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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: denaya/indoSBERT-large |
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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 denaya/indoSBERT-large |
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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 large v1 test |
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type: allstats-semantic-large-v1_test |
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metrics: |
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- type: cosine_accuracy |
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value: 0.9834364761558063 |
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name: Cosine Accuracy |
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- type: cosine_accuracy_threshold |
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value: 0.7773222327232361 |
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name: Cosine Accuracy Threshold |
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- type: cosine_f1 |
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value: 0.9745739033249511 |
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name: Cosine F1 |
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- type: cosine_f1_threshold |
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value: 0.7773222327232361 |
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name: Cosine F1 Threshold |
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- type: cosine_precision |
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value: 0.9748462828395752 |
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name: Cosine Precision |
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- type: cosine_recall |
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value: 0.9743016759776536 |
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name: Cosine Recall |
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- type: cosine_ap |
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value: 0.9959810762137397 |
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name: Cosine Ap |
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- type: cosine_mcc |
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value: 0.9622916280716365 |
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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 large v1 dev |
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type: allstats-semantic-large-v1_dev |
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metrics: |
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- type: cosine_accuracy |
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value: 0.9760905274685161 |
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name: Cosine Accuracy |
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- type: cosine_accuracy_threshold |
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value: 0.7572722434997559 |
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name: Cosine Accuracy Threshold |
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- type: cosine_f1 |
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value: 0.9640997533570841 |
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name: Cosine F1 |
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- type: cosine_f1_threshold |
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value: 0.7572722434997559 |
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name: Cosine F1 Threshold |
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- type: cosine_precision |
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value: 0.9386339381003201 |
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name: Cosine Precision |
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- type: cosine_recall |
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value: 0.9909859154929578 |
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name: Cosine Recall |
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- type: cosine_ap |
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value: 0.9953499585582108 |
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name: Cosine Ap |
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- type: cosine_mcc |
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value: 0.9469795586519781 |
