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--- |
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base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
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datasets: |
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- yahyaabd/allstats-semantic-dataset-v4 |
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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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pipeline_tag: sentence-similarity |
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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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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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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 v2 eval |
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type: allstats-semantic-mpnet-v2-eval |
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metrics: |
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- type: pearson_cosine |
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value: 0.9437560461787071 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.7866108512073439 |
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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 v2 test |
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type: allstats-semantic-mpnet-v2-test |
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metrics: |
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- type: pearson_cosine |
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value: 0.9433638771860691 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: 0.7869770777792755 |
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name: Spearman Cosine |
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--- |
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# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
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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-semantic-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4) 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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## Model Details |
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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-semantic-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4) |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 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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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("yahyaabd/allstats-semantic-mpnet-v2") |
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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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# 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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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Datasets: `allstats-semantic-mpnet-v2-eval` and `allstats-semantic-mpnet-v2-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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| Metric | allstats-semantic-mpnet-v2-eval | allstats-semantic-mpnet-v2-test | |
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|:--------------------|:--------------------------------|:--------------------------------| |
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| pearson_cosine | 0.9438 | 0.9434 | |
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| **spearman_cosine** | **0.7866** | **0.787** | |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Dataset |
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#### allstats-semantic-dataset-v4 |
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* Dataset: [allstats-semantic-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4) at [0e15dee](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4/tree/0e15dee2f2696b45949c1e2256b7cb0b8c87ad8d) |
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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" |
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} |
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``` |
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### Evaluation Dataset |
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#### allstats-semantic-dataset-v4 |
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* Dataset: [allstats-semantic-dataset-v4](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4) at [0e15dee](https://huggingface.co/datasets/yahyaabd/allstats-semantic-dataset-v4/tree/0e15dee2f2696b45949c1e2256b7cb0b8c87ad8d) |
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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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{ |
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"loss_fct": "torch.nn.modules.loss.MSELoss" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `num_train_epochs`: 0.5 |
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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`: 0.05 |
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- `eval_on_start`: True |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 32 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 0.5 |
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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`: 0.05 |
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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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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | Validation Loss | allstats-semantic-mpnet-v2-eval_spearman_cosine | allstats-semantic-mpnet-v2-test_spearman_cosine | |
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|:----------:|:--------:|:-------------:|:---------------:|:-----------------------------------------------:|:-----------------------------------------------:| |
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| 0 | 0 | - | 0.1031 | 0.6244 | - | |
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| 0.1090 | 250 | 0.0442 | 0.0321 | 0.7393 | - | |
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| 0.2180 | 500 | 0.0295 | 0.0248 | 0.7641 | - | |
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| 0.3269 | 750 | 0.0259 | 0.0224 | 0.7733 | - | |
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| **0.4359** | **1000** | **0.0208** | **0.0199** | **0.7866** | **-** | |
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| 0.5 | 1147 | - | - | - | 0.7870 | |
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* The bold row denotes the saved checkpoint. |
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### Framework Versions |
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- Python: 3.10.12 |
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- Sentence Transformers: 3.3.1 |
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- Transformers: 4.48.0 |
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- PyTorch: 2.4.1+cu121 |
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- Accelerate: 0.34.2 |
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- Datasets: 3.2.0 |
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- Tokenizers: 0.21.0 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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