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--- |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:2602 |
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- loss:ContrastiveLoss |
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base_model: denaya/indoSBERT-large |
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widget: |
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- source_sentence: Data triwulanan GDS, investasi non-fin, pinjaman neto pemerintah |
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(triliun) 2010 |
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sentences: |
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- 'Nilai Ekspor Menurut Pelabuhan Utama (Nilai FOB: juta US$) 2000-2023' |
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- Suhu Minimum, Rata-Rata, dan Maksimum di Stasiun Pengamatan BMKG (oC), 2011-2015 |
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- 'Nilai Ekspor Menurut Negara Tujuan Utama (Nilai FOB: juta US$), 2000-2023' |
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- source_sentence: Data triwulanan GDS, investasi non-fin, pinjaman neto pemerintah |
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(triliun) 2010 |
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sentences: |
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- Tabungan Bruto, Investasi Nonfinansial, dan Pinjaman Neto Triwulanan Sektor Pemerintahan |
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Umum (triliun rupiah), 2009-2015 |
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- Produksi Perikanan Budidaya Menurut Provinsi dan Jenis Budidaya, 2000-2020 |
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- Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Kelompok Umur |
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(ribu rupiah), 2017 |
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- source_sentence: Gaji bersih vs kelompok umur dan lapangan pekerjaan, 2023 |
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sentences: |
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- Investasi Nonfinansial Menurut Sektor (triliun rupiah), 2008-2014 |
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- Posisi Kredit Usaha Mikro, Kecil, dan Menengah (UMKM) 1 pada Bank Umum (miliar |
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rupiah), 2012-2016 |
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- Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah) |
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- source_sentence: Data utang luar negeri Indonesia (pemerintah dan BI), detail kreditor |
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dan syarat, tahun 2010 |
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sentences: |
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- Angka Partisipasi Sekolah (APS) Penduduk Umur 7-18 Tahun Menurut Klasifikasi Desa, |
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Jenis Kelamin, dan Kelompok Umur, 2009-2023 |
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- Indeks Integritas Ujian Nasional |
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- Rekapitulasi Luas Penutupan Lahan Hutan dan Non Hutan Menurut Provinsi Tahun 2014-2022 |
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(Ribu Ha) |
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- source_sentence: Laporan keuangan perusahaan asuransi wajib & BPJS akhir 2015 |
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sentences: |
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- Indeks Harga Konsumen Menurut Kelompok Pengeluaran, 2020-2023 |
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- Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah) |
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- Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan |
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dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Jawa Timur, 2018-2023 |
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datasets: |
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- yahyaabd/bps-statictable-query-title-pairs |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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model-index: |
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- name: SentenceTransformer based on denaya/indoSBERT-large |
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results: |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: allstats semantic base v1 eval |
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type: allstats-semantic-base-v1-eval |
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metrics: |
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- type: pearson_cosine |
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value: 0.902671671573215 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.7797277576994545 |
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name: Spearman Cosine |
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- task: |
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type: semantic-similarity |
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name: Semantic Similarity |
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dataset: |
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name: allstat semantic base v1 test |
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type: allstat-semantic-base-v1-test |
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metrics: |
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- type: pearson_cosine |
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value: 0.9166324050239434 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.8089661156615633 |
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name: Spearman Cosine |
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--- |
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# SentenceTransformer based on denaya/indoSBERT-large |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large) on the [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-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. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large) <!-- at revision 5c64d43f07f7054dfbf33d226b3066414b6ebc4a --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 256 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs) |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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(2): Dense({'in_features': 1024, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("yahyaabd/allstats-ir-indoSBERT-large-v1") |
