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yahyaabd
/
allstats-search-miniLM-v1

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:25551
loss:OnlineContrastiveLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
yahyaabd commited on
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Add new SentenceTransformer model

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Files changed (11) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +590 -0
  3. config.json +26 -0
  4. config_sentence_transformers.json +10 -0
  5. model.safetensors +3 -0
  6. modules.json +14 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +37 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +58 -0
  11. vocab.txt +0 -0
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 384,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
@@ -0,0 +1,590 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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:25551
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+ - loss:OnlineContrastiveLoss
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+ base_model: sentence-transformers/paraphrase-MiniLM-L12-v2
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+ widget:
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+ - source_sentence: Berapa gaji ratarata buruhkaryawan di Indonesia lihat dari umur
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+ dan lapangan pekerjaannya 2019
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+ sentences:
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+ - Rasio laju peningkatan konsumsi tanah dengan laju pertumbuhan penduduk
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+ - Rata-rata UpahGaji Bersih sebulan Buruh/Karyawan Pegawai Menurut Kelompok Umur
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+ dan lapangan pekerjaan utama, 2019
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+ - Ringkasan Neraca Arus Dana, Triwulan Pertama, 2005, (Miliar Rupiah)
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+ - source_sentence: Average monthly net wage/salary of employees by age group and type
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+ of work (Rupiah), 2018
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+ sentences:
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+ - Ringkasan Neraca Arus Dana, Triwulan III, 2014**), (Miliar Rupiah)
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+ - Nilai Produksi dan Biaya Produksi Rumah Tangga Usaha Peternakan Menurut Jenis
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+ Ternak, 2014
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+ - Rekapitulasi Laporan Posisi Keuangan Perusahaan Penyelenggara Program Asuransi
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+ Wajib dan BPJS Per 31 Desember (miliar rupiah) 2000-2021
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+ - source_sentence: jumlah pembangunan fasilitas sekolah baru
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+ sentences:
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+ - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi
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+ yang Ditamatkan dan Lapangan Pekerjaan Utama di 9 Sektor (rupiah), 2017
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+ - Posisi Kredit Perbankan1dalam Rupiah dan Valuta Asing Menurut Sektor Ekonomi (miliar
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+ rupiah), 2016-2018
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+ - Angka Kematian Bayi/AKB (Infant Mortality Rate/IMR) Hasil Long Form SP2020 Menurut
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+ Provinsi/Kabupaten/Kota, 2020
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+ - source_sentence: Data Pendapatan Rata-rata Orang Yang Berusaha Sendiri Per Provinsi,
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+ Berdasarkan Lapangan Pekerjaan Utama (2020)
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+ sentences:
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+ - Nilai Pendapatan Disposabel Menurut Golongan Rumah Tangga (miliar rupiah), 2000,
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+ 2005, dan 2008
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+ - IHK dan Rata-rata Upah per Bulan Buruh Pertambangan di Bawah Mandor (Supervisor),
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+ 1996-2014 (1996=100)
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+ - Ringkasan Neraca Arus Dana Tahun 2004 (Miliar Rupiah)
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+ - source_sentence: Bagaimana perkembangan koperasi di Indonesia, khususnya sekitar
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+ tayun 2000?
