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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:1432 |
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- loss:MultipleNegativesRankingLoss |
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base_model: denaya/indoSBERT-large |
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widget: |
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- source_sentence: 'Input-output domestik Indonesia: 17 sektor usaha, harga produsen, |
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data tahun 2016 (juta Rp)' |
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sentences: |
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- 'Impor Besi dan Baja Menurut Negara Asal Utama, 2017-2023 ' |
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- 'IHK dan Rata-rata Upah per Bulan Buruh Hotel di Bawah Mandor (Supervisor), 1996-2014 |
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(1996=100) ' |
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- 'Tabel Input-Output Indonesia Transaksi Domestik Atas Dasar Harga Produsen (17 |
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Lapangan Usaha), 2016 (Juta Rupiah) ' |
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- source_sentence: 'Gaji bulanan: beda umur, beda jenis pekerjaan (9 sektor), 2017' |
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sentences: |
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- 'Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur |
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dan Lapangan Pekerjaan Utama di 9 Sektor (Rupiah), 2017 ' |
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- 'Ekspor Rumput Laut dan Ganggang Lainnya menurut Negara Tujuan Utama, 2012-2023 ' |
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- 'Rata-Rata Harga Valuta Asing Terpilih menurut Provinsi 2017 ' |
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- source_sentence: Ringkasan aliran dana kuartal terakhir 2009 dalam Rupiah |
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sentences: |
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- 'Jumlah Perahu/Kapal, Luas Usaha Budidaya dan Produksi menurut Sub Sektor Perikanan, |
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2002-2016 ' |
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- 'Jumlah Pendapatan Menurut Golongan Rumah Tangga (miliar rupiah) 2000, 2005, dan |
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2008 ' |
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- 'Ringkasan Neraca Arus Dana, Triwulan IV, 2009, (Miliar Rupiah) ' |
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- source_sentence: Berapa total transaksi (harga pembeli) untuk 9 sektor ekonomi di |
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Indonesia tahun 2005? (miliar rupiah) |
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sentences: |
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- 'Jumlah Rumah Tangga Perikanan Budidaya Menurut Provinsi dan Jenis Budidaya, 2000-2016 ' |
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- 'Transaksi Total Atas Dasar Harga Pembeli 9 Sektor Ekonomi (miliar rupiah), 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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- source_sentence: Bagaimana kaitan antara pendidikan dan kegiatan mingguan penduduk |
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usia 15+ pada tahun 2022? |
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sentences: |
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- 'Persentase Perkembangan Distribusi Pengeluaran ' |
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- 'Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Lapangan Pekerjaan |
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Utama (ribu rupiah), 2018 ' |
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- 'Penduduk Berumur 15 Tahun Ke Atas Menurut Pendidikan Tertinggi yang Ditamatkan |
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dan Jenis Kegiatan Selama Seminggu yang Lalu, 2008-2024 ' |
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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@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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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 denaya/indoSBERT-large |
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results: |
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- task: |
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type: information-retrieval |
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name: Information Retrieval |
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dataset: |
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name: eval |
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type: eval |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.9120521172638436 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.990228013029316 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.993485342019544 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.996742671009772 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.9120521172638436 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.3572204125950054 |
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name: Cosine Precision@3 |
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- type: cosine_precision@5 |
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value: 0.23778501628664495 |
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name: Cosine Precision@5 |
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- type: cosine_precision@10 |
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value: 0.13745928338762217 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.7097252402956855 |
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name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.7867346590488319 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.8052359035035943 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.8221312325947948 |
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name: Cosine Recall@10 |
