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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: 0.9649571089614893 |
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name: Cosine Accuracy |
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- type: cosine_accuracy_threshold |
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value: 0.688197910785675 |
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name: Cosine Accuracy Threshold |
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- type: cosine_f1 |
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value: 0.9462184873949578 |
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name: Cosine F1 |
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- type: cosine_f1_threshold |
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value: 0.688197910785675 |
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name: Cosine F1 Threshold |
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- type: cosine_precision |
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value: 0.9409470752089136 |
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name: Cosine Precision |
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- type: cosine_recall |
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value: 0.9515492957746479 |
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name: Cosine Recall |
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- type: cosine_ap |
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value: 0.9858302481584482 |
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name: Cosine Ap |
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- type: cosine_mcc |
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value: 0.9202633777403256 |
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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: 0.9651396240189816 |
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name: Cosine Accuracy |
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- type: cosine_accuracy_threshold |
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value: 0.6833629608154297 |
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name: Cosine Accuracy Threshold |
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- type: cosine_f1 |
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value: 0.9464836088540207 |
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name: Cosine F1 |
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- type: cosine_f1_threshold |
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value: 0.6833629608154297 |
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name: Cosine F1 Threshold |
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- type: cosine_precision |
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value: 0.9414715719063546 |
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name: Cosine Precision |
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- type: cosine_recall |
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value: 0.9515492957746479 |
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name: Cosine Recall |
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- type: cosine_ap |
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value: 0.9862354589024407 |
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name: Cosine Ap |
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- type: cosine_mcc |
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value: 0.9206641526376831 |
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name: Cosine Mcc |
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--- |
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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-v1-3") |
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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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|
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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 | 0.965 | 0.9651 | |
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| cosine_accuracy_threshold | 0.6882 | 0.6834 | |
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| cosine_f1 | 0.9462 | 0.9465 | |
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| cosine_f1_threshold | 0.6882 | 0.6834 | |
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| cosine_precision | 0.9409 | 0.9415 | |
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| cosine_recall | 0.9515 | 0.9515 | |
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| **cosine_ap** | **0.9858** | **0.9862** | |
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| cosine_mcc | 0.9203 | 0.9207 | |
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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`: 16 |
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- `per_device_eval_batch_size`: 16 |
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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`: 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`: 1.0 |
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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 | - | - | 0.8789 | - | |
