cassador commited on
Commit
3038ada
1 Parent(s): 2c816f5

Add new SentenceTransformer model.

Browse files
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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+ base_model: indobenchmark/indobert-base-p2
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+ datasets: []
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+ language: []
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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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+ - 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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+ 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:133472
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+ - loss:SoftmaxLoss
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+ widget:
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+ - source_sentence: Dua tim anak-anak, yang satu berwarna hijau dan yang lainnya berwarna
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+ merah, bermain bersama dalam permainan Rugby saat hujan.
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+ sentences:
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+ - Tiga orang berada di dalam perahu.
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+ - seorang pria di atas sepeda
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+ - Tim rugby anak-anak, merah versus hijau bermain di tengah hujan.
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+ - source_sentence: Seorang pria melakukan perawatan di rel kereta api
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+ sentences:
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+ - Dua orang terlibat dalam percakapan.
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+ - Ada seorang wanita melakukan pekerjaan di rel kereta api.
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+ - orang-orang duduk di bar
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+ - source_sentence: Sepasang suami istri dengan pakaian renang berjalan di pantai.
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+ sentences:
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+ - pasangan itu duduk di dalam
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+ - Pria itu sedang makan.
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+ - Dua orang sedang berpose untuk difoto.
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+ - source_sentence: Dua orang sedang duduk di samping api unggun bertumpuk kayu di
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+ malam hari.
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+ sentences:
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+ - Seseorang memegang jeruk dan berjalan
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+ - Orang-orang duduk di luar di malam hari.
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+ - Orang-orang berada di luar.
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+ - source_sentence: Wanita profesional di meja pendaftaran acara sementara pria berjas
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+ melihat.
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+ sentences:
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+ - Orang-orang berkumpul untuk sebuah acara.
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+ - Seorang wanita sedang berjalan menuju taman.
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+ - Ada seorang anak yang tersenyum untuk difoto.
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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: 0.23146247451934734
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.23182555096720683
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+ name: Spearman Cosine
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+ - type: pearson_manhattan
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+ value: 0.19847600869622337
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: 0.2038189662328075
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+ name: Spearman Manhattan
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+ - type: pearson_euclidean
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+ value: 0.198744291061789
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: 0.20385658228775938
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+ name: Spearman Euclidean
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+ - type: pearson_dot
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+ value: 0.2561502821889763
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: 0.25101474046220823
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: 0.2561502821889763
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: 0.25101474046220823
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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: 0.5914831439397401
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+ name: Pearson Cosine
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+ - type: spearman_cosine
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+ value: 0.5978838704506128
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+ name: Spearman Cosine
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+ - type: pearson_manhattan
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+ value: 0.5131648451956073
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+ name: Pearson Manhattan
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+ - type: spearman_manhattan
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+ value: 0.5147175261736068
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+ name: Spearman Manhattan
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+ - type: pearson_euclidean
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+ value: 0.5942850778734059
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+ name: Pearson Euclidean
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+ - type: spearman_euclidean
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+ value: 0.6001963453484881
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+ name: Spearman Euclidean
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+ - type: pearson_dot
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+ value: 0.5880400881430983
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+ name: Pearson Dot
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+ - type: spearman_dot
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+ value: 0.5933998114680769
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+ name: Spearman Dot
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+ - type: pearson_max
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+ value: 0.5942850778734059
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+ name: Pearson Max
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+ - type: spearman_max
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+ value: 0.6001963453484881
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+ 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). 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:** Unknown -->
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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': 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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+
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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-snli-v1")
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+ # Run inference
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+ sentences = [
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+ 'Wanita profesional di meja pendaftaran acara sementara pria berjas melihat.',
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+ 'Orang-orang berkumpul untuk sebuah acara.',
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+ 'Ada seorang anak yang tersenyum untuk difoto.',
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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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+ * 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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+
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+ | Metric | Value |
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+ |:--------------------|:-----------|
