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yahyaabd
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allstats-search-multilingual-base-v1

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:25580
loss:OnlineContrastiveLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
yahyaabd commited on
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1 Parent(s): 4449edb

Add new SentenceTransformer model

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Files changed (12) hide show
  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +487 -0
  4. config.json +29 -0
  5. config_sentence_transformers.json +10 -0
  6. model.safetensors +3 -0
  7. modules.json +14 -0
  8. sentence_bert_config.json +4 -0
  9. sentencepiece.bpe.model +3 -0
  10. special_tokens_map.json +51 -0
  11. tokenizer.json +3 -0
  12. tokenizer_config.json +62 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
5
+ "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 +1,487 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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-mpnet-base-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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+ - 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-multilingual-mpnet-base-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 search multilingual base v1 eval
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+ type: allstats-search-multilingual-base-v1-eval
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.8700002079644513
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8061513951134361
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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: allstats search multilingual base v1 test
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+ type: allstats-search-multilingual-base-v1-test
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.9023194252531408
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8092675333588865
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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-multilingual-mpnet-base-v2
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+
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+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-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 768-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-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 -->
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+ - **Maximum Sequence Length:** 128 tokens
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+ - **Output Dimensionality:** 768 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)
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+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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+
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+ ### Full Model Architecture
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+
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+ ```
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+ SentenceTransformer(
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+ (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
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+ (1): Pooling({'word_embedding_dimension': 768, '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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+
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+ ## 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-multilingual-base-v1")
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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, 768]
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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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+ <!--
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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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+
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+ ## Evaluation
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+
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+ ### Metrics
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+
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+ #### Semantic Similarity
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+
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+ * Datasets: `allstats-search-multilingual-base-v1-eval` and `allstats-search-multilingual-base-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-search-multilingual-base-v1-eval | allstats-search-multilingual-base-v1-test |
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+ |:--------------------|:------------------------------------------|:------------------------------------------|
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+ | pearson_cosine | 0.87 | 0.9023 |
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+ | **spearman_cosine** | **0.8062** | **0.8093** |
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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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+
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+ <!--
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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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+
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+ ## Training Details
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+
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+ ### Training Dataset
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+
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+ #### 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 [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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+
227
+ #### 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 [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> |
241
+ | <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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+
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+ - `eval_strategy`: steps
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+ - `per_device_train_batch_size`: 64
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+ - `per_device_eval_batch_size`: 64
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+ - `warmup_ratio`: 0.05
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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>
258
+
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+ - `overwrite_output_dir`: False