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- type: cosine_ndcg@10 |
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value: 0.8348212945928647 |
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name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.9497052892818366 |
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name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.7729410950742827 |
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name: Cosine Map@100 |
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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: quora duplicates dev |
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type: quora_duplicates_dev |
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metrics: |
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- type: cosine_accuracy |
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value: 0.9914529914529915 |
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name: Cosine Accuracy |
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- type: cosine_accuracy_threshold |
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value: 0.31953397393226624 |
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name: Cosine Accuracy Threshold |
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- type: cosine_f1 |
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value: 0.9850953206239168 |
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name: Cosine F1 |
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- type: cosine_f1_threshold |
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value: 0.30364981293678284 |
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name: Cosine F1 Threshold |
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- type: cosine_precision |
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value: 0.988865692414753 |
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name: Cosine Precision |
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- type: cosine_recall |
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value: 0.981353591160221 |
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name: Cosine Recall |
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- type: cosine_ap |
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value: 0.9956970583311449 |
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name: Cosine Ap |
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- type: cosine_mcc |
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value: 0.9791180702139771 |
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name: Cosine Mcc |
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--- |
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|
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# SentenceTransformer based on denaya/indoSBERT-large |
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|
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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). 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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|
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## Model Details |
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|
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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:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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|
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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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|
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### Full Model Architecture |
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|
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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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|
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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-large-bpstable-v1") |
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# Run inference |
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sentences = [ |
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'Bagaimana kaitan antara pendidikan dan kegiatan mingguan penduduk usia 15+ pada tahun 2022?', |
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'Penduduk Berumur 15 Tahun Ke Atas Menurut Pendidikan Tertinggi yang Ditamatkan dan Jenis Kegiatan Selama Seminggu yang Lalu, 2008-2024 ', |
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'Persentase Perkembangan Distribusi Pengeluaran ', |
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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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|
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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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<!-- |
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### Out-of-Scope Use |
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|
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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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|
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## Evaluation |
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|
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### Metrics |
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|
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#### Information Retrieval |
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* Dataset: `eval` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.9121 | |
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| cosine_accuracy@3 | 0.9902 | |
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| cosine_accuracy@5 | 0.9935 | |
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| cosine_accuracy@10 | 0.9967 | |
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| cosine_precision@1 | 0.9121 | |
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| cosine_precision@3 | 0.3572 | |
