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yahyaabd
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allstats-semantic-mpnet-v1

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:73392
loss:CosineSimilarityLoss
Eval Results
text-embeddings-inference
Model card Files Community
yahyaabd commited on
Commit
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Add new SentenceTransformer model

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Files changed (11) hide show
  1. .gitattributes +1 -0
  2. 1_Pooling/config.json +10 -0
  3. README.md +541 -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. special_tokens_map.json +51 -0
  10. tokenizer.json +3 -0
  11. tokenizer_config.json +62 -0
.gitattributes CHANGED
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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
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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:73392
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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: Berapa persen kenaikan Indeks Harga Perdagangan Besar (IHPB) Umum
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+ Nasional pada bulan April 2021?
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+ sentences:
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+ - Statistik Kriminal 2023
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+ - Ekonomi Indonesia Triwulan I-2021 turun 0,74 persen (y-on-y)
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+ - Survei Biaya Hidup (SBH) 2018 Ambon dan Tual
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+ - source_sentence: Usaha pertanian sampingan di Indonesia tahun 2022
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+ sentences:
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+ - Analisis Hasil Survei Dampak Covid-19 Terhadap Pelaku Usaha
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+ - Direktori Usaha Pertanian Lainnya 2022
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+ - EksporImpor September 2018
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+ - source_sentence: Pertumbuhan industri Indonesia 2006-2009
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+ sentences:
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+ - Pertumbuhan Produksi IBS Triwulan III 2019 Naik 4,35 Persen
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+ - Indikator Ekonomi April 2000
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+ - Perkembangan Indeks Produksi Industri Besar dan Sedang 2006 - 2009
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+ - source_sentence: 'Sensus ekonomi Kalbar 2016: data usaha'
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+ sentences:
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+ - Pertumbuhan ekonomi Indonesia tahun 2022
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+ - Buletin Statistik Perdagangan Luar Negeri Impor November 2017
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+ - Data jumlah wisatawan mancanegara 2019
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+ - source_sentence: Direktori perusahaan pengelola hutan 2015
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+ sentences:
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+ - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan
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+ Negara, April 2017
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+ - Direktori Perusahaan Kehutanan 2015
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+ - Indeks Pembangunan Manusia (IPM) Indonesia tahun 2024 mencapai 75,02, meningkat
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+ 0,63 poin atau 0,85 persen dibandingkan tahun sebelumnya yang sebesar 74,39.
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+ datasets:
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+ - yahyaabd/bps-semantic-pairs-synthetic-dataset-v1
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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 v1 eval
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+ type: allstats-semantic-mpnet-v1-eval
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.9721680353379998
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8769707416598509
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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 semantic mpnet v1 test
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+ type: allstat-semantic-mpnet-v1-test
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+ metrics:
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+ - type: pearson_cosine
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+ value: 0.9714701009323166
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.8696530606326947
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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 [bps-semantic-pairs-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-synthetic-dataset-v1) 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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+ - [bps-semantic-pairs-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-synthetic-dataset-v1)
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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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+
115
+ ```bash
116
+ pip install -U sentence-transformers
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+ ```
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+
119
+ Then you can load this model and run inference.
