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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:25580 |
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- loss:OnlineContrastiveLoss |
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base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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widget: |
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- source_sentence: ikhtisar arus kas triwulan 1, 2004 (miliar) |
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sentences: |
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- Balita (0-59 Bulan) Menurut Status Gizi, Tahun 1998-2005 |
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- Perbandingan Indeks dan Tingkat Inflasi Desember 2023 Kota-kota di Luar Pulau |
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Jawa dan Sumatera dengan Nasional (2018=100) |
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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 Sulawesi Tengah, 2018-2023 |
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- source_sentence: BaIgaimana gambaran neraca arus dana dUi Indonesia pada kuartal |
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kedua tahun 2015? |
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sentences: |
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- Jumlah Sekolah, Guru, dan Murid Sekolah Menengah Pertama (SMP) di Bawah Kementrian |
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Pendidikan dan Kebudayaan Menurut Provinsi 2011/2012-2015/2016 |
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- Ringkasan Neraca Arus Dana Triwulan III Tahun 2003 (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 Sulawesi Tenggara, 2018-2023 |
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- source_sentence: Berapa persen pengeluaran orang di kotaa untuk makanan vs non-makanan, |
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per provinsi, 2018? |
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sentences: |
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- Ekspor Tanaman Obat, Aromatik, dan Rempah-Rempah menurut Negara Tujuan Utama, |
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2012-2023 |
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- Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Pendidikan Tertinggi |
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yang Ditamatkan (ribu rupiah), 2017 |
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- IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), |
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1996-2014 (1996=100) |
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- source_sentence: Negara-negara asal impor crude oil dan produk turunannya tahun |
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2002-2023 |
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sentences: |
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- Persentase Pengeluaran Rata-rata per Kapita Sebulan Menurut Kelompok Barang, Indonesia, |
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1999, 2002-2023 |
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- Rata-rata Pendapatan Bersih Berusaha Sendiri menurut Provinsi dan Pendidikan yang |
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Ditamatkan (ribu rupiah), 2016 |
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- Perkembangan Beberapa Agregat Pendapatan dan Pendapatan per Kapita Atas Dasar |
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Harga Berlaku, 2010-2016 |
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- source_sentence: Arus dana Q3 2006 |
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sentences: |
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- Posisi Simpanan Berjangka Rupiah pada Bank Umum dan BPR Menurut Golongan Pemilik |
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(miliar rupiah), 2005-2018 |
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- Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah) |
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- Rata-Rata Pengeluaran per Kapita Sebulan di Daerah Perkotaan Menurut Kelompok |
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Barang dan Golongan Pengeluaran per Kapita Sebulan, 2000-2012 |
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datasets: |
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- yahyaabd/query-hard-pos-neg-doc-pairs-statictable |
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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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- cosine_accuracy |
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- cosine_accuracy_threshold |
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- cosine_f1 |
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- cosine_f1_threshold |
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- cosine_precision |
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- cosine_recall |
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- cosine_ap |
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- cosine_mcc |
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model-index: |
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- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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results: |
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- task: |
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type: binary-classification |
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name: Binary Classification |
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dataset: |
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name: allstats semantic mini v1 test |
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type: allstats-semantic-mini-v1_test |
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metrics: |
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- type: cosine_accuracy |
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value: 0.9814342919548599 |
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name: Cosine Accuracy |
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- type: cosine_accuracy_threshold |
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value: 0.8306337594985962 |
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name: Cosine Accuracy Threshold |
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- type: cosine_f1 |
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value: 0.9713160854893139 |
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name: Cosine F1 |
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- type: cosine_f1_threshold |
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value: 0.8306337594985962 |
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name: Cosine F1 Threshold |
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- type: cosine_precision |
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value: 0.977916194790487 |
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name: Cosine Precision |
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- type: cosine_recall |
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value: 0.964804469273743 |
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name: Cosine Recall |
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- type: cosine_ap |
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value: 0.9929976456739232 |
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name: Cosine Ap |
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- type: cosine_mcc |
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value: 0.9576402497090888 |
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name: Cosine Mcc |
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- task: |
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type: binary-classification |
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name: Binary Classification |
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dataset: |
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name: allstats semantic mini v1 dev |
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type: allstats-semantic-mini-v1_dev |
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metrics: |
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- type: cosine_accuracy |
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value: 0.9691549552838109 |
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name: Cosine Accuracy |
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- type: cosine_accuracy_threshold |
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value: 0.8363478779792786 |
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name: Cosine Accuracy Threshold |
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- type: cosine_f1 |
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value: 0.9520110957004161 |
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name: Cosine F1 |
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- type: cosine_f1_threshold |
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value: 0.8184970021247864 |
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name: Cosine F1 Threshold |
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- type: cosine_precision |
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value: 0.9377049180327869 |
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name: Cosine Precision |
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- type: cosine_recall |
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value: 0.9667605633802817 |
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name: Cosine Recall |
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- type: cosine_ap |
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value: 0.9913975998012987 |
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name: Cosine Ap |
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- type: cosine_mcc |