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+ - `do_predict`: False
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+ - `eval_strategy`: steps
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+ - `prediction_loss_only`: True
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+ - `per_device_train_batch_size`: 64
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+ - `per_device_eval_batch_size`: 64
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+ - `per_gpu_train_batch_size`: None
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+ - `per_gpu_eval_batch_size`: None
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+ - `gradient_accumulation_steps`: 1
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+ - `eval_accumulation_steps`: None
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+ - `torch_empty_cache_steps`: None
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+ - `learning_rate`: 5e-05
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+ - `weight_decay`: 0.0
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+ - `adam_beta1`: 0.9
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+ - `adam_beta2`: 0.999
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+ - `adam_epsilon`: 1e-08
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+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 3
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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`: 0.05
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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
317
+ - `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
362
+ - `include_tokens_per_second`: False
363
+ - `include_num_input_tokens_seen`: False
364
+ - `neftune_noise_alpha`: None
365
+ - `optim_target_modules`: None
366
+ - `batch_eval_metrics`: False
367
+ - `eval_on_start`: True
368
+ - `use_liger_kernel`: False
369
+ - `eval_use_gather_object`: False
370
+ - `average_tokens_across_devices`: False
371
+ - `prompts`: None
372
+ - `batch_sampler`: batch_sampler
373
+ - `multi_dataset_batch_sampler`: proportional
374
+
375
+ </details>
376
+
377
+ ### Training Logs
378
+ | Epoch | Step | Training Loss | Validation Loss | allstats-search-multilingual-base-v1-eval_spearman_cosine | allstats-search-multilingual-base-v1-test_spearman_cosine |
379
+ |:-------:|:-------:|:-------------:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|
380
+ | 0 | 0 | - | 1.3012 | 0.7447 | - |
381
+ | 0.05 | 20 | 0.9548 | 0.3980 | 0.7961 | - |
382
+ | 0.1 | 40 | 0.3959 | 0.3512 | 0.7993 | - |
383
+ | 0.15 | 60 | 0.1949 | 0.3102 | 0.8016 | - |
384
+ | 0.2 | 80 | 0.2126 | 0.4306 | 0.7967 | - |
385
+ | 0.25 | 100 | 0.2228 | 0.2865 | 0.8026 | - |
386
+ | 0.3 | 120 | 0.1306 | 0.2476 | 0.8035 | - |
387
+ | 0.35 | 140 | 0.172 | 0.2592 | 0.8014 | - |
388
+ | 0.4 | 160 | 0.1619 | 0.2495 | 0.8037 | - |
389
+ | 0.45 | 180 | 0.1416 | 0.1890 | 0.8046 | - |
390
+ | 0.5 | 200 | 0.1041 | 0.1717 | 0.8059 | - |
391
+ | 0.55 | 220 | 0.2145 | 0.2165 | 0.8049 | - |
392
+ | 0.6 | 240 | 0.0459 | 0.2176 | 0.8036 | - |
393
+ | 0.65 | 260 | 0.0627 | 0.2670 | 0.8023 | - |
394
+ | 0.7 | 280 | 0.1132 | 0.2309 | 0.8041 | - |
395
+ | 0.75 | 300 | 0.1048 | 0.2623 | 0.8028 | - |
396
+ | 0.8 | 320 | 0.0524 | 0.2328 | 0.8031 | - |
397
+ | 0.85 | 340 | 0.034 | 0.2580 | 0.8024 | - |
398
+ | 0.9 | 360 | 0.0664 | 0.2309 | 0.8034 | - |
399
+ | 0.95 | 380 | 0.0623 | 0.1746 | 0.8053 | - |
400
+ | 1.0 | 400 | 0.0402 | 0.2126 | 0.8041 | - |
401
+ | 1.05 | 420 | 0.0459 | 0.1660 | 0.8062 | - |
402
+ | 1.1 | 440 | 0.0739 | 0.1487 | 0.8068 | - |
403
+ | 1.15 | 460 | 0.0191 | 0.1595 | 0.8066 | - |
404
+ | 1.2 | 480 | 0.0073 | 0.1509 | 0.8066 | - |
405
+ | 1.25 | 500 | 0.0265 | 0.1779 | 0.8062 | - |
406
+ | 1.3 | 520 | 0.0325 | 0.2646 | 0.8032 | - |
407
+ | 1.35 | 540 | 0.0536 | 0.2818 | 0.8030 | - |
408
+ | 1.4 | 560 | 0.0076 | 0.1768 | 0.8057 | - |
409
+ | 1.45 | 580 | 0.011 | 0.1866 | 0.8054 | - |
410
+ | 1.5 | 600 | 0.0181 | 0.1726 | 0.8057 | - |
411
+ | 1.55 | 620 | 0.032 | 0.1881 | 0.8052 | - |
412
+ | 1.6 | 640 | 0.0459 | 0.1482 | 0.8066 | - |
413
+ | 1.65 | 660 | 0.041 | 0.1571 | 0.8065 | - |
414
+ | **1.7** | **680** | **0.0228** | **0.1298** | **0.807** | **-** |
415
+ | 1.75 | 700 | 0.0275 | 0.1571 | 0.8067 | - |
416
+ | 1.8 | 720 | 0.0 | 0.1624 | 0.8066 | - |
417
+ | 1.85 | 740 | 0.0218 | 0.1537 | 0.8068 | - |
418
+ | 1.9 | 760 | 0.0241 | 0.1699 | 0.8062 | - |
419
+ | 1.95 | 780 | 0.0065 | 0.1841 | 0.8059 | - |
420
+ | 2.0 | 800 | 0.0073 | 0.1805 | 0.8061 | - |
421
+ | 2.05 | 820 | 0.0 | 0.1703 | 0.8064 | - |
422
+ | 2.1 | 840 | 0.0 | 0.1702 | 0.8064 | - |
423
+ | 2.15 | 860 | 0.0 | 0.1717 | 0.8064 | - |
424
+ | 2.2 | 880 | 0.0 | 0.1717 | 0.8064 | - |
425
+ | 2.25 | 900 | 0.0 | 0.1717 | 0.8064 | - |
426
+ | 2.3 | 920 | 0.0097 | 0.1875 | 0.8059 | - |
427
+ | 2.35 | 940 | 0.0148 | 0.1868 | 0.8060 | - |
428
+ | 2.4 | 960 | 0.0067 | 0.2205 | 0.8051 | - |
429
+ | 2.45 | 980 | 0.0 | 0.2295 | 0.8049 | - |
430
+ | 2.5 | 1000 | 0.0154 | 0.2238 | 0.8052 | - |
431
+ | 2.55 | 1020 | 0.0063 | 0.2125 | 0.8055 | - |
432
+ | 2.6 | 1040 | 0.0 | 0.2183 | 0.8053 | - |
433
+ | 2.65 | 1060 | 0.0 | 0.2188 | 0.8053 | - |
434
+ | 2.7 | 1080 | 0.0068 | 0.2082 | 0.8056 | - |
435
+ | 2.75 | 1100 | 0.0384 | 0.1770 | 0.8060 | - |
436
+ | 2.8 | 1120 | 0.0 | 0.1645 | 0.8061 | - |
437
+ | 2.85 | 1140 | 0.0105 | 0.1613 | 0.8061 | - |
438
+ | 2.9 | 1160 | 0.0 | 0.1601 | 0.8061 | - |
439
+ | 2.95 | 1180 | 0.0 | 0.1601 | 0.8062 | - |
440
+ | 3.0 | 1200 | 0.0 | 0.1601 | 0.8062 | - |
441
+ | -1 | -1 | - | - | - | 0.8093 |
442
+
443
+ * The bold row denotes the saved checkpoint.
444
+
445
+ ### Framework Versions
446
+ - Python: 3.10.12
447
+ - Sentence Transformers: 3.4.0
448
+ - Transformers: 4.48.1
449
+ - PyTorch: 2.5.1+cu124
450
+ - Accelerate: 1.3.0
451
+ - Datasets: 3.2.0
452
+ - Tokenizers: 0.21.0
453
+
454
+ ## Citation
455
+
456
+ ### BibTeX
457
+
458
+ #### Sentence Transformers
459
+ ```bibtex
460
+ @inproceedings{reimers-2019-sentence-bert,
461
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
462
+ author = "Reimers, Nils and Gurevych, Iryna",
463
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
464
+ month = "11",
465
+ year = "2019",
466
+ publisher = "Association for Computational Linguistics",
467
+ url = "https://arxiv.org/abs/1908.10084",
468
+ }
469
+ ```
470
+
471
+ <!--
472
+ ## Glossary
473
+
474
+ *Clearly define terms in order to be accessible across audiences.*
475
+ -->
476
+
477
+ <!--
478
+ ## Model Card Authors
479
+
480
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
481
+ -->
482
+
483
+ <!--
484
+ ## Model Card Contact
485
+
486
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
487
+ -->
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