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# Run inference |
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sentences = [ |
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'Laporan keuangan perusahaan asuransi wajib & BPJS akhir 2015', |
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'Ringkasan Neraca Arus Dana, Triwulan I, 2013*), (Miliar Rupiah)', |
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'Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Jawa Timur, 2018-2023', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 256] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Datasets: `allstats-semantic-base-v1-eval` and `allstat-semantic-base-v1-test` |
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* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
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| Metric | allstats-semantic-base-v1-eval | allstat-semantic-base-v1-test | |
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|:--------------------|:-------------------------------|:------------------------------| |
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| pearson_cosine | 0.9027 | 0.9166 | |
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| **spearman_cosine** | **0.7797** | **0.809** | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### bps-statictable-query-title-pairs |
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* Dataset: [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs) at [c7df38f](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs/tree/c7df38f8b228efe13b1589b94c78fc7b57f02b58) |
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* Size: 2,602 training samples |
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* Columns: <code>query</code>, <code>doc</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | query | doc | label | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| |
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| type | string | string | int | |
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| details | <ul><li>min: 4 tokens</li><li>mean: 16.78 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 21.01 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>0: ~66.50%</li><li>1: ~33.50%</li></ul> | |
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* Samples: |
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| query | doc | label | |
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|:-----------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------|:---------------| |
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| <code>Pertumbuhan populasi provinsi di Indonesia 1971-2024</code> | <code>Kecepatan Angin dan Kelembaban di Stasiun Pengamatan BMKG, 2000-2010</code> | <code>0</code> | |
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| <code>Perbandingan upah nominal dan riil pekerja pertanian di Indonesia (tahun dasar 2012), periode 2017.</code> | <code>Upah Nominal dan Riil Buruh Tani di Indonesia (Rupiah), 2009-2019 (2012=100)</code> | <code>1</code> | |
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| <code>Laporan singkat cash flow statement Q4/2005</code> | <code>Nilai Produksi dan Biaya Produksi per Hektar Usaha Tanaman Bawang Merah dan Cabai Merah, 2014</code> | <code>0</code> | |
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* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: |
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```json |
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{ |
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"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", |
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"margin": 0.5, |
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"size_average": true |
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} |
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``` |
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### Evaluation Dataset |
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#### bps-statictable-query-title-pairs |
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* Dataset: [bps-statictable-query-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs) at [c7df38f](https://huggingface.co/datasets/yahyaabd/bps-statictable-query-title-pairs/tree/c7df38f8b228efe13b1589b94c78fc7b57f02b58) |
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* Size: 558 evaluation samples |
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* Columns: <code>query</code>, <code>doc</code>, and <code>label</code> |
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* Approximate statistics based on the first 558 samples: |
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| | query | doc | label | |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| |
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| type | string | string | int | |
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| details | <ul><li>min: 3 tokens</li><li>mean: 16.82 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 21.13 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>0: ~70.97%</li><li>1: ~29.03%</li></ul> | |
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* Samples: |
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| query | doc | label | |
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|:------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <code>Data pengeluaran makanan rata-rata warga Sulteng per minggu di tahun 2022, berdasarkan kelompok pendapatan</code> | <code>Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 (84 x 84)</code> | <code>0</code> | |
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| <code>Konsumsi & belanja makanan per orang di NTB, beda kelompok pengeluaran, 2021</code> | <code>Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Nusa Tenggara Barat, 2018-2023</code> | <code>1</code> | |
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| <code>Bagaimana perbandingan PNS pria dan wanita di berbagai golongan tahun 2014?</code> | <code>Penduduk Berumur 15 Tahun Ke Atas Menurut Provinsi dan Jenis Kegiatan Selama Seminggu yang Lalu, 2008 - 2024</code> | <code>0</code> | |