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| cosine_precision@5 | 0.2378 | |
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| cosine_precision@10 | 0.1375 | |
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| cosine_recall@1 | 0.7097 | |
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| cosine_recall@3 | 0.7867 | |
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| cosine_recall@5 | 0.8052 | |
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| cosine_recall@10 | 0.8221 | |
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| **cosine_ndcg@10** | **0.8348** | |
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| cosine_mrr@10 | 0.9497 | |
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| cosine_map@100 | 0.7729 | |
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#### Binary Classification |
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* Dataset: `quora_duplicates_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 | Value | |
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|:--------------------------|:-----------| |
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| cosine_accuracy | 0.9915 | |
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| cosine_accuracy_threshold | 0.3195 | |
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| cosine_f1 | 0.9851 | |
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| cosine_f1_threshold | 0.3036 | |
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| cosine_precision | 0.9889 | |
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| cosine_recall | 0.9814 | |
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| **cosine_ap** | **0.9957** | |
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| cosine_mcc | 0.9791 | |
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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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#### Unnamed Dataset |
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* Size: 1,432 training samples |
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* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | 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: 16.84 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 20.88 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>1: 100.00%</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | label | |
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|:-------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|:---------------| |
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| <code>Average monthly net wage/salary of employees by age group and type of work (Rupiah), 2018</code> | <code>Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur dan Jenis Pekerjaan (Rupiah), 2018 </code> | <code>1</code> | |
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| <code>Cek average real wage buruh industri pengolahan (level bawah) sekitar tahun 2009</code> | <code>Rata-rata Upah Riil Per Bulan Buruh Industri Pengolahan di Bawah Mandor, 2005-2014 (1996=100) </code> | <code>1</code> | |
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| <code>Dimana saya bisa lihat rekapitulasi dokumen RPB kabupaten/kota?</code> | <code>Rekap Dokumen RPB Kabupaten/Kota </code> | <code>1</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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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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|
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 16 |
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- `per_device_eval_batch_size`: 16 |
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- `num_train_epochs`: 30 |
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- `fp16`: True |
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- `multi_dataset_batch_sampler`: round_robin |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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|
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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`: 16 |
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- `per_device_eval_batch_size`: 16 |
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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 |
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- `num_train_epochs`: 30 |
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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`: 0.0 |
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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`: False |
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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 |
|
- `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 |
|
- `torch_compile_backend`: None |
|
- `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 |
|
- `eval_on_start`: False |
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- `use_liger_kernel`: False |
|
- `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`: round_robin |
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|
|
</details> |
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|
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### Training Logs |
|
<details><summary>Click to expand</summary> |
|
|
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| Epoch | Step | Training Loss | eval_cosine_ndcg@10 | quora_duplicates_dev_cosine_ap | |
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|:-------:|:----:|:-------------:|:-------------------:|:------------------------------:| |
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| 0.2222 | 20 | - | 0.7769 | - | |
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| 0.4444 | 40 | - | 0.8167 | - | |
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| 0.6667 | 60 | - | 0.8221 | - | |
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| 0.8889 | 80 | - | 0.8282 | - | |
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| 1.0 | 90 | - | 0.8256 | - | |
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| 1.1111 | 100 | - | 0.8278 | - | |
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| 1.3333 | 120 | - | 0.8388 | - | |