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| 0 | 0 | - | 0.4455 | - | 0.8789 | |
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| 0.0125 | 20 | 0.4484 | 0.3363 | - | 0.8893 | |
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| 0.0250 | 40 | 0.1921 | 0.2230 | - | 0.9052 | |
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| 0.0375 | 60 | 0.1779 | 0.1435 | - | 0.9440 | |
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| 0.0500 | 80 | 0.1047 | 0.1269 | - | 0.9511 | |
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| 0.0625 | 100 | 0.0669 | 0.1498 | - | 0.9445 | |
|
| 0.0750 | 120 | 0.1662 | 0.1028 | - | 0.9630 | |
|
| 0.0876 | 140 | 0.0774 | 0.1115 | - | 0.9589 | |
|
| 0.1001 | 160 | 0.0947 | 0.1204 | - | 0.9500 | |
|
| 0.1126 | 180 | 0.1285 | 0.1464 | - | 0.9456 | |
|
| 0.1251 | 200 | 0.0793 | 0.1024 | - | 0.9600 | |
|
| 0.1376 | 220 | 0.0792 | 0.0992 | - | 0.9607 | |
|
| 0.1501 | 240 | 0.0696 | 0.0931 | - | 0.9642 | |
|
| 0.1626 | 260 | 0.0692 | 0.1205 | - | 0.9538 | |
|
| 0.1751 | 280 | 0.1015 | 0.0980 | - | 0.9629 | |
|
| 0.1876 | 300 | 0.0628 | 0.1001 | - | 0.9634 | |
|
| 0.2001 | 320 | 0.0335 | 0.1094 | - | 0.9650 | |
|
| 0.2126 | 340 | 0.0668 | 0.0941 | - | 0.9673 | |
|
| 0.2251 | 360 | 0.0662 | 0.0765 | - | 0.9748 | |
|
| 0.2376 | 380 | 0.0251 | 0.0674 | - | 0.9784 | |
|
| 0.2502 | 400 | 0.0771 | 0.0667 | - | 0.9805 | |
|
| 0.2627 | 420 | 0.0363 | 0.0576 | - | 0.9785 | |
|
| 0.2752 | 440 | 0.0762 | 0.0787 | - | 0.9726 | |
|
| 0.2877 | 460 | 0.0475 | 0.0649 | - | 0.9773 | |
|
| 0.3002 | 480 | 0.0086 | 0.0692 | - | 0.9760 | |
|
| 0.3127 | 500 | 0.0242 | 0.0636 | - | 0.9771 | |
|
| 0.3252 | 520 | 0.0342 | 0.0700 | - | 0.9758 | |
|
| 0.3377 | 540 | 0.0568 | 0.0547 | - | 0.9792 | |
|
| 0.3502 | 560 | 0.0286 | 0.0508 | - | 0.9808 | |
|
| 0.3627 | 580 | 0.0426 | 0.0518 | - | 0.9823 | |
|
| 0.3752 | 600 | 0.03 | 0.0553 | - | 0.9806 | |
|
| 0.3877 | 620 | 0.0146 | 0.0826 | - | 0.9748 | |
|
| 0.4003 | 640 | 0.0417 | 0.0667 | - | 0.9779 | |
|
| 0.4128 | 660 | 0.0081 | 0.0667 | - | 0.9775 | |
|
| 0.4253 | 680 | 0.0094 | 0.0704 | - | 0.9798 | |
|
| 0.4378 | 700 | 0.0225 | 0.0525 | - | 0.9841 | |
|
| 0.4503 | 720 | 0.0217 | 0.0462 | - | 0.9861 | |
|
| 0.4628 | 740 | 0.011 | 0.0466 | - | 0.9858 | |
|
| 0.4753 | 760 | 0.0191 | 0.0495 | - | 0.9846 | |
|
| 0.4878 | 780 | 0.0146 | 0.0478 | - | 0.9847 | |
|
| 0.5003 | 800 | 0.0076 | 0.0424 | - | 0.9852 | |
|
| 0.5128 | 820 | 0.035 | 0.0549 | - | 0.9821 | |
|
| 0.5253 | 840 | 0.0321 | 0.0551 | - | 0.9796 | |
|
| 0.5378 | 860 | 0.0241 | 0.0559 | - | 0.9781 | |
|
| 0.5503 | 880 | 0.0335 | 0.0525 | - | 0.9792 | |
|
| 0.5629 | 900 | 0.0125 | 0.0539 | - | 0.9799 | |
|
| 0.5754 | 920 | 0.0154 | 0.0512 | - | 0.9823 | |
|
| 0.5879 | 940 | 0.0133 | 0.0497 | - | 0.9824 | |
|
| 0.6004 | 960 | 0.0072 | 0.0532 | - | 0.9821 | |
|
| 0.6129 | 980 | 0.0192 | 0.0520 | - | 0.9809 | |
|
| 0.6254 | 1000 | 0.0199 | 0.0503 | - | 0.9811 | |
|
| 0.6379 | 1020 | 0.0069 | 0.0484 | - | 0.9824 | |
|
| 0.6504 | 1040 | 0.0065 | 0.0514 | - | 0.9806 | |
|
| 0.6629 | 1060 | 0.0098 | 0.0479 | - | 0.9834 | |
|
| 0.6754 | 1080 | 0.0 | 0.0480 | - | 0.9841 | |
|
| 0.6879 | 1100 | 0.0247 | 0.0508 | - | 0.9835 | |
|
| 0.7004 | 1120 | 0.0137 | 0.0481 | - | 0.9842 | |
|
| 0.7129 | 1140 | 0.0068 | 0.0512 | - | 0.9838 | |
|
| 0.7255 | 1160 | 0.0182 | 0.0473 | - | 0.9851 | |
|
| 0.7380 | 1180 | 0.0129 | 0.0442 | - | 0.9859 | |
|
| 0.7505 | 1200 | 0.0 | 0.0436 | - | 0.9860 | |
|
| 0.7630 | 1220 | 0.0073 | 0.0439 | - | 0.9858 | |
|
| 0.7755 | 1240 | 0.0081 | 0.0441 | - | 0.9859 | |
|
| 0.7880 | 1260 | 0.0305 | 0.0460 | - | 0.9857 | |
|
| 0.8005 | 1280 | 0.0003 | 0.0486 | - | 0.9851 | |
|
| 0.8130 | 1300 | 0.0218 | 0.0501 | - | 0.9852 | |
|
| 0.8255 | 1320 | 0.0187 | 0.0435 | - | 0.9844 | |
|
| 0.8380 | 1340 | 0.0205 | 0.0437 | - | 0.9846 | |
|
| 0.8505 | 1360 | 0.0094 | 0.0442 | - | 0.9851 | |
|
| 0.8630 | 1380 | 0.0083 | 0.0426 | - | 0.9856 | |
|
| **0.8755** | **1400** | **0.0** | **0.0423** | **-** | **0.9858** | |
|
| 0.8881 | 1420 | 0.0 | 0.0424 | - | 0.9859 | |
|
| 0.9006 | 1440 | 0.0073 | 0.0428 | - | 0.9859 | |
|
| 0.9131 | 1460 | 0.0075 | 0.0441 | - | 0.9859 | |
|
| 0.9256 | 1480 | 0.0177 | 0.0447 | - | 0.9858 | |
|
| 0.9381 | 1500 | 0.0 | 0.0438 | - | 0.9858 | |
|
| 0.9506 | 1520 | 0.0 | 0.0438 | - | 0.9858 | |
|
| 0.9631 | 1540 | 0.0072 | 0.0440 | - | 0.9860 | |
|
| 0.9756 | 1560 | 0.0101 | 0.0436 | - | 0.9861 | |
|
| 0.9881 | 1580 | 0.0277 | 0.0437 | - | 0.9862 | |
|
| -1 | -1 | - | - | 0.9858 | - | |
|
|
|
* The bold row denotes the saved checkpoint. |
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|
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### 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 |
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### BibTeX |
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|
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#### Sentence Transformers |
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```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
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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", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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