120
+ ```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-semantic-mpnet-v1")
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+ # Run inference
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+ sentences = [
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+ 'Direktori perusahaan pengelola hutan 2015',
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+ 'Direktori Perusahaan Kehutanan 2015',
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+ 'Indeks Pembangunan Manusia (IPM) Indonesia tahun 2024 mencapai 75,02, meningkat 0,63 poin atau 0,85 persen dibandingkan tahun sebelumnya yang sebesar 74,39.',
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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-semantic-mpnet-v1-eval` and `allstat-semantic-mpnet-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-mpnet-v1-eval | allstat-semantic-mpnet-v1-test |
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+ |:--------------------|:--------------------------------|:-------------------------------|
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+ | pearson_cosine | 0.9722 | 0.9715 |
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+ | **spearman_cosine** | **0.877** | **0.8697** |
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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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+ #### bps-semantic-pairs-synthetic-dataset-v1
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+
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+ * Dataset: [bps-semantic-pairs-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-synthetic-dataset-v1) at [6656af9](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-synthetic-dataset-v1/tree/6656af9b517b88dc1445ccd85e5fa78bd07b08d1)
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+ * Size: 73,392 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: 11.28 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.71 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | query | doc | label |
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+ |:-----------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
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+ | <code>Data bisnis Kalbar sensus 2016</code> | <code>Indikator Ekonomi Oktober 2012</code> | <code>0.1</code> |
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+ | <code>Informasi tentang pola pengeluaran masyarakat Bengkulu berdasarkan kelompok pendapatan?</code> | <code>Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Bengkulu, 2018-2023</code> | <code>0.88</code> |
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+ | <code>Laopran keuagnan lmebaga non proft 20112-013</code> | <code>Neraca Lembaga Non Profit yang Melayani Rumah Tangga 2011-2013</code> | <code>0.93</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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+ {
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+ "loss_fct": "torch.nn.modules.loss.MSELoss"
215
+ }
216
+ ```
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+
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+ ### Evaluation Dataset
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+
220
+ #### bps-semantic-pairs-synthetic-dataset-v1
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+
222
+ * Dataset: [bps-semantic-pairs-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-synthetic-dataset-v1) at [6656af9](https://huggingface.co/datasets/yahyaabd/bps-semantic-pairs-synthetic-dataset-v1/tree/6656af9b517b88dc1445ccd85e5fa78bd07b08d1)
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+ * Size: 15,726 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: 4 tokens</li><li>mean: 11.52 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.38 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> |
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+ * Samples:
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+ | query | doc | label |
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+ |:-----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------|:------------------|
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+ | <code>Data transportasi bulan Februari 2021</code> | <code>Tenaga Kerja Februari 2023</code> | <code>0.08</code> |
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+ | <code>Sebear berspa prrsen eknaikan Inseks Hraga Predagangan eBsar (IHB) Umym Nasiona di aMret 202?</code> | <code>Maret 2020, Indeks Harga Perdagangan Besar (IHPB) Umum Nasional naik 0,10 persen</code> | <code>1.0</code> |
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+ | <code>Data ekspor dan moda transportasi tahun 2018-2019</code> | <code>Indikator Pasar Tenaga Kerja Indonesia Agustus 2012</code> | <code>0.08</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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+ {
239
+ "loss_fct": "torch.nn.modules.loss.MSELoss"
240
+ }
241
+ ```