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+ sentences:
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+ - Rata-Rata Harian Aliran Sungai, Tinggi Aliran, dan Volume Air di Beberapa Sungai
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+ yang Daerah Pengalirannya Lebih dari 1.000 km2, 2000-2011
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+ - Penduduk Berumur 15 Tahun Ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut
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+ Golongan Umur dan Jumlah Jam Kerja Seluruhnya, 2008 - 2024
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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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+ 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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+ - pearson_cosine
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+ - spearman_cosine
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+ model-index:
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+ - name: SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L12-v2
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+ results:
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: allstats semantic mini v1 eval
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+ type: allstats-semantic-mini-v1-eval
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.8479971660039509
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.7745638757528484
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+ name: Spearman Cosine
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+ - task:
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+ type: semantic-similarity
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+ name: Semantic Similarity
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+ dataset:
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+ name: allstat search mini v1 test
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+ type: allstat-search-mini-v1-test
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.8538445733470035
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.7767623851780713
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+ name: Spearman Cosine
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+ ---
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+
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+ # SentenceTransformer based on sentence-transformers/paraphrase-MiniLM-L12-v2
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-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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+
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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:** [sentence-transformers/paraphrase-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L12-v2) <!-- at revision 3f21b01a41e265ecb43cef6afeef20b7e578b637 -->
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+ - **Maximum Sequence Length:** 256 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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+
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+ ### Model Sources
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+
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+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
109
+ - **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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+
112
+ ### Full Model Architecture
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+
114
+ ```
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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': 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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+
121
+ ## Usage
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+
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+ ### Direct Usage (Sentence Transformers)
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+
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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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+
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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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+
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+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("yahyaabd/allstats-search-miniLM-v1")
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+ # Run inference
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+ sentences = [
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+ 'Bagaimana perkembangan koperasi di Indonesia, khususnya sekitar tayun 2000?',
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+ 'IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), 1996-2014 (1996=100)',
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+ 'Rata-Rata Harian Aliran Sungai, Tinggi Aliran, dan Volume Air di Beberapa Sungai yang Daerah Pengalirannya Lebih dari 1.000 km2, 2000-2011',
142
+ ]
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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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+
147
+ # Get the similarity scores for the embeddings
148
+ similarities = model.similarity(embeddings, embeddings)
149
+ print(similarities.shape)
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+ # [3, 3]
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+ ```
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+
153
+ <!--
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+ ### Direct Usage (Transformers)
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+
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+ <details><summary>Click to see the direct usage in Transformers</summary>
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+
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+ </details>
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+ -->
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+
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+ <!--
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+ ### Downstream Usage (Sentence Transformers)
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+
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+ You can finetune this model on your own dataset.
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+
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+ <details><summary>Click to expand</summary>
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+
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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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+
177
+ ## Evaluation
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+
179
+ ### Metrics
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+
181
+ #### Semantic Similarity
