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--- |
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language: |
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- id |
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library_name: sentence-transformers |
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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:10000 |
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- loss:SoftmaxLoss |
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base_model: indobenchmark/indobert-base-p2 |
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datasets: |
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- afaji/indonli |
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metrics: |
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- pearson_cosine |
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- spearman_cosine |
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- pearson_manhattan |
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- spearman_manhattan |
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- pearson_euclidean |
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- spearman_euclidean |
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- pearson_dot |
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- spearman_dot |
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- pearson_max |
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- spearman_max |
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widget: |
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- source_sentence: Dengan meniupnya, perawat bisa segera mengerti bahwa ia dipanggil |
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dan akan segera datang menolong. |
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sentences: |
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- 38% pemilih tidak mendukung meninggalkan Uni Eropa. |
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- Perawat mengerti bahwa ia dipanggil dan akan segera datang menolong. |
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- Dari fakta-fakta tersebut dapat diindikasikan pembakaran gereja dilakukan secara |
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sengaja. |
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- source_sentence: Kebudayaan jawa lainnya adalah Sintren, Sintren adalan kesenian |
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tradisional masyarakat Jawa, khususnya Pekalongan. |
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sentences: |
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- Sintren merupakan kesenian tradisional masyarakat Jawa yang ada sejak zaman kerajaan. |
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- Klinik ini melarang pasiennya menghisap ganja. |
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- Perubahan dunia saat itu dipengaruhi oleh Krisis Suez. |
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- source_sentence: Saat ini, sudah empat wanita yang mengaku dilecehkan. Yang terakhir |
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ialah aktris Rose McGowan, dengan tuntutan pemerkosaan. |
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sentences: |
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- Di Maroko Tenggara tidak pernah ada fosil vertebrata. |
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- Tidak ada yang dilecehkan. |
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- Ganja tidak boleh diberikan kepada pasien penyakit apapun. |
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- source_sentence: Peperangan di tanah berubah dari lini depan statis Perang Dunia |
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I menjadi peningkatan mobilitas dan persenjataan gabungan. |
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sentences: |
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- Peperangan di tanah awalnya berbentuk lini depan statis Perang Dunia I. |
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- Ia berdarah keturunan India. |
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- Kesultanan Yogyakarta berasal dari Kerajaan Mataram. |
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- source_sentence: Bahan dasar Dalgona Coffee hanya tiga jenis yaitu bubuk kopi, gula, |
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dan air. Banyak resep beredar dengan komposisi dua sendok bubuk kopi, dua sendok |
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gula, dan dua sendok air panas. |
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sentences: |
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- Semua orang di dunia menyukai air putih. |
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- Jutting berada di Pengadilan Tinggi Hongkong 5 tahun kemudian. |
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- Resep komposisi Dalgona Coffee adalah 2 sendok bubuk kopi. |
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pipeline_tag: sentence-similarity |
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model-index: |
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- name: SentenceTransformer based on indobenchmark/indobert-base-p2 |
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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: sts dev |
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type: sts-dev |
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metrics: |
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- type: pearson_cosine |
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value: -0.4766226820019628 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: -0.4665046363205431 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: -0.46278474137062864 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: -0.46103038796182516 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: -0.4732431317820645 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: -0.4673139200425683 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: -0.4679129419420587 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: -0.4577457216480116 |
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name: Spearman Dot |
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- type: pearson_max |
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value: -0.46278474137062864 |
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name: Pearson Max |
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- type: spearman_max |