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+
243
+ ### 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`: 24
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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>
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`: 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`: 24
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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
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+ - `dataloader_persistent_workers`: False
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+ - `skip_memory_metrics`: True
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+ - `use_legacy_prediction_loop`: False
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+ - `push_to_hub`: False
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+ - `resume_from_checkpoint`: None
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+ - `hub_model_id`: None
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+ - `hub_strategy`: every_save
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+ - `hub_private_repo`: None
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+ - `hub_always_push`: False
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+ - `gradient_checkpointing`: False
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+ - `gradient_checkpointing_kwargs`: None
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+ - `include_inputs_for_metrics`: False
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+ - `include_for_metrics`: []
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+ - `eval_do_concat_batches`: True
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+ - `fp16_backend`: auto
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+ - `push_to_hub_model_id`: None
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+ - `push_to_hub_organization`: None
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+ - `mp_parameters`:
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+ - `auto_find_batch_size`: False
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+ - `full_determinism`: False
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+ - `torchdynamo`: None
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+ - `ray_scope`: last
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+ - `ddp_timeout`: 1800
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+ - `torch_compile`: False
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+ - `torch_compile_backend`: None
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+ - `torch_compile_mode`: None
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+ - `dispatch_batches`: None
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+ - `split_batches`: None
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+ - `include_tokens_per_second`: False
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+ - `include_num_input_tokens_seen`: False
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+ - `neftune_noise_alpha`: None
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+ - `optim_target_modules`: None
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+ - `batch_eval_metrics`: False
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+ - `eval_on_start`: True
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+ - `use_liger_kernel`: False
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+ - `eval_use_gather_object`: False
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+ - `average_tokens_across_devices`: False
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+ - `prompts`: None
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+ - `batch_sampler`: batch_sampler
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+ - `multi_dataset_batch_sampler`: proportional
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+
376
+ </details>
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+
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+ ### Training Logs
379
+ <details><summary>Click to expand</summary>
380
+
381
+ | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-mpnet-v1-eval_spearman_cosine | allstat-semantic-mpnet-v1-test_spearman_cosine |
382
+ |:-----------:|:---------:|:-------------:|:---------------:|:-----------------------------------------------:|:----------------------------------------------:|
383
+ | 0 | 0 | - | 0.1031 | 0.6244 | - |
384
+ | 0.2180 | 250 | 0.064 | 0.0413 | 0.6958 | - |
385
+ | 0.4359 | 500 | 0.0381 | 0.0305 | 0.7301 | - |
386
+ | 0.6539 | 750 | 0.0284 | 0.0243 | 0.7651 | - |
387
+ | 0.8718 | 1000 | 0.025 | 0.0213 | 0.7656 | - |
388
+ | 1.0898 | 1250 | 0.0207 | 0.0201 | 0.7822 | - |
389
+ | 1.3078 | 1500 | 0.0188 | 0.0194 | 0.7805 | - |
390
+ | 1.5257 | 1750 | 0.0182 | 0.0177 | 0.7918 | - |
391
+ | 1.7437 | 2000 | 0.0177 | 0.0168 | 0.8098 | - |
392
+ | 1.9616 | 2250 | 0.0173 | 0.0173 | 0.7979 | - |
393
+ | 2.1796 | 2500 | 0.0151 | 0.0174 | 0.8010 | - |
394