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+
183
+ * Datasets: `allstats-semantic-mini-v1-eval` and `allstat-search-mini-v1-test`
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+ * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
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+
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+ | Metric | allstats-semantic-mini-v1-eval | allstat-search-mini-v1-test |
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+ |:--------------------|:-------------------------------|:----------------------------|
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+ | pearson_cosine | 0.848 | 0.8538 |
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+ | **spearman_cosine** | **0.7746** | **0.7768** |
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+
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+ <!--
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+ ## Bias, Risks and Limitations
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+
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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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+
197
+ <!--
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+ ### Recommendations
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+
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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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+
203
+ ## Training Details
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+
205
+ ### Training Dataset
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+
207
+ #### query-hard-pos-neg-doc-pairs-statictable
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+
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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 [25756d3](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/25756d36046bf92b56bce1b450fd080853688667)
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+ * Size: 25,551 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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+ |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
215
+ | type | string | string | int |
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+ | details | <ul><li>min: 9 tokens</li><li>mean: 28.64 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 36.67 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>0: ~65.80%</li><li>1: ~34.20%</li></ul> |
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+ * Samples:
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+ | query | doc | label |
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+ |:-----------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:---------------|
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+ | <code>Gaji nominal, indeks upah: nominal & riil pekerja manufaktur non-mandor (2012=100), 2013-2014</code> | <code>Ringkasan Neraca Arus Dana, Triwulan I, 2007, (Miliar Rupiah)</code> | <code>0</code> |
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+ | <code>gaji nominal, indeks upah: nominal & riil pekerja manufaktur non-mandor (2012=100), 2013-2014</code> | <code>Ringkasan Neraca Arus Dana, Triwulan I, 2007, (Miliar Rupiah)</code> | <code>0</code> |
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+ | <code>GAJI NOMINAL, INDEKS UPAH: NOMINAL & RIIL PEKERJA MANUFAKTUR NON-MANDOR (2012=100), 2013-2014</code> | <code>Ringkasan Neraca Arus Dana, Triwulan I, 2007, (Miliar Rupiah)</code> | <code>0</code> |
223
+ * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
224
+
225
+ ### Evaluation Dataset
226
+
227
+ #### query-hard-pos-neg-doc-pairs-statictable
228
+
229
+ * Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [25756d3](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/25756d36046bf92b56bce1b450fd080853688667)
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+ * Size: 5,463 evaluation samples
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+ * Columns: <code>query</code>, <code>doc</code>, and <code>label</code>
232
+ * Approximate statistics based on the first 1000 samples:
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+ | | query | doc | label |
234
+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
235
+ | type | string | string | int |
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+ | details | <ul><li>min: 10 tokens</li><li>mean: 29.3 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 37.1 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>0: ~73.20%</li><li>1: ~26.80%</li></ul> |
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+ * Samples:
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+ | query | doc | label |
239
+ |:------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|:---------------|
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+ | <code>Bagaimana penghasilan wirausahawan di Indonesia bervariasi per provinsi dan jenis pekerjaan utama di tahun 2016?</code> | <code>Realisasi Penerimaan dan Pengeluaran Pemerintah Desa (Juta Rupiah) di Perkotaan menurut Provinsi, 2000-2012</code> | <code>0</code> |
241
+ | <code>bagaimana penghasilan wirausahawan di indonesia bervariasi per provinsi dan jenis pekerjaan utama di tahun 2016?</code> | <code>Realisasi Penerimaan dan Pengeluaran Pemerintah Desa (Juta Rupiah) di Perkotaan menurut Provinsi, 2000-2012</code> | <code>0</code> |
242
+ | <code>BAGAIMANA PENGHASILAN WIRAUSAHAWAN DI INDONESIA BERVARIASI PER PROVINSI DAN JENIS PEKERJAAN UTAMA DI TAHUN 2016?</code> | <code>Realisasi Penerimaan dan Pengeluaran Pemerintah Desa (Juta Rupiah) di Perkotaan menurut Provinsi, 2000-2012</code> | <code>0</code> |
243
+ * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
244
+
245
+ ### Training Hyperparameters
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+ #### Non-Default Hyperparameters
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+
248
+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 32
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+ - `per_device_eval_batch_size`: 32
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+ - `num_train_epochs`: 4
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+ - `warmup_ratio`: 0.1
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+ - `fp16`: True
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+ - `load_best_model_at_end`: True
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+ - `eval_on_start`: True
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+
257
+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
259
+
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+ - `overwrite_output_dir`: False
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+ - `do_predict`: False
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+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: True
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+ - `per_device_train_batch_size`: 32
