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yahyaabd
/
allstats-v1-1

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:79621
loss:CosineSimilarityLoss
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 (12) hide show
  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +486 -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,
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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
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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:79621
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+ - loss:CosineSimilarityLoss
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+ base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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+ widget:
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+ - source_sentence: Data demografi Indonesia 2021 perempuan dan lakilaki
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+ sentences:
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+ - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Komoditi HS, Februari
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+ 2015
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+ - Statistik Potensi Desa Provinsi Jawa Barat 2014
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+ - Pengeluaran untuk Konsumsi Penduduk Indonesia, September 2017
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+ - source_sentence: Data analisis tematik kependudukan Indonesia migrasi dan ketenagakerjaan
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+ sentences:
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+ - Direktori Perusahaan Industri Penggilingan Padi Tahun 2012 Provinsi Bengkulu
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+ - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut HS, Juni 2023
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+ - Luas Panen dan Produksi Padi 2022
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+ - source_sentence: Daftar perusahaan penggilingan padi Kalimantan
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+ sentences:
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+ - Ringkasan Neraca Arus Dana, Triwulan II, 2011*), (Miliar Rupiah)
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+ - Klasifikasi Baku Komoditas Indonesia 2012 Buku 1
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+ - Statistik Penduduk Lanjut Usia Provinsi Nusa Tenggara Barat 2010-Hasil Sensus
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+ Penduduk 2010
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+ - source_sentence: Perdagangan luar negeri impor Januari 2010
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+ sentences:
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+ - Buletin Statistik Perdagangan Luar Negeri Impor Januari 2010
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+ - Statistik Tanaman Sayuran dan Buah-buahan Semusim Indonesia 2012
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+ - Klasifikasi Baku Komoditas Indonesia (KBKI) 2012 Buku 4
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+ - source_sentence: Biaya hidup kelompok perumahan Indonesia 2017
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+ sentences:
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+ - Indeks Harga Perdagangan Besar 2007
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+ - Statistik Upah 2013
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+ - Survei Biaya Hidup (SBH) 2018 Bulukumba, Watampone, Makassar, Pare-Pare, dan Palopo
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+ datasets:
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+ - yahyaabd/allstats-search-pairs-dataset
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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 semantic mpnet eval
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+ type: allstats-semantic-mpnet-eval
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.9832636747278353
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8514737414469329
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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 semantic mpnet test
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+ type: allstats-semantic-mpnet-test
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.9832774320084267
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8521298612131248
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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 [allstats-search-pairs-dataset](https://huggingface.co/datasets/yahyaabd/allstats-search-pairs-dataset) 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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+ - [allstats-search-pairs-dataset](https://huggingface.co/datasets/yahyaabd/allstats-search-pairs-dataset)
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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
115
+ pip install -U sentence-transformers
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+ ```
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+
118
+ Then you can load this model and run inference.
119
+ ```python
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+ from sentence_transformers import SentenceTransformer
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+
122
+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("yahyaabd/allstats-v1-1")
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+ # Run inference
125
+ sentences = [
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+ 'Biaya hidup kelompok perumahan Indonesia 2017',
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+ 'Statistik Upah 2013',
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+ 'Survei Biaya Hidup (SBH) 2018 Bulukumba, Watampone, Makassar, Pare-Pare, dan Palopo',
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+ ]
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+ embeddings = model.encode(sentences)
131
+ print(embeddings.shape)
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+ # [3, 768]
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+
134
+ # Get the similarity scores for the embeddings
135
+ 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-semantic-mpnet-eval` and `allstats-semantic-mpnet-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-mpnet-eval | allstats-semantic-mpnet-test |
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+ |:--------------------|:-----------------------------|:-----------------------------|
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+ | pearson_cosine | 0.9833 | 0.9833 |
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+ | **spearman_cosine** | **0.8515** | **0.8521** |
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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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+ #### allstats-search-pairs-dataset
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+
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+ * Dataset: [allstats-search-pairs-dataset](https://huggingface.co/datasets/yahyaabd/allstats-search-pairs-dataset) at [6712cb1](https://huggingface.co/datasets/yahyaabd/allstats-search-pairs-dataset/tree/6712cb14bbd89da6f87890ac082b09e0adb7a02e)