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value: -0.4577457216480116 |
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name: Spearman Max |
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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: sts test |
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type: sts-test |
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metrics: |
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- type: pearson_cosine |
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value: -0.20358655624514646 |
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name: Pearson Cosine |
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- type: spearman_cosine |
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value: -0.20098073423584242 |
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name: Spearman Cosine |
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- type: pearson_manhattan |
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value: -0.16857445418120778 |
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name: Pearson Manhattan |
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- type: spearman_manhattan |
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value: -0.18417229002858432 |
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name: Spearman Manhattan |
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- type: pearson_euclidean |
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value: -0.17954736289799147 |
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name: Pearson Euclidean |
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- type: spearman_euclidean |
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value: -0.1907831094006202 |
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name: Spearman Euclidean |
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- type: pearson_dot |
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value: -0.2158654981443921 |
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name: Pearson Dot |
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- type: spearman_dot |
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value: -0.2141585054513143 |
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name: Spearman Dot |
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- type: pearson_max |
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value: -0.16857445418120778 |
|
name: Pearson Max |
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- type: spearman_max |
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value: -0.18417229002858432 |
|
name: Spearman Max |
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--- |
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|
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# SentenceTransformer based on indobenchmark/indobert-base-p2 |
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|
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p2](https://huggingface.co./indobenchmark/indobert-base-p2) on the [afaji/indonli](https://huggingface.co./datasets/afaji/indonli) 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:** [indobenchmark/indobert-base-p2](https://huggingface.co./indobenchmark/indobert-base-p2) <!-- at revision 94b4e0a82081fa57f227fcc2024d1ea89b57ac1f --> |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 tokens |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** |
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- [afaji/indonli](https://huggingface.co./datasets/afaji/indonli) |
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- **Language:** id |
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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': 512, 'do_lower_case': False}) with Transformer model: BertModel |
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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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### Direct Usage (Sentence Transformers) |
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|
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First install the Sentence Transformers library: |
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|
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```bash |
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pip install -U sentence-transformers |
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``` |
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|
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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|
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# Download from the 🤗 Hub |
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model = SentenceTransformer("cassador/indobert-base-p2-nli-v1") |
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# Run inference |
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sentences = [ |
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'Bahan dasar Dalgona Coffee hanya tiga jenis yaitu bubuk kopi, gula, dan air. Banyak resep beredar dengan komposisi dua sendok bubuk kopi, dua sendok gula, dan dua sendok air panas.', |
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'Resep komposisi Dalgona Coffee adalah 2 sendok bubuk kopi.', |
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'Jutting berada di Pengadilan Tinggi Hongkong 5 tahun kemudian.', |
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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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<details><summary>Click to see the direct usage in Transformers</summary> |
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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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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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--> |
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|
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<!-- |
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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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--> |
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|
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## Evaluation |
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### Metrics |
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#### Semantic Similarity |