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value: 0.9287314743364886 |
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name: Cosine Mcc |
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--- |
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# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
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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 [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) 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. |
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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:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 8d6b950845285729817bf8e1af1861502c2fed0c --> |
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- **Maximum Sequence Length:** 128 tokens |
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- **Output Dimensionality:** 384 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) |
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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': 128, 'do_lower_case': False}) with Transformer model: BertModel |
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(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}) |
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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-search-miniLM-v1-8") |
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# Run inference |
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sentences = [ |
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'Arus dana Q3 2006', |
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'Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah)', |
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'Rata-Rata Pengeluaran per Kapita Sebulan di Daerah Perkotaan Menurut Kelompok Barang dan Golongan Pengeluaran per Kapita Sebulan, 2000-2012', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 384] |
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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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#### Binary Classification |
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* Datasets: `allstats-semantic-mini-v1_test` and `allstats-semantic-mini-v1_dev` |
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* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) |
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| Metric | allstats-semantic-mini-v1_test | allstats-semantic-mini-v1_dev | |
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|:--------------------------|:-------------------------------|:------------------------------| |
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| cosine_accuracy | 0.9814 | 0.9692 | |
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| cosine_accuracy_threshold | 0.8306 | 0.8363 | |
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| cosine_f1 | 0.9713 | 0.952 | |
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| cosine_f1_threshold | 0.8306 | 0.8185 | |
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| cosine_precision | 0.9779 | 0.9377 | |
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| cosine_recall | 0.9648 | 0.9668 | |
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| **cosine_ap** | **0.993** | **0.9914** | |
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| cosine_mcc | 0.9576 | 0.9287 | |
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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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#### query-hard-pos-neg-doc-pairs-statictable |
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* Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [7b28b96](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/7b28b964daa3073a4d012d1ffca46ecd4f26bb5f) |
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* Size: 25,580 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: 7 tokens</li><li>mean: 20.14 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 24.9 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>0: ~70.80%</li><li>1: ~29.20%</li></ul> | |
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* Samples: |
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| query | doc | label | |
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|:-------------------------------------------------------------------------|:----------------------------------------------|:---------------| |
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| <code>Status pekerjaan utama penduduk usia 15+ yang bekerja, 2020</code> | <code>Jumlah Penghuni Lapas per Kanwil</code> | <code>0</code> | |
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| <code>status pekerjaan utama penduduk usia 15+ yang bekerja, 2020</code> | <code>Jumlah Penghuni Lapas per Kanwil</code> | <code>0</code> | |
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| <code>STATUS PEKERJAAN UTAMA PENDUDUK USIA 15+ YANG BEKERJA, 2020</code> | <code>Jumlah Penghuni Lapas per Kanwil</code> | <code>0</code> | |
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* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) |
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|
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### Evaluation Dataset |
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#### query-hard-pos-neg-doc-pairs-statictable |
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* Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [7b28b96](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/7b28b964daa3073a4d012d1ffca46ecd4f26bb5f) |
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* Size: 5,479 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 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: 7 tokens</li><li>mean: 20.78 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.28 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~71.50%</li><li>1: ~28.50%</li></ul> | |
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* Samples: |
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| query | doc | label | |
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|:-----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <code>Bagaimana perbandingan PNS pria dan wanita di berbagai golongan tahun 2014?</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017</code> | <code>0</code> | |
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| <code>bagaimana perbandingan pns pria dan wanita di berbagai golongan tahun 2014?</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017</code> | <code>0</code> | |
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| <code>BAGAIMANA PERBANDINGAN PNS PRIA DAN WANITA DI BERBAGAI GOLONGAN TAHUN 2014?</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017</code> | <code>0</code> | |
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* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) |
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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`: 64 |
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- `per_device_eval_batch_size`: 64 |
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- `num_train_epochs`: 1 |
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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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|
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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`: 64 |
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- `per_device_eval_batch_size`: 64 |
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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`: 1 |
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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-mini-v1_test_cosine_ap | allstats-semantic-mini-v1_dev_cosine_ap | |
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|:-------:|:-------:|:-------------:|:---------------:|:----------------------------------------:|:---------------------------------------:| |
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| -1 | -1 | - | - | 0.8910 | - | |
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| 0 | 0 | - | 2.2466 | - | 0.8789 | |
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| 0.05 | 20 | 1.49 | 0.9117 | - | 0.9125 | |
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| 0.1 | 40 | 0.6482 | 0.4372 | - | 0.9671 | |
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| 0.15 | 60 | 0.2562 | 0.3121 | - | 0.9769 | |
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| 0.2 | 80 | 0.1642 | 0.2737 | - | 0.9789 | |
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| 0.25 | 100 | 0.1716 | 0.2185 | - | 0.9864 | |
|
| 0.3 | 120 | 0.0883 | 0.2888 | - | 0.9827 | |
|
| 0.35 | 140 | 0.1069 | 0.1778 | - | 0.9868 | |
|
| 0.4 | 160 | 0.0532 | 0.1926 | - | 0.9869 | |
|
| 0.45 | 180 | 0.1053 | 0.2130 | - | 0.9856 | |
|
| 0.5 | 200 | 0.061 | 0.1592 | - | 0.9895 | |
|
| 0.55 | 220 | 0.1283 | 0.1529 | - | 0.9888 | |
|
| 0.6 | 240 | 0.0244 | 0.1601 | - | 0.9886 | |
|
| 0.65 | 260 | 0.0274 | 0.1692 | - | 0.9875 | |
|
| 0.7 | 280 | 0.0796 | 0.1668 | - | 0.9879 | |
|
| 0.75 | 300 | 0.0471 | 0.1505 | - | 0.9883 | |
|
| 0.8 | 320 | 0.0374 | 0.1375 | - | 0.9897 | |
|
| 0.85 | 340 | 0.0221 | 0.1471 | - | 0.9898 | |
|
| 0.9 | 360 | 0.0089 | 0.1365 | - | 0.9911 | |
|
| 0.95 | 380 | 0.04 | 0.1362 | - | 0.9912 | |
|
| **1.0** | **400** | **0.0285** | **0.1318** | **-** | **0.9914** | |
|
| -1 | -1 | - | - | 0.9930 | - | |
|
|
|
* The bold row denotes the saved checkpoint. |
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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 |
|
- Accelerate: 1.3.0 |
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- Datasets: 3.2.0 |
|
- Tokenizers: 0.21.0 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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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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``` |
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