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+ - `per_device_eval_batch_size`: 32
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+ - `per_gpu_train_batch_size`: None
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+ - `per_gpu_eval_batch_size`: None
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+ - `gradient_accumulation_steps`: 1
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+ - `eval_accumulation_steps`: None
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+ - `torch_empty_cache_steps`: None
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+ - `learning_rate`: 5e-05
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+ - `weight_decay`: 0.0
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+ - `adam_beta1`: 0.9
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+ - `adam_beta2`: 0.999
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+ - `adam_epsilon`: 1e-08
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+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 4
278
+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
280
+ - `lr_scheduler_kwargs`: {}
281
+ - `warmup_ratio`: 0.1
282
+ - `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
287
+ - `save_safetensors`: True
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+ - `save_on_each_node`: False
289
+ - `save_only_model`: False
290
+ - `restore_callback_states_from_checkpoint`: False
291
+ - `no_cuda`: False
292
+ - `use_cpu`: False
293
+ - `use_mps_device`: False
294
+ - `seed`: 42
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+ - `data_seed`: None
296
+ - `jit_mode_eval`: False
297
+ - `use_ipex`: False
298
+ - `bf16`: False
299
+ - `fp16`: True
300
+ - `fp16_opt_level`: O1
301
+ - `half_precision_backend`: auto
302
+ - `bf16_full_eval`: False
303
+ - `fp16_full_eval`: False
304
+ - `tf32`: None
305
+ - `local_rank`: 0
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+ - `ddp_backend`: None
307
+ - `tpu_num_cores`: None
308
+ - `tpu_metrics_debug`: False
309
+ - `debug`: []
310
+ - `dataloader_drop_last`: False
311
+ - `dataloader_num_workers`: 0
312
+ - `dataloader_prefetch_factor`: None
313
+ - `past_index`: -1
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+ - `disable_tqdm`: False
315
+ - `remove_unused_columns`: True
316
+ - `label_names`: None
317
+ - `load_best_model_at_end`: True
318
+ - `ignore_data_skip`: False
319
+ - `fsdp`: []
320
+ - `fsdp_min_num_params`: 0
321
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
322
+ - `fsdp_transformer_layer_cls_to_wrap`: None
323
+ - `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
325
+ - `label_smoothing_factor`: 0.0
326
+ - `optim`: adamw_torch
327
+ - `optim_args`: None
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+ - `adafactor`: False
329
+ - `group_by_length`: False
330
+ - `length_column_name`: length
331
+ - `ddp_find_unused_parameters`: None
332
+ - `ddp_bucket_cap_mb`: None
333
+ - `ddp_broadcast_buffers`: False
334
+ - `dataloader_pin_memory`: True
335
+ - `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
339
+ - `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
363
+ - `include_tokens_per_second`: False
364
+ - `include_num_input_tokens_seen`: False
365
+ - `neftune_noise_alpha`: None
366
+ - `optim_target_modules`: None
367
+ - `batch_eval_metrics`: False
368
+ - `eval_on_start`: True
369
+ - `use_liger_kernel`: False
370
+ - `eval_use_gather_object`: False
371
+ - `average_tokens_across_devices`: False
372
+ - `prompts`: None
373
+ - `batch_sampler`: batch_sampler
374
+ - `multi_dataset_batch_sampler`: proportional
375
+
376
+ </details>
377
+
378
+ ### Training Logs
379
+ <details><summary>Click to expand</summary>
380
+
381
+ | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-mini-v1-eval_spearman_cosine | allstat-search-mini-v1-test_spearman_cosine |
382
+ |:----------:|:--------:|:-------------:|:---------------:|:----------------------------------------------:|:-------------------------------------------:|
383
+ | 0 | 0 | - | 1.0797 | 0.5314 | - |
384
+ | 0.0250 | 20 | 1.2823 | 0.9331 | 0.5510 | - |
385
+ | 0.0501 | 40 | 0.9562 | 0.6159 | 0.6492 | - |
386
+ | 0.0751 | 60 | 0.5872 | 0.4629 | 0.6913 | - |
387
+ | 0.1001 | 80 | 0.4101 | 0.3605 | 0.7221 | - |
388
+ | 0.1252 | 100 | 0.419 | 0.3919 | 0.7301 | - |
389
+ | 0.1502 | 120 | 0.1517 | 0.2565 | 0.7457 | - |
390
+ | 0.1752 | 140 | 0.2678 | 0.2503 | 0.7484 | - |
391
+ | 0.2003 | 160 | 0.225 | 0.2010 | 0.7546 | - |
392
+ | 0.2253 | 180 | 0.2846 | 0.3203 | 0.7420 | - |
393
+ | 0.2503 | 200 | 0.2086 | 0.1981 | 0.7589 | - |
394
+ | 0.2753 | 220 | 0.1255 | 0.1982 | 0.7610 | - |
395
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396
+ | 0.3254 | 260 | 0.1328 | 0.2218 | 0.7561 | - |
397
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398
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399
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400
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401
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402
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403
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404
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405
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407
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411
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412
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413
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414
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415
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423
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+ | -1 | -1 | - | - | - | 0.7768 |
544
+
545
+ * The bold row denotes the saved checkpoint.
546
+ </details>
547
+
548
+ ### Framework Versions
549
+ - Python: 3.10.12
550
+ - Sentence Transformers: 3.4.0
551
+ - Transformers: 4.48.1
552
+ - PyTorch: 2.5.1+cu124
553
+ - Accelerate: 1.3.0
554
+ - Datasets: 3.2.0
555
+ - Tokenizers: 0.21.0
556
+
557
+ ## Citation
558
+
559
+ ### BibTeX
560
+
561
+ #### Sentence Transformers
562
+ ```bibtex
563
+ @inproceedings{reimers-2019-sentence-bert,
564
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
565
+ author = "Reimers, Nils and Gurevych, Iryna",
566
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
567
+ month = "11",
568
+ year = "2019",
569
+ publisher = "Association for Computational Linguistics",
570
+ url = "https://arxiv.org/abs/1908.10084",
571
+ }
572
+ ```
573
+
574
+ <!--
575
+ ## Glossary
576
+
577
+ *Clearly define terms in order to be accessible across audiences.*
578
+ -->
579
+
580
+ <!--
581
+ ## Model Card Authors
582
+
583
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
584
+ -->
585
+
586
+ <!--
587
+ ## Model Card Contact
588
+
589
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
590
+ -->
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+ }
vocab.txt ADDED
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