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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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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large) 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 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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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [denaya/indoSBERT-large](https://huggingface.co/denaya/indoSBERT-large) <!-- at revision 5c64d43f07f7054dfbf33d226b3066414b6ebc4a --> |
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- **Maximum Sequence Length:** 256 tokens |
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- **Output Dimensionality:** 256 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [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': 256, 'do_lower_case': False}) with Transformer model: BertModel |
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(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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(2): Dense({'in_features': 1024, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("yahyaabd/allstats-search-large-v1-32-2") |
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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, 256] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Binary Classification |
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* Datasets: `allstats-semantic-large-v1_test` and `allstats-semantic-large-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-large-v1_test | allstats-semantic-large-v1_dev | |
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|:--------------------------|:--------------------------------|:-------------------------------| |
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| cosine_accuracy | 0.9834 | 0.9761 | |
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| cosine_accuracy_threshold | 0.7773 | 0.7573 | |
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| cosine_f1 | 0.9746 | 0.9641 | |
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| cosine_f1_threshold | 0.7773 | 0.7573 | |
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| cosine_precision | 0.9748 | 0.9386 | |
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| cosine_recall | 0.9743 | 0.991 | |
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| **cosine_ap** | **0.996** | **0.9953** | |
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| cosine_mcc | 0.9623 | 0.947 | |
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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: 6 tokens</li><li>mean: 17.12 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 20.47 tokens</li><li>max: 42 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: 17.85 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 21.2 tokens</li><li>max: 31 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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|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `num_train_epochs`: 2 |
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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`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 2 |
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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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|
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</details> |
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|
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### Training Logs |
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| Epoch | Step | Training Loss | Validation Loss | allstats-semantic-large-v1_test_cosine_ap | allstats-semantic-large-v1_dev_cosine_ap | |
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|:--------:|:-------:|:-------------:|:---------------:|:-----------------------------------------:|:----------------------------------------:| |
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| -1 | -1 | - | - | 0.9750 | - | |
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| 0 | 0 | - | 0.1850 | - | 0.9766 | |
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| 0.025 | 20 | 0.1581 | 0.1538 | - | 0.9789 | |
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| 0.05 | 40 | 0.1898 | 0.1200 | - | 0.9848 | |
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| 0.075 | 60 | 0.0647 | 0.1096 | - | 0.9855 | |
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| 0.1 | 80 | 0.118 | 0.1242 | - | 0.9831 | |