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+ * Size: 79,621 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 | float |
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+ | details | <ul><li>min: 5 tokens</li><li>mean: 10.78 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.73 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.44</li><li>max: 0.99</li></ul> |
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+ * Samples:
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+ | query | doc | label |
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+ |:--------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------|:------------------|
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+ | <code>Produksi jagung di Indonesia tahun 2009</code> | <code>Indeks Unit Value Ekspor Menurut Kode SITC Bulan Februari 2024</code> | <code>0.1</code> |
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+ | <code>Data produksi industri manufaktur 2021</code> | <code>Perkembangan Indeks Produksi Industri Manufaktur 2021</code> | <code>0.96</code> |
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+ | <code>direktori perusahaan industri penggilingan padi tahun 2012 provinsi sulawesi utara dan gorontalo</code> | <code>Neraca Pemerintahan Umum Indonesia 2007-2012</code> | <code>0.03</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
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+ {
213
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
214
+ }
215
+ ```
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+
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+ ### Evaluation Dataset
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+
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+ #### allstats-search-pairs-dataset
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+
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+ * Dataset: [allstats-search-pairs-dataset](https://huggingface.co/datasets/yahyaabd/allstats-search-pairs-dataset) at [6712cb1](https://huggingface.co/datasets/yahyaabd/allstats-search-pairs-dataset/tree/6712cb14bbd89da6f87890ac082b09e0adb7a02e)
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+ * Size: 9,952 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 | float |
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+ | details | <ul><li>min: 5 tokens</li><li>mean: 10.75 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.09 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 0.01</li><li>mean: 0.48</li><li>max: 0.99</li></ul> |
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+ * Samples:
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+ | query | doc | label |
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+ |:--------------------------------------------------------------------|:-----------------------------------------------------------------|:------------------|
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+ | <code>Daftar perusahaan industri pengolahan skala kecil 2006</code> | <code>Statistik Migrasi Nusa Tenggara Barat Hasil SP 2010</code> | <code>0.05</code> |
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+ | <code>Populasi Indonesia per provinsi 2000-2010</code> | <code>Indikator Ekonomi Desember 2023</code> | <code>0.08</code> |
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+ | <code>Data harga barang desa non-pangan tahun 2022</code> | <code>Statistik Kunjungan Tamu Asing 2004</code> | <code>0.1</code> |
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+ * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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+ ```json
237
+ {
238
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
239
+ }
240
+ ```
241
+
242
+ ### 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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+ - `num_train_epochs`: 12
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+ - `warmup_ratio`: 0.1
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+ - `fp16`: True
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+ - `dataloader_num_workers`: 4
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+ - `load_best_model_at_end`: True
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+ - `label_smoothing_factor`: 0.01
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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
+
259
+ - `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`: 12
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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`: 4
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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`: 0.01
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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
334
+ - `dataloader_persistent_workers`: False
335
+ - `skip_memory_metrics`: True
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+ - `use_legacy_prediction_loop`: False
337
+ - `push_to_hub`: False
338
+ - `resume_from_checkpoint`: None
339
+ - `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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+
375
+ </details>
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+
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+ ### Training Logs
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+ | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-mpnet-eval_spearman_cosine | allstats-semantic-mpnet-test_spearman_cosine |
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+ |:----------:|:---------:|:-------------:|:---------------:|:--------------------------------------------:|:--------------------------------------------:|
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+ | 0 | 0 | - | 0.0958 | 0.6404 | - |
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+ | 0.2008 | 250 | 0.0464 | 0.0246 | 0.7693 | - |
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+ | 0.4016 | 500 | 0.0218 | 0.0179 | 0.7720 | - |
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+ | 0.6024 | 750 | 0.0172 | 0.0153 | 0.7790 | - |
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+ | 0.8032 | 1000 | 0.0156 | 0.0136 | 0.7809 | - |
385
+ | 1.0040 | 1250 | 0.0137 | 0.0139 | 0.7769 | - |
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+ | **11.245** | **14000** | **0.0006** | **0.0055** | **0.8506** | **-** |
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+ | 12.0 | 14940 | - | - | - | 0.8521 |
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+
442
+ * The bold row denotes the saved checkpoint.
443
+
444
+ ### Framework Versions
445
+ - Python: 3.10.12
446
+ - Sentence Transformers: 3.3.1
447
+ - Transformers: 4.47.0
448
+ - PyTorch: 2.5.1+cu121
449
+ - Accelerate: 1.2.1
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+ - Datasets: 3.2.0
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+ - Tokenizers: 0.21.0
452
+
453
+ ## Citation
454
+
455
+ ### BibTeX
456
+
457
+ #### Sentence Transformers
458
+ ```bibtex
459
+ @inproceedings{reimers-2019-sentence-bert,
460
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
461
+ author = "Reimers, Nils and Gurevych, Iryna",
462
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
463
+ month = "11",
464
+ year = "2019",
465
+ publisher = "Association for Computational Linguistics",
466
+ url = "https://arxiv.org/abs/1908.10084",
467
+ }
468
+ ```
469
+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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