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* Dataset: `sts-dev` |
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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 | Value | |
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|:--------------------|:------------| |
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| pearson_cosine | -0.4766 | |
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| **spearman_cosine** | **-0.4665** | |
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| pearson_manhattan | -0.4628 | |
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| spearman_manhattan | -0.461 | |
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| pearson_euclidean | -0.4732 | |
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| spearman_euclidean | -0.4673 | |
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| pearson_dot | -0.4679 | |
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| spearman_dot | -0.4577 | |
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| pearson_max | -0.4628 | |
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| spearman_max | -0.4577 | |
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|
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#### Semantic Similarity |
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* Dataset: `sts-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 | Value | |
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|:--------------------|:-----------| |
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| pearson_cosine | -0.2036 | |
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| **spearman_cosine** | **-0.201** | |
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| pearson_manhattan | -0.1686 | |
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| spearman_manhattan | -0.1842 | |
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| pearson_euclidean | -0.1795 | |
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| spearman_euclidean | -0.1908 | |
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| pearson_dot | -0.2159 | |
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| spearman_dot | -0.2142 | |
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| pearson_max | -0.1686 | |
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| spearman_max | -0.1842 | |
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<!-- |
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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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--> |
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<!-- |
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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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--> |
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|
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## Training Details |
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### Training Dataset |
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#### afaji/indonli |
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* Dataset: [afaji/indonli](https://huggingface.co./datasets/afaji/indonli) |
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* Size: 10,000 training samples |
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* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | premise | hypothesis | label | |
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|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------| |
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| type | string | string | int | |
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| details | <ul><li>min: 12 tokens</li><li>mean: 29.73 tokens</li><li>max: 179 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.93 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>0: ~31.40%</li><li>1: ~34.60%</li><li>2: ~34.00%</li></ul> | |
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* Samples: |
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| premise | hypothesis | label | |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------|:---------------| |
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| <code>Presiden Joko Widodo (Jokowi) menyampaikan prediksi bahwa wabah virus Corona (COVID-19) di Indonesia akan selesai akhir tahun ini.</code> | <code>Prediksi akhir wabah tidak disampaikan Jokowi.</code> | <code>2</code> | |
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| <code>Meski biasanya hanya digunakan di fasilitas kesehatan, saat ini masker dan sarung tangan sekali pakai banyak dipakai di tingkat rumah tangga.</code> | <code>Masker sekali pakai banyak dipakai di tingkat rumah tangga.</code> | <code>0</code> | |
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| <code>Data dari Nielsen Music mencatat, "Joanne" telah terjual 201 ribu kopi di akhir minggu ini, seperti dilansir aceshowbiz.com.</code> | <code>Nielsen Music mencatat pada akhir minggu ini.</code> | <code>1</code> | |
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* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) |
|
|
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### Evaluation Dataset |
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|
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#### afaji/indonli |
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|
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* Dataset: [afaji/indonli](https://huggingface.co./datasets/afaji/indonli) |
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* Size: 1,000 evaluation samples |
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* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | premise | hypothesis | label | |
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|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------| |
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| type | string | string | int | |
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| details | <ul><li>min: 9 tokens</li><li>mean: 28.09 tokens</li><li>max: 179 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.01 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>0: ~37.00%</li><li>1: ~29.20%</li><li>2: ~33.80%</li></ul> | |
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* Samples: |
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| premise | hypothesis | label | |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|:---------------| |