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| 0.125 | 100 | 0.0545 | 0.1301 | - | 0.9827 | |
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| 0.15 | 120 | 0.0646 | 0.1114 | - | 0.9862 | |
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| 0.175 | 140 | 0.0775 | 0.1005 | - | 0.9865 | |
|
| 0.2 | 160 | 0.0664 | 0.1234 | - | 0.9840 | |
|
| 0.225 | 180 | 0.067 | 0.1349 | - | 0.9850 | |
|
| 0.25 | 200 | 0.0823 | 0.1032 | - | 0.9877 | |
|
| 0.275 | 220 | 0.0895 | 0.1432 | - | 0.9808 | |
|
| 0.3 | 240 | 0.0666 | 0.1389 | - | 0.9809 | |
|
| 0.325 | 260 | 0.0872 | 0.1122 | - | 0.9844 | |
|
| 0.35 | 280 | 0.0551 | 0.1435 | - | 0.9838 | |
|
| 0.375 | 300 | 0.0919 | 0.1068 | - | 0.9886 | |
|
| 0.4 | 320 | 0.0437 | 0.0903 | - | 0.9861 | |
|
| 0.425 | 340 | 0.0619 | 0.1065 | - | 0.9850 | |
|
| 0.45 | 360 | 0.0469 | 0.1346 | - | 0.9844 | |
|
| 0.475 | 380 | 0.029 | 0.1351 | - | 0.9828 | |
|
| 0.5 | 400 | 0.0511 | 0.1123 | - | 0.9843 | |
|
| 0.525 | 420 | 0.0394 | 0.1434 | - | 0.9815 | |
|
| 0.55 | 440 | 0.0178 | 0.1577 | - | 0.9769 | |
|
| 0.575 | 460 | 0.047 | 0.1253 | - | 0.9796 | |
|
| 0.6 | 480 | 0.0066 | 0.1262 | - | 0.9791 | |
|
| 0.625 | 500 | 0.0383 | 0.1277 | - | 0.9814 | |
|
| 0.65 | 520 | 0.0084 | 0.1361 | - | 0.9845 | |
|
| 0.675 | 540 | 0.0409 | 0.1202 | - | 0.9872 | |
|
| 0.7 | 560 | 0.0372 | 0.1245 | - | 0.9854 | |
|
| 0.725 | 580 | 0.0353 | 0.1469 | - | 0.9817 | |
|
| 0.75 | 600 | 0.0429 | 0.1225 | - | 0.9836 | |
|
| 0.775 | 620 | 0.0595 | 0.1082 | - | 0.9862 | |
|
| 0.8 | 640 | 0.0266 | 0.0886 | - | 0.9903 | |
|
| 0.825 | 660 | 0.0178 | 0.0712 | - | 0.9918 | |
|
| **0.85** | **680** | **0.0567** | **0.0511** | **-** | **0.9936** | |
|
| 0.875 | 700 | 0.0142 | 0.0538 | - | 0.9916 | |
|
| 0.9 | 720 | 0.0136 | 0.0726 | - | 0.9890 | |
|
| 0.925 | 740 | 0.0192 | 0.0707 | - | 0.9884 | |
|
| 0.95 | 760 | 0.0253 | 0.0937 | - | 0.9872 | |
|
| 0.975 | 780 | 0.0149 | 0.0792 | - | 0.9878 | |
|
| 1.0 | 800 | 0.0231 | 0.0912 | - | 0.9879 | |
|
| 1.025 | 820 | 0.0 | 0.1030 | - | 0.9871 | |
|
| 1.05 | 840 | 0.0096 | 0.0990 | - | 0.9876 | |
|
| 1.075 | 860 | 0.0 | 0.1032 | - | 0.9868 | |
|
| 1.1 | 880 | 0.0 | 0.1037 | - | 0.9866 | |
|
| 1.125 | 900 | 0.0 | 0.1038 | - | 0.9866 | |
|
| 1.15 | 920 | 0.0 | 0.1038 | - | 0.9866 | |
|
| 1.175 | 940 | 0.0 | 0.1038 | - | 0.9866 | |
|
| 1.2 | 960 | 0.0121 | 0.1030 | - | 0.9895 | |
|
| 1.225 | 980 | 0.0 | 0.1035 | - | 0.9899 | |
|
| 1.25 | 1000 | 0.0 | 0.1040 | - | 0.9898 | |
|
| 1.275 | 1020 | 0.0 | 0.1049 | - | 0.9898 | |
|
| 1.3 | 1040 | 0.0 | 0.1049 | - | 0.9898 | |
|
| 1.325 | 1060 | 0.0067 | 0.1015 | - | 0.9903 | |
|
| 1.35 | 1080 | 0.0 | 0.1048 | - | 0.9901 | |
|
| 1.375 | 1100 | 0.0159 | 0.0956 | - | 0.9910 | |
|
| 1.4 | 1120 | 0.0067 | 0.0818 | - | 0.9926 | |
|
| 1.425 | 1140 | 0.0151 | 0.0838 | - | 0.9926 | |
|
| 1.45 | 1160 | 0.0 | 0.0889 | - | 0.9920 | |
|
| 1.475 | 1180 | 0.0 | 0.0894 | - | 0.9920 | |
|
| 1.5 | 1200 | 0.023 | 0.0696 | - | 0.9935 | |
|
| 1.525 | 1220 | 0.0 | 0.0693 | - | 0.9935 | |
|
| 1.55 | 1240 | 0.0 | 0.0711 | - | 0.9935 | |
|
| 1.575 | 1260 | 0.0 | 0.0711 | - | 0.9935 | |
|
| 1.6 | 1280 | 0.0 | 0.0711 | - | 0.9935 | |
|
| 1.625 | 1300 | 0.0176 | 0.0743 | - | 0.9936 | |
|
| 1.65 | 1320 | 0.0 | 0.0806 | - | 0.9931 | |
|
| 1.675 | 1340 | 0.0 | 0.0817 | - | 0.9931 | |
|
| 1.7 | 1360 | 0.007 | 0.0809 | - | 0.9929 | |
|
| 1.725 | 1380 | 0.0209 | 0.0700 | - | 0.9941 | |
|
| 1.75 | 1400 | 0.0068 | 0.0605 | - | 0.9949 | |
|
| 1.775 | 1420 | 0.0069 | 0.0564 | - | 0.9951 | |
|
| 1.8 | 1440 | 0.0097 | 0.0559 | - | 0.9953 | |
|
| 1.825 | 1460 | 0.0 | 0.0557 | - | 0.9953 | |
|
| 1.85 | 1480 | 0.0 | 0.0557 | - | 0.9953 | |
|
| 1.875 | 1500 | 0.0 | 0.0557 | - | 0.9953 | |
|
| 1.9 | 1520 | 0.0 | 0.0557 | - | 0.9953 | |
|
| 1.925 | 1540 | 0.0 | 0.0557 | - | 0.9953 | |
|
| 1.95 | 1560 | 0.0089 | 0.0544 | - | 0.9953 | |
|
| 1.975 | 1580 | 0.0 | 0.0544 | - | 0.9953 | |
|
| 2.0 | 1600 | 0.0 | 0.0544 | - | 0.9953 | |
|
| -1 | -1 | - | - | 0.9960 | - | |
|
|
|
* 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 |
|
|
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## Citation |
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### BibTeX |
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|
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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", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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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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