+ | 2.3976 | 2750 | 0.014 | 0.0163 | 0.8005 | - |
395
+ | 2.6155 | 3000 | 0.0142 | 0.0159 | 0.8027 | - |
396
+ | 2.8335 | 3250 | 0.0137 | 0.0154 | 0.8074 | - |
397
+ | 3.0514 | 3500 | 0.013 | 0.0146 | 0.8173 | - |
398
+ | 3.2694 | 3750 | 0.0099 | 0.0138 | 0.8179 | - |
399
+ | 3.4874 | 4000 | 0.0105 | 0.0135 | 0.8138 | - |
400
+ | 3.7053 | 4250 | 0.0109 | 0.0145 | 0.8138 | - |
401
+ | 3.9233 | 4500 | 0.011 | 0.0145 | 0.8244 | - |
402
+ | 4.1412 | 4750 | 0.0086 | 0.0132 | 0.8327 | - |
403
+ | 4.3592 | 5000 | 0.0077 | 0.0129 | 0.8307 | - |
404
+ | 4.5772 | 5250 | 0.0081 | 0.0124 | 0.8380 | - |
405
+ | 4.7951 | 5500 | 0.0087 | 0.0128 | 0.8358 | - |
406
+ | 5.0131 | 5750 | 0.0076 | 0.0135 | 0.8280 | - |
407
+ | 5.2310 | 6000 | 0.0061 | 0.0122 | 0.8399 | - |
408
+ | 5.4490 | 6250 | 0.0062 | 0.0119 | 0.8344 | - |
409
+ | 5.6670 | 6500 | 0.007 | 0.0113 | 0.8432 | - |
410
+ | 5.8849 | 6750 | 0.0069 | 0.0117 | 0.8353 | - |
411
+ | 6.1029 | 7000 | 0.0056 | 0.0117 | 0.8333 | - |
412
+ | 6.3208 | 7250 | 0.0047 | 0.0114 | 0.8438 | - |
413
+ | 6.5388 | 7500 | 0.0059 | 0.0114 | 0.8429 | - |
414
+ | 6.7568 | 7750 | 0.0054 | 0.0113 | 0.8452 | - |
415
+ | 6.9747 | 8000 | 0.0059 | 0.0118 | 0.8477 | - |
416
+ | 7.1927 | 8250 | 0.0045 | 0.0109 | 0.8474 | - |
417
+ | 7.4106 | 8500 | 0.0042 | 0.0111 | 0.8532 | - |
418
+ | 7.6286 | 8750 | 0.0045 | 0.0114 | 0.8385 | - |
419
+ | 7.8466 | 9000 | 0.005 | 0.0111 | 0.8502 | - |
420
+ | 8.0645 | 9250 | 0.0045 | 0.0111 | 0.8496 | - |
421
+ | 8.2825 | 9500 | 0.0035 | 0.0109 | 0.8490 | - |
422
+ | 8.5004 | 9750 | 0.0038 | 0.0112 | 0.8519 | - |
423
+ | 8.7184 | 10000 | 0.0038 | 0.0112 | 0.8463 | - |
424
+ | 8.9364 | 10250 | 0.0039 | 0.0109 | 0.8556 | - |
425
+ | 9.1543 | 10500 | 0.0035 | 0.0110 | 0.8534 | - |
426
+ | 9.3723 | 10750 | 0.003 | 0.0111 | 0.8525 | - |
427
+ | 9.5902 | 11000 | 0.0039 | 0.0108 | 0.8593 | - |
428
+ | 9.8082 | 11250 | 0.0038 | 0.0112 | 0.8537 | - |
429
+ | 10.0262 | 11500 | 0.0033 | 0.0108 | 0.8553 | - |
430
+ | 10.2441 | 11750 | 0.0023 | 0.0104 | 0.8601 | - |
431
+ | 10.4621 | 12000 | 0.0025 | 0.0104 | 0.8571 | - |
432
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433
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434
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435
+ | 11.3339 | 13000 | 0.002 | 0.0104 | 0.8614 | - |
436
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437
+ | 11.7698 | 13500 | 0.0025 | 0.0106 | 0.8580 | - |
438
+ | 11.9878 | 13750 | 0.0023 | 0.0108 | 0.8613 | - |
439
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440
+ | 12.4237 | 14250 | 0.0017 | 0.0104 | 0.8641 | - |
441
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442
+ | 12.8596 | 14750 | 0.002 | 0.0104 | 0.8649 | - |
443
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444
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448
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449
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450
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458
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459
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460
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461
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462
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465
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467
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470
+ | **18.9625** | **21750** | **0.0009** | **0.0098** | **0.8738** | **-** |
471
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472
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473
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484
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493
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494
+ | 24.0 | 27528 | - | - | - | 0.8697 |
495
+
496
+ * The bold row denotes the saved checkpoint.
497
+ </details>
498
+
499
+ ### Framework Versions
500
+ - Python: 3.10.12
501
+ - Sentence Transformers: 3.3.1
502
+ - Transformers: 4.47.1
503
+ - PyTorch: 2.5.1+cu124
504
+ - Accelerate: 1.2.1
505
+ - Datasets: 3.2.0
506
+ - Tokenizers: 0.21.0
507
+
508
+ ## Citation
509
+
510
+ ### BibTeX
511
+
512
+ #### Sentence Transformers
513
+ ```bibtex
514
+ @inproceedings{reimers-2019-sentence-bert,
515
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
516
+ author = "Reimers, Nils and Gurevych, Iryna",
517
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
518
+ month = "11",
519
+ year = "2019",
520
+ publisher = "Association for Computational Linguistics",
521
+ url = "https://arxiv.org/abs/1908.10084",
522
+ }
523
+ ```
524
+
525
+ <!--
526
+ ## Glossary
527
+
528
+ *Clearly define terms in order to be accessible across audiences.*
529
+ -->
530
+
531
+ <!--
532
+ ## Model Card Authors
533
+
534
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
535
+ -->
536
+
537
+ <!--
538
+ ## Model Card Contact
539
+
540
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
541
+ -->
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