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* Loss: [<code>ContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: |
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```json |
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{ |
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"distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", |
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"margin": 0.5, |
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"size_average": true |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `num_train_epochs`: 4 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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- `load_best_model_at_end`: True |
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- `eval_on_start`: True |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: True |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | Validation Loss | allstats-semantic-base-v1-eval_spearman_cosine | allstat-semantic-base-v1-test_spearman_cosine | |
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|:----------:|:-------:|:-------------:|:---------------:|:----------------------------------------------:|:---------------------------------------------:| |
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| 0 | 0 | - | 0.0086 | 0.7549 | - | |
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| 0.1220 | 10 | 0.0082 | 0.0069 | 0.7610 | - | |
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| 0.2439 | 20 | 0.0058 | 0.0049 | 0.7688 | - | |
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| 0.3659 | 30 | 0.0047 | 0.0041 | 0.7686 | - | |
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| 0.4878 | 40 | 0.0034 | 0.0036 | 0.7682 | - | |
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| 0.6098 | 50 | 0.003 | 0.0034 | 0.7696 | - | |
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| 0.7317 | 60 | 0.0031 | 0.0027 | 0.7728 | - | |
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| 0.8537 | 70 | 0.0031 | 0.0029 | 0.7713 | - | |
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| 0.9756 | 80 | 0.003 | 0.0031 | 0.7731 | - | |
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| 1.0976 | 90 | 0.0011 | 0.0025 | 0.7746 | - | |
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| 1.2195 | 100 | 0.001 | 0.0023 | 0.7759 | - | |
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| 1.3415 | 110 | 0.0013 | 0.0021 | 0.7767 | - | |
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| 1.4634 | 120 | 0.0011 | 0.0021 | 0.7773 | - | |
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| 1.5854 | 130 | 0.0008 | 0.0021 | 0.7786 | - | |
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| 1.7073 | 140 | 0.0006 | 0.0021 | 0.7789 | - | |
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| 1.8293 | 150 | 0.0007 | 0.0020 | 0.7788 | - | |
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| **1.9512** | **160** | **0.0018** | **0.002** | **0.7799** | **-** | |
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| 2.0732 | 170 | 0.0006 | 0.0020 | 0.7800 | - | |
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| 2.1951 | 180 | 0.0004 | 0.0021 | 0.7795 | - | |
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| 2.3171 | 190 | 0.0006 | 0.0021 | 0.7796 | - | |
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| 2.4390 | 200 | 0.0004 | 0.0021 | 0.7798 | - | |
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| 2.5610 | 210 | 0.0003 | 0.0021 | 0.7799 | - | |
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| 2.6829 | 220 | 0.0003 | 0.0021 | 0.7798 | - | |
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| 2.8049 | 230 | 0.0004 | 0.0021 | 0.7797 | - | |
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| 2.9268 | 240 | 0.0007 | 0.0021 | 0.7798 | - | |
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| 3.0488 | 250 | 0.0003 | 0.0021 | 0.7798 | - | |
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| 3.1707 | 260 | 0.0002 | 0.0021 | 0.7796 | - | |
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| 3.2927 | 270 | 0.0003 | 0.0021 | 0.7797 | - | |
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| 3.4146 | 280 | 0.0002 | 0.0021 | 0.7797 | - | |
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| 3.5366 | 290 | 0.0002 | 0.0021 | 0.7797 | - | |
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| 3.6585 | 300 | 0.0002 | 0.0021 | 0.7797 | - | |
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| 3.7805 | 310 | 0.0004 | 0.0021 | 0.7797 | - | |
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| 3.9024 | 320 | 0.0003 | 0.0021 | 0.7797 | - | |
|
| -1 | -1 | - | - | - | 0.8090 | |
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|
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* The bold row denotes the saved checkpoint. |
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|
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.4.0 |
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- Transformers: 4.48.1 |
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- PyTorch: 2.5.1+cu124 |
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- Accelerate: 1.3.0 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.21.0 |
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## Citation |
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### BibTeX |
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|
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#### 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", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
|
``` |
|
|
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#### ContrastiveLoss |
|
```bibtex |
|
@inproceedings{hadsell2006dimensionality, |
|
author={Hadsell, R. and Chopra, S. and LeCun, Y.}, |
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booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, |
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title={Dimensionality Reduction by Learning an Invariant Mapping}, |
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year={2006}, |
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volume={2}, |
|
number={}, |
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pages={1735-1742}, |
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doi={10.1109/CVPR.2006.100} |
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} |
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``` |
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