- Statistik Potensi Desa Provinsi Maluku 2011
	- Klasifikasi Baku Komoditas Indonesia (KBKI) 2012 Buku 4
	datasets:
	- yahyaabd/bps-publication-pos-neg-pairs
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	model-index:
	- name: SentenceTransformer based on denaya/indoSBERT-large
	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: 0.9658815836712943
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7841756166101173
	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: 0.9592021090962591
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7818288777895762
	name: Spearman Cosine
	---

	# SentenceTransformer based on denaya/indoSBERT-large

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large) on the [bps-publication-pos-neg-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs) dataset. It maps sentences & paragraphs to a 256-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: [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large) <!-- at revision 5c64d43f07f7054dfbf33d226b3066414b6ebc4a -->
	- Maximum Sequence Length: 256 tokens
	- Output Dimensionality: 256 dimensions
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [bps-publication-pos-neg-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs)
	<!-- - 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': 256, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 1024, '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})
	(2): Dense({'in_features': 1024, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
	)
	```

	## 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-v1-3")
	# Run inference
	sentences = [
	'Studi efisiensi industri manufaktr',
	'Statistik Potensi Desa Provinsi Maluku 2011',
	'Klasifikasi Baku Komoditas Indonesia (KBKI) 2012 Buku 4',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 256]

	# 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 \| 0.9659 \| 0.9592 \|
	\| spearman_cosine \| 0.7842 \| 0.7818 \|

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	## Training Details

	### Training Dataset

	#### bps-publication-pos-neg-pairs

	* Dataset: [bps-publication-pos-neg-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs) at [46a5cb7](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs/tree/46a5cb7b0d6b00e9ef6bb1bf0ab6b6628ab66a9b)
	* Size: 23,478 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 \| int \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 11.84 tokens</li><li>max: 24 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 10.77 tokens</li><li>max: 28 tokens</li></ul> \| <ul><li>0: ~72.40%</li><li>1: ~27.60%</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:---------------\|
	\| <code>Direktori perusahaan perantara keuangan bukan koperasi tahun 2006 (SE)</code> \| <code>Tinjauan Regional Berdasarkan PDRB Kabupaten/Kota 2018-2022, Buku 2 Pulau Jawa-Bali</code> \| <code>0</code> \|
	\| <code>Informasi lengkap tentang PPLS 2011</code> \| <code>Indeks Harga Perdagangan Besar Indonesia tahun 2005</code> \| <code>0</code> \|
	\| <code>Data konversi GKG ke beras tahun 2012</code> \| <code>Indikator Ekonomi Juli 2023</code> \| <code>0</code> \|
	* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
	```json
	{
	"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
	"margin": 0.5,
	"size_average": true
	}
	```

	### Evaluation Dataset

	#### bps-publication-pos-neg-pairs

	* Dataset: [bps-publication-pos-neg-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs) at [46a5cb7](https://huggingface.co/datasets/yahyaabd/bps-publication-pos-neg-pairs/tree/46a5cb7b0d6b00e9ef6bb1bf0ab6b6628ab66a9b)
	* Size: 5,031 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 \| int \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 11.97 tokens</li><li>max: 24 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 10.76 tokens</li><li>max: 32 tokens</li></ul> \| <ul><li>0: ~72.70%</li><li>1: ~27.30%</li></ul> \|
	* Samples:
	\| query \| doc \| label \|
	\|:-------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:---------------\|
	\| <code>Informasi angka tanaman berkhasiat ogbat dan tanaman hias di tahun 2005</code> \| <code>Tinjauan Regional Berdasarkan PDRB Kabupaten/Kota 2010-2013 - Buku 2 Pulau Jawa-Bali</code> \| <code>0</code> \|
	\| <code>Informasi lengkap statistik horsikultura tahun 2020</code> \| <code>NERACA ENERGI INDONESIA 2017-2021</code> \| <code>0</code> \|
	\| <code>Statistik air bersih Indonesia periode 2014-2019</code> \| <code>Profil Usaha Konstruksi Perorangan Provinsi Kalimantan Utara, 2022</code> \| <code>0</code> \|
	* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters:
	```json
	{
	"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
	"margin": 0.5,
	"size_average": true
	}
	```

	### 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
	- `load_best_model_at_end`: True
	- `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`: 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`: 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`: 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 \| - \| 0.0053 \| 0.7770 \| - \|
	\| 0.5450 \| 200 \| 0.0023 \| 0.0005 \| 0.7842 \| - \|
	\| 1.0899 \| 400 \| 0.0005 \| 0.0002 \| 0.7842 \| - \|
	\| 1.6349 \| 600 \| 0.0002 \| 0.0002 \| 0.7842 \| - \|
	\| 2.1798 \| 800 \| 0.0001 \| 0.0001 \| 0.7842 \| - \|
	\| 2.7248 \| 1000 \| 0.0001 \| 0.0001 \| 0.7842 \| - \|
	\| 3.2698 \| 1200 \| 0.0 \| 0.0001 \| 0.7842 \| - \|
	\| 3.8147 \| 1400 \| 0.0 \| 0.0001 \| 0.7842 \| - \|
	\| 4.3597 \| 1600 \| 0.0 \| 0.0001 \| 0.7842 \| - \|
	\| 4.9046 \| 1800 \| 0.0 \| 0.0001 \| 0.7842 \| - \|
	\| 5.4496 \| 2000 \| 0.0 \| 0.0001 \| 0.7842 \| - \|
	\| 5.9946 \| 2200 \| 0.0 \| 0.0001 \| 0.7842 \| - \|
	\| 6.5395 \| 2400 \| 0.0 \| 0.0001 \| 0.7842 \| - \|
	\| 7.0845 \| 2600 \| 0.0 \| 0.0001 \| 0.7842 \| - \|
	\| 7.6294 \| 2800 \| 0.0 \| 0.0001 \| 0.7842 \| - \|
	\| -1 \| -1 \| - \| - \| - \| 0.7818 \|

	* The bold row denotes the saved checkpoint.

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.4.0
	- Transformers: 4.48.1
	- PyTorch: 2.5.1+cu124
	- Accelerate: 1.3.0
	- 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",
	}
	```

	#### ContrastiveLoss
	```bibtex
	@inproceedings{hadsell2006dimensionality,
	author={Hadsell, R. and Chopra, S. and LeCun, Y.},
	booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
	title={Dimensionality Reduction by Learning an Invariant Mapping},
	year={2006},
	volume={2},
	number={},
	pages={1735-1742},
	doi={10.1109/CVPR.2006.100}
	}
	```

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