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| <code>Manuskrip tersebut berisi tiga catatan yang menceritakan bagaimana peristiwa jatuhnya meteorit serta laporan kematian akibat kejadian tersebut seperti dilansir dari Science Alert, Sabtu (25/4/2020).</code> | <code>Manuskrip tersebut tidak mencatat laporan kematian.</code> | <code>2</code> | |
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| <code>Dilansir dari Business Insider, menurut observasi dari Mauna Loa Observatory di Hawaii pada karbon dioksida (CO2) di level mencapai 410 ppm tidak langsung memberikan efek pada pernapasan, karena tubuh manusia juga masih membutuhkan CO2 dalam kadar tertentu.</code> | <code>Tidak ada observasi yang pernah dilansir oleh Business Insider.</code> | <code>2</code> | |
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| <code>Perekonomian Jakarta terutama ditunjang oleh sektor perdagangan, jasa, properti, industri kreatif, dan keuangan.</code> | <code>Sektor jasa memberi pengaruh lebih besar daripada industri kreatif dalam perekonomian Jakarta.</code> | <code>1</code> | |
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* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) |
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|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: epoch |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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- `warmup_ratio`: 0.1 |
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- `fp16`: True |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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|
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 8 |
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- `per_device_eval_batch_size`: 8 |
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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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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 4 |
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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`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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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`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
|
- `include_inputs_for_metrics`: False |
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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 |
|
- `push_to_hub_organization`: None |
|
- `mp_parameters`: |
|
- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
|
- `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 |
|
- `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 |
|
- `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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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: proportional |
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|
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</details> |
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|
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### Training Logs |
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| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |
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|:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:| |
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| 0 | 0 | - | - | -0.0893 | - | |
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| 0.08 | 100 | 1.0851 | - | - | - | |
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| 0.16 | 200 | 1.0163 | - | - | - | |
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| 0.24 | 300 | 0.9524 | - | - | - | |
|
| 0.32 | 400 | 0.9257 | - | - | - | |
|
| 0.4 | 500 | 0.9397 | - | - | - | |
|
| 0.48 | 600 | 0.9125 | - | - | - | |
|
| 0.56 | 700 | 0.913 | - | - | - | |
|
| 0.64 | 800 | 0.8792 | - | - | - | |
|
| 0.72 | 900 | 0.932 | - | - | - | |
|
| 0.8 | 1000 | 0.9112 | - | - | - | |
|
| 0.88 | 1100 | 0.8809 | - | - | - | |
|
| 0.96 | 1200 | 0.8567 | - | - | - | |
|
| 1.0 | 1250 | - | 0.8585 | -0.4868 | - | |
|
| 1.04 | 1300 | 0.8482 | - | - | - | |
|
| 1.12 | 1400 | 0.7235 | - | - | - | |
|
| 1.2 | 1500 | 0.714 | - | - | - | |
|
| 1.28 | 1600 | 0.7053 | - | - | - | |
|
| 1.3600 | 1700 | 0.7205 | - | - | - | |
|
| 1.44 | 1800 | 0.7203 | - | - | - | |
|
| 1.52 | 1900 | 0.6957 | - | - | - | |
|
| 1.6 | 2000 | 0.7271 | - | - | - | |
|
| 1.6800 | 2100 | 0.7302 | - | - | - | |
|
| 1.76 | 2200 | 0.7054 | - | - | - | |
|
| 1.8400 | 2300 | 0.7134 | - | - | - | |
|
| 1.92 | 2400 | 0.6919 | - | - | - | |
|
| 2.0 | 2500 | 0.7416 | 0.8465 | -0.4085 | - | |
|
| 2.08 | 2600 | 0.4955 | - | - | - | |
|
| 2.16 | 2700 | 0.4484 | - | - | - | |
|
| 2.24 | 2800 | 0.4413 | - | - | - | |
|
| 2.32 | 2900 | 0.4567 | - | - | - | |
|
| 2.4 | 3000 | 0.4889 | - | - | - | |
|
| 2.48 | 3100 | 0.4284 | - | - | - | |
|
| 2.56 | 3200 | 0.5041 | - | - | - | |
|
| 2.64 | 3300 | 0.4755 | - | - | - | |
|
| 2.7200 | 3400 | 0.4726 | - | - | - | |
|
| 2.8 | 3500 | 0.4656 | - | - | - | |
|
| 2.88 | 3600 | 0.4389 | - | - | - | |
|
| 2.96 | 3700 | 0.4789 | - | - | - | |
|
| 3.0 | 3750 | - | 1.0011 | -0.4586 | - | |
|
| 3.04 | 3800 | 0.3492 | - | - | - | |
|
| 3.12 | 3900 | 0.2477 | - | - | - | |
|
| 3.2 | 4000 | 0.2556 | - | - | - | |
|
| 3.2800 | 4100 | 0.2531 | - | - | - | |
|
| 3.36 | 4200 | 0.2767 | - | - | - | |
|
| 3.44 | 4300 | 0.2665 | - | - | - | |
|
| 3.52 | 4400 | 0.2493 | - | - | - | |
|
| 3.6 | 4500 | 0.2757 | - | - | - | |
|
| 3.68 | 4600 | 0.2662 | - | - | - | |
|
| 3.76 | 4700 | 0.2666 | - | - | - | |
|
| 3.84 | 4800 | 0.2748 | - | - | - | |
|
| 3.92 | 4900 | 0.246 | - | - | - | |
|
| 4.0 | 5000 | 0.2411 | 1.2455 | -0.4665 | -0.2010 | |
|
|
|
|
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### Framework Versions |
|
- Python: 3.10.12 |
|
- Sentence Transformers: 3.0.1 |
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- Transformers: 4.41.2 |
|
- PyTorch: 2.3.0+cu121 |
|
- Accelerate: 0.31.0 |
|
- Datasets: 2.20.0 |
|
- Tokenizers: 0.19.1 |
|
|
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## Citation |
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|
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### BibTeX |
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|
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#### Sentence Transformers and SoftmaxLoss |
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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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