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+ | pearson_cosine | 0.2315 |
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+ | **spearman_cosine** | **0.2318** |
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+ | pearson_manhattan | 0.1985 |
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+ | spearman_manhattan | 0.2038 |
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+ | pearson_euclidean | 0.1987 |
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+ | spearman_euclidean | 0.2039 |
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+ | pearson_dot | 0.2562 |
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+ | spearman_dot | 0.251 |
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+ | pearson_max | 0.2562 |
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+ | spearman_max | 0.251 |
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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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+
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+ | Metric | Value |
246
+ |:--------------------|:-----------|
247
+ | pearson_cosine | 0.5915 |
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+ | **spearman_cosine** | **0.5979** |
249
+ | pearson_manhattan | 0.5132 |
250
+ | spearman_manhattan | 0.5147 |
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+ | pearson_euclidean | 0.5943 |
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+ | spearman_euclidean | 0.6002 |
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+ | pearson_dot | 0.588 |
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+ | spearman_dot | 0.5934 |
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+ | pearson_max | 0.5943 |
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+ | spearman_max | 0.6002 |
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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.*
262
+ -->
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+
264
+ <!--
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+ ### Recommendations
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+
267
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
268
+ -->
269
+
270
+ ## Training Details
271
+
272
+ ### Training Dataset
273
+
274
+ #### Unnamed Dataset
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+
276
+
277
+ * Size: 133,472 training samples
278
+ * Columns: <code>label</code>, <code>kalimat1</code>, and <code>kalimat2</code>
279
+ * Approximate statistics based on the first 1000 samples:
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+ | | label | kalimat1 | kalimat2 |
281
+ |:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
282
+ | type | int | string | string |
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+ | details | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.47 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.62 tokens</li><li>max: 22 tokens</li></ul> |
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+ * Samples:
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+ | label | kalimat1 | kalimat2 |
286
+ |:---------------|:------------------------------------------------------------------|:----------------------------------------------------------------|
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+ | <code>0</code> | <code>Seseorang di atas kuda melompati pesawat yang rusak.</code> | <code>Seseorang sedang makan malam, memesan telur dadar.</code> |
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+ | <code>1</code> | <code>Seseorang di atas kuda melompati pesawat yang rusak.</code> | <code>Seseorang berada di luar ruangan, di atas kuda.</code> |
289
+ | <code>1</code> | <code>Anak-anak tersenyum dan melambai ke kamera</code> | <code>Ada anak-anak yang hadir</code> |
290
+ * Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
291
+
292
+ ### Evaluation Dataset
293
+
294
+ #### Unnamed Dataset
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+
296
+
297
+ * Size: 6,607 evaluation samples
298
+ * Columns: <code>label</code>, <code>kalimat1</code>, and <code>kalimat2</code>
299
+ * Approximate statistics based on the first 1000 samples:
300
+ | | label | kalimat1 | kalimat2 |
301
+ |:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
302
+ | type | int | string | string |
303
+ | details | <ul><li>0: ~50.10%</li><li>1: ~49.90%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.87 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.45 tokens</li><li>max: 27 tokens</li></ul> |
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+ * Samples:
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+ | label | kalimat1 | kalimat2 |
306
+ |:---------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------|
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+ | <code>1</code> | <code>Dua wanita berpelukan sambil memegang paket untuk pergi.</code> | <code>Dua wanita memegang paket.</code> |
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+ | <code>0</code> | <code>Dua wanita berpelukan sambil memegang paket untuk pergi.</code> | <code>Orang-orang berkelahi di luar toko makanan.</code> |
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+ | <code>1</code> | <code>Dua anak kecil berbaju biru, satu dengan nomor 9 dan satu dengan nomor 2 berdiri di tangga kayu di kamar mandi dan mencuci tangan di wastafel.</code> | <code>Dua anak dengan kaus bernomor mencuci tangan mereka.</code> |
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+ * Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
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+
312
+ ### 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`: 32
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+ - `per_device_eval_batch_size`: 32
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+ - `learning_rate`: 2e-05
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+ - `num_train_epochs`: 2
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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`: 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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+ - `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`: 2
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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}
387
+ - `fsdp_transformer_layer_cls_to_wrap`: None
388
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
389
+ - `deepspeed`: None
390
+ - `label_smoothing_factor`: 0.0
391
+ - `optim`: adamw_torch
392
+ - `optim_args`: None
393
+ - `adafactor`: False
394
+ - `group_by_length`: False
395
+ - `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
400
+ - `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
405
+ - `hub_model_id`: None
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+ - `hub_strategy`: every_save
407
+ - `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
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+ - `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
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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
426
+ - `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
431
+ - `batch_eval_metrics`: False
432
+ - `batch_sampler`: batch_sampler
433
+ - `multi_dataset_batch_sampler`: proportional
434
+
435
+ </details>
436
+
437
+ ### Training Logs
438
+ | Epoch | Step | sts-dev_spearman_cosine | sts-test_spearman_cosine |
439
+ |:-----:|:----:|:-----------------------:|:------------------------:|
440
+ | 0 | 0 | 0.2318 | - |
441
+ | 2.0 | 8342 | - | 0.5979 |
442
+
443
+
444
+ ### Framework Versions
445
+ - Python: 3.10.12
446
+ - Sentence Transformers: 3.0.1
447
+ - Transformers: 4.41.2
448
+ - PyTorch: 2.3.0+cu121
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+ - Accelerate: 0.31.0
450
+ - Datasets: 2.20.0
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+ - Tokenizers: 0.19.1
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+
453
+ ## Citation
454
+
455
+ ### BibTeX
456
+
457
+ #### 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",
462
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
463
+ month = "11",
464
+ year = "2019",
465
+ publisher = "Association for Computational Linguistics",
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+ url = "https://arxiv.org/abs/1908.10084",
467
+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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