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| 1.5556 | 140 | - | 0.8347 | - | |
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| 1.7778 | 160 | - | 0.8351 | - | |
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| 2.0 | 180 | - | 0.8407 | - | |
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| 2.2222 | 200 | - | 0.8302 | - | |
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| 2.4444 | 220 | - | 0.8261 | - | |
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| 2.6667 | 240 | - | 0.8217 | - | |
|
| 2.8889 | 260 | - | 0.8161 | - | |
|
| 3.0 | 270 | - | 0.8143 | - | |
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| 3.1111 | 280 | - | 0.8133 | - | |
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| 3.3333 | 300 | - | 0.8259 | - | |
|
| 3.5556 | 320 | - | 0.8342 | - | |
|
| 3.7778 | 340 | - | 0.8267 | - | |
|
| 4.0 | 360 | - | 0.8190 | - | |
|
| 4.2222 | 380 | - | 0.8193 | - | |
|
| 4.4444 | 400 | - | 0.8281 | - | |
|
| 4.6667 | 420 | - | 0.8283 | - | |
|
| 4.8889 | 440 | - | 0.8197 | - | |
|
| 5.0 | 450 | - | 0.8211 | - | |
|
| 5.1111 | 460 | - | 0.8118 | - | |
|
| 5.3333 | 480 | - | 0.8298 | - | |
|
| 5.5556 | 500 | 0.0412 | 0.8283 | - | |
|
| 5.7778 | 520 | - | 0.8264 | - | |
|
| 6.0 | 540 | - | 0.8271 | - | |
|
| 6.2222 | 560 | - | 0.8243 | - | |
|
| 6.4444 | 580 | - | 0.8256 | - | |
|
| 6.6667 | 600 | - | 0.8356 | - | |
|
| 6.8889 | 620 | - | 0.8332 | - | |
|
| 7.0 | 630 | - | 0.8250 | - | |
|
| 7.1111 | 640 | - | 0.8179 | - | |
|
| 7.3333 | 660 | - | 0.8356 | - | |
|
| 7.5556 | 680 | - | 0.8400 | - | |
|
| 7.7778 | 700 | - | 0.8349 | - | |
|
| 8.0 | 720 | - | 0.8281 | - | |
|
| 8.2222 | 740 | - | 0.8330 | - | |
|
| 8.4444 | 760 | - | 0.8338 | - | |
|
| 8.6667 | 780 | - | 0.8338 | - | |
|
| 8.8889 | 800 | - | 0.8344 | - | |
|
| 9.0 | 810 | - | 0.8319 | - | |
|
| 9.1111 | 820 | - | 0.8328 | - | |
|
| 9.3333 | 840 | - | 0.8325 | - | |
|
| 9.5556 | 860 | - | 0.8375 | - | |
|
| 9.7778 | 880 | - | 0.8306 | - | |
|
| 10.0 | 900 | - | 0.8263 | - | |
|
| 10.2222 | 920 | - | 0.8280 | - | |
|
| 10.4444 | 940 | - | 0.8272 | - | |
|
| 10.6667 | 960 | - | 0.8280 | - | |
|
| 10.8889 | 980 | - | 0.8313 | - | |
|
| 11.0 | 990 | - | 0.8307 | - | |
|
| 11.1111 | 1000 | 0.0198 | 0.8324 | - | |
|
| 11.3333 | 1020 | - | 0.8303 | - | |
|
| 11.5556 | 1040 | - | 0.8262 | - | |
|
| 11.7778 | 1060 | - | 0.8294 | - | |
|
| 12.0 | 1080 | - | 0.8309 | - | |
|
| 12.2222 | 1100 | - | 0.8274 | - | |
|
| 12.4444 | 1120 | - | 0.8312 | - | |
|
| 12.6667 | 1140 | - | 0.8371 | - | |
|
| 12.8889 | 1160 | - | 0.8408 | - | |
|
| 13.0 | 1170 | - | 0.8374 | - | |
|
| 13.1111 | 1180 | - | 0.8344 | - | |
|
| 13.3333 | 1200 | - | 0.8341 | - | |
|
| 13.5556 | 1220 | - | 0.8333 | - | |
|
| 13.7778 | 1240 | - | 0.8388 | - | |
|
| 14.0 | 1260 | - | 0.8414 | - | |
|
| 14.2222 | 1280 | - | 0.8344 | - | |
|
| 14.4444 | 1300 | - | 0.8328 | - | |
|
| 14.6667 | 1320 | - | 0.8340 | - | |
|
| 14.8889 | 1340 | - | 0.8317 | - | |
|
| 15.0 | 1350 | - | 0.8260 | - | |
|
| 15.1111 | 1360 | - | 0.8252 | - | |
|
| 15.3333 | 1380 | - | 0.8244 | - | |
|
| 15.5556 | 1400 | - | 0.8269 | - | |
|
| 15.7778 | 1420 | - | 0.8275 | - | |
|
| 16.0 | 1440 | - | 0.8281 | - | |
|
| 16.2222 | 1460 | - | 0.8294 | - | |
|
| 16.4444 | 1480 | - | 0.8299 | - | |
|
| 16.6667 | 1500 | 0.0136 | 0.8318 | - | |
|
| 16.8889 | 1520 | - | 0.8320 | - | |
|
| 17.0 | 1530 | - | 0.8332 | - | |
|
| 17.1111 | 1540 | - | 0.8337 | - | |
|
| 17.3333 | 1560 | - | 0.8299 | - | |
|
| 17.5556 | 1580 | - | 0.8283 | - | |
|
| 17.7778 | 1600 | - | 0.8309 | - | |
|
| 18.0 | 1620 | - | 0.8329 | - | |
|
| 18.2222 | 1640 | - | 0.8317 | - | |
|
| 18.4444 | 1660 | - | 0.8313 | - | |
|
| 18.6667 | 1680 | - | 0.8317 | - | |
|
| 18.8889 | 1700 | - | 0.8356 | - | |
|
| 19.0 | 1710 | - | 0.8345 | - | |
|
| 19.1111 | 1720 | - | 0.8358 | - | |
|
| 19.3333 | 1740 | - | 0.8334 | - | |
|
| 19.5556 | 1760 | - | 0.8335 | - | |
|
| 19.7778 | 1780 | - | 0.8318 | - | |
|
| 20.0 | 1800 | - | 0.8326 | - | |
|
| 20.2222 | 1820 | - | 0.8318 | - | |
|
| 20.4444 | 1840 | - | 0.8335 | - | |
|
| 20.6667 | 1860 | - | 0.8333 | - | |
|
| 20.8889 | 1880 | - | 0.8335 | - | |
|
| 21.0 | 1890 | - | 0.8341 | - | |
|
| 21.1111 | 1900 | - | 0.8341 | - | |
|
| 21.3333 | 1920 | - | 0.8355 | - | |
|
| 21.5556 | 1940 | - | 0.8360 | - | |
|
| 21.7778 | 1960 | - | 0.8343 | - | |
|
| 22.0 | 1980 | - | 0.8351 | - | |
|
| 22.2222 | 2000 | 0.015 | 0.8342 | - | |
|
| 22.4444 | 2020 | - | 0.8342 | - | |
|
| 22.6667 | 2040 | - | 0.8339 | - | |
|
| 22.8889 | 2060 | - | 0.8342 | - | |
|
| 23.0 | 2070 | - | 0.8345 | - | |
|
| 23.1111 | 2080 | - | 0.8354 | - | |
|
| 23.3333 | 2100 | - | 0.8366 | - | |
|
| 23.5556 | 2120 | - | 0.8379 | - | |
|
| 23.7778 | 2140 | - | 0.8386 | - | |
|
| 24.0 | 2160 | - | 0.8367 | - | |
|
| 24.2222 | 2180 | - | 0.8357 | - | |
|
| 24.4444 | 2200 | - | 0.8372 | - | |
|
| 24.6667 | 2220 | - | 0.8377 | - | |
|
| 24.8889 | 2240 | - | 0.8373 | - | |
|
| 25.0 | 2250 | - | 0.8367 | - | |
|
| 25.1111 | 2260 | - | 0.8366 | - | |
|
| 25.3333 | 2280 | - | 0.8369 | - | |
|
| 25.5556 | 2300 | - | 0.8373 | - | |
|
| 25.7778 | 2320 | - | 0.8366 | - | |
|
| 26.0 | 2340 | - | 0.8354 | - | |
|
| 26.2222 | 2360 | - | 0.8347 | - | |
|
| 26.4444 | 2380 | - | 0.8344 | - | |
|
| 26.6667 | 2400 | - | 0.8341 | - | |
|
| 26.8889 | 2420 | - | 0.8343 | - | |
|
| 27.0 | 2430 | - | 0.8344 | - | |
|
| 27.1111 | 2440 | - | 0.8345 | - | |
|
| 27.3333 | 2460 | - | 0.8344 | - | |
|
| 27.5556 | 2480 | - | 0.8347 | - | |
|
| 27.7778 | 2500 | 0.0136 | 0.8342 | - | |
|
| 28.0 | 2520 | - | 0.8347 | - | |
|
| 28.2222 | 2540 | - | 0.8346 | - | |
|
| 28.4444 | 2560 | - | 0.8346 | - | |
|
| 28.6667 | 2580 | - | 0.8347 | - | |
|
| 28.8889 | 2600 | - | 0.8348 | - | |
|
| 29.0 | 2610 | - | 0.8348 | - | |
|
| 29.1111 | 2620 | - | 0.8348 | - | |
|
| 29.3333 | 2640 | - | 0.8348 | - | |
|
| 29.5556 | 2660 | - | 0.8348 | - | |
|
| 29.7778 | 2680 | - | 0.8348 | - | |
|
| 30.0 | 2700 | - | 0.8348 | - | |
|
| -1 | -1 | - | - | 0.9957 | |
|
|
|
</details> |
|
|
|
### 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", |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
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