--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:212940 - loss:CosineSimilarityLoss base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 widget: - source_sentence: Ringkasan data strategis BPS 2012 sentences: - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Provinsi dan Jenis Pekerjaan Utama, 2021 - Laporan Perekonomian Indonesia 2007 - Statistik Potensi Desa Provinsi Banten 2008 - source_sentence: tahun berapa ekspor naik 2,37% dan impor naik 30,30%? sentences: - Bulan November 2006 Ekspor Naik 2,37 % dan Impor Naik 30,30 % - Indeks Harga Konsumen per Kelompok di 82 Kota 1 (2012=100) - 'Februari 2022: Tingkat Pengangguran Terbuka (TPT) sebesar 5,83 persen dan Rata-rata upah buruh sebesar 2,89 juta rupiah per bulan' - source_sentence: akses air bersih di indonesia (2005-2009) sentences: - Desember 2016, Rupiah Terapresiasi 0,74 Persen Terhadap Dolar Amerika - Statistik Air Bersih 2005-2009 - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Pendidikan Tertinggi yang Ditamatkan dan Lapangan Pekerjaan Utama di 17 Sektor (rupiah), 2018 - source_sentence: Tinjauan Regional Berdasarkan PDRB Kabupaten/Kota 2014-2018, Buku 2 Pulau Jawa dan Bali sentences: - Profil Migran Hasil Susenas 2011-2012 - Statistik Gas Kota 2004-2008 - Jumlah kunjungan wisman ke Indonesia melalui pintu masuk utama pada Juni 2022 mencapai 345,44 ribu kunjungan dan Jumlah penumpang angkutan udara internasional pada Juni 2022 naik 23,28 persen - source_sentence: perubahan nilai tukar petani bulan mei 2017 sentences: - Perkembangan Nilai Tukar Petani Mei 2017 - NTP Naik 0,15%, Harga Gabah Kualitas GKG Naik 0,98% - Statistik Restoran/Rumah Makan Tahun 2014 datasets: - yahyaabd/allstats-semantic-search-synthetic-dataset-v1 pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: allstats semantic search v1 3 dev type: allstats-semantic-search-v1-3-dev metrics: - type: pearson_cosine value: 0.9955935469233214 name: Pearson Cosine - type: spearman_cosine value: 0.9588270212992008 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: allstat semantic search v1 3 test type: allstat-semantic-search-v1-3-test metrics: - type: pearson_cosine value: 0.9955194411367296 name: Pearson Cosine - type: spearman_cosine value: 0.958337873285875 name: Spearman Cosine --- # SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2 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-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (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}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("yahyaabd/allstats-semantic-search-model-v1-3") # Run inference sentences = [ 'perubahan nilai tukar petani bulan mei 2017', 'Perkembangan Nilai Tukar Petani Mei 2017', 'Statistik Restoran/Rumah Makan Tahun 2014', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Datasets: `allstats-semantic-search-v1-3-dev` and `allstat-semantic-search-v1-3-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | allstats-semantic-search-v1-3-dev | allstat-semantic-search-v1-3-test | |:--------------------|:----------------------------------|:----------------------------------| | pearson_cosine | 0.9956 | 0.9955 | | **spearman_cosine** | **0.9588** | **0.9583** | ## Training Details ### Training Dataset #### allstats-semantic-search-synthetic-dataset-v1 * Dataset: [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) at [b13c0a7](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/b13c0a7412396a836cfbb887e140f183f3a6d65e) * Size: 212,940 training samples * Columns: query, doc, and label * Approximate statistics based on the first 1000 samples: | | query | doc | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | query | doc | label | |:---------------------------------------------------------------|:-----------------------------------------------------------------------|:------------------| | aDta industri besar dan sedang Indonesia 2008 | Statistik Industri Besar dan Sedang Indonesia 2008 | 0.9 | | profil bisnis konstruksi individu jawa barat 2022 | Statistik Industri Manufaktur Indonesia 2015 - Bahan Baku | 0.15 | | data statistik ekonomi indonesia | Nilai Tukar Valuta Asing di Indonesia 2014 | 0.08 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Evaluation Dataset #### allstats-semantic-search-synthetic-dataset-v1 * Dataset: [allstats-semantic-search-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1) at [b13c0a7](https://huggingface.co/datasets/yahyaabd/allstats-semantic-search-synthetic-dataset-v1/tree/b13c0a7412396a836cfbb887e140f183f3a6d65e) * Size: 26,618 evaluation samples * Columns: query, doc, and label * Approximate statistics based on the first 1000 samples: | | query | doc | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | query | doc | label | |:-------------------------------------------------------------------|:---------------------------------------------------------------------------|:------------------| | tahun berapa ekspor naik 2,37% dan impor naik 30,30%? | Bulan November 2006 Ekspor Naik 2,37 % dan Impor Naik 30,30 % | 1.0 | | Berapa produksi padi pada tahun 2023? | Produksi padi tahun lainnya | 0.0 | | data statistik solus per aqua 2015 | Statistik Solus Per Aqua (SPA) 2015 | 0.97 | * Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters: ```json { "loss_fct": "torch.nn.modules.loss.MSELoss" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `num_train_epochs`: 12 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 12 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-search-v1-3-dev_spearman_cosine | allstat-semantic-search-v1-3-test_spearman_cosine | |:-------:|:-----:|:-------------:|:---------------:|:-------------------------------------------------:|:-------------------------------------------------:| | 0.0751 | 500 | 0.0653 | 0.0400 | 0.7035 | - | | 0.1503 | 1000 | 0.0361 | 0.0296 | 0.7310 | - | | 0.2254 | 1500 | 0.0278 | 0.0226 | 0.7669 | - | | 0.3005 | 2000 | 0.0226 | 0.0195 | 0.7748 | - | | 0.3757 | 2500 | 0.0208 | 0.0183 | 0.7769 | - | | 0.4508 | 3000 | 0.0184 | 0.0172 | 0.7994 | - | | 0.5259 | 3500 | 0.0179 | 0.0159 | 0.7931 | - | | 0.6011 | 4000 | 0.0159 | 0.0155 | 0.7966 | - | | 0.6762 | 4500 | 0.0161 | 0.0150 | 0.8047 | - | | 0.7513 | 5000 | 0.0163 | 0.0153 | 0.7910 | - | | 0.8264 | 5500 | 0.0158 | 0.0155 | 0.7956 | - | | 0.9016 | 6000 | 0.0149 | 0.0141 | 0.8148 | - | | 0.9767 | 6500 | 0.0149 | 0.0145 | 0.8287 | - | | 1.0518 | 7000 | 0.0148 | 0.0150 | 0.7933 | - | | 1.1270 | 7500 | 0.0131 | 0.0136 | 0.8083 | - | | 1.2021 | 8000 | 0.0124 | 0.0131 | 0.8173 | - | | 1.2772 | 8500 | 0.0133 | 0.0130 | 0.8117 | - | | 1.3524 | 9000 | 0.012 | 0.0126 | 0.8259 | - | | 1.4275 | 9500 | 0.0119 | 0.0120 | 0.8178 | - | | 1.5026 | 10000 | 0.0116 | 0.0118 | 0.8332 | - | | 1.5778 | 10500 | 0.0132 | 0.0123 | 0.8108 | - | | 1.6529 | 11000 | 0.0114 | 0.0111 | 0.8365 | - | | 1.7280 | 11500 | 0.0105 | 0.0109 | 0.8235 | - | | 1.8032 | 12000 | 0.0107 | 0.0105 | 0.8445 | - | | 1.8783 | 12500 | 0.0106 | 0.0101 | 0.8330 | - | | 1.9534 | 13000 | 0.0095 | 0.0096 | 0.8437 | - | | 2.0285 | 13500 | 0.0093 | 0.0094 | 0.8417 | - | | 2.1037 | 14000 | 0.0079 | 0.0093 | 0.8485 | - | | 2.1788 | 14500 | 0.008 | 0.0089 | 0.8422 | - | | 2.2539 | 15000 | 0.0081 | 0.0086 | 0.8485 | - | | 2.3291 | 15500 | 0.008 | 0.0084 | 0.8530 | - | | 2.4042 | 16000 | 0.007 | 0.0084 | 0.8597 | - | | 2.4793 | 16500 | 0.0081 | 0.0087 | 0.8499 | - | | 2.5545 | 17000 | 0.0078 | 0.0078 | 0.8577 | - | | 2.6296 | 17500 | 0.007 | 0.0080 | 0.8559 | - | | 2.7047 | 18000 | 0.0072 | 0.0078 | 0.8569 | - | | 2.7799 | 18500 | 0.0069 | 0.0079 | 0.8579 | - | | 2.8550 | 19000 | 0.0064 | 0.0072 | 0.8693 | - | | 2.9301 | 19500 | 0.0064 | 0.0070 | 0.8747 | - | | 3.0053 | 20000 | 0.0061 | 0.0068 | 0.8757 | - | | 3.0804 | 20500 | 0.0052 | 0.0069 | 0.8727 | - | | 3.1555 | 21000 | 0.005 | 0.0067 | 0.8734 | - | | 3.2307 | 21500 | 0.0054 | 0.0065 | 0.8727 | - | | 3.3058 | 22000 | 0.0058 | 0.0070 | 0.8715 | - | | 3.3809 | 22500 | 0.0056 | 0.0066 | 0.8724 | - | | 3.4560 | 23000 | 0.0056 | 0.0070 | 0.8740 | - | | 3.5312 | 23500 | 0.0054 | 0.0060 | 0.8775 | - | | 3.6063 | 24000 | 0.0051 | 0.0062 | 0.8746 | - | | 3.6814 | 24500 | 0.0047 | 0.0060 | 0.8765 | - | | 3.7566 | 25000 | 0.0051 | 0.0067 | 0.8783 | - | | 3.8317 | 25500 | 0.0048 | 0.0058 | 0.8824 | - | | 3.9068 | 26000 | 0.0048 | 0.0059 | 0.8862 | - | | 3.9820 | 26500 | 0.005 | 0.0056 | 0.8853 | - | | 4.0571 | 27000 | 0.0042 | 0.0053 | 0.8868 | - | | 4.1322 | 27500 | 0.0036 | 0.0056 | 0.8893 | - | | 4.2074 | 28000 | 0.0041 | 0.0052 | 0.8954 | - | | 4.2825 | 28500 | 0.0041 | 0.0050 | 0.8943 | - | | 4.3576 | 29000 | 0.0036 | 0.0050 | 0.8890 | - | | 4.4328 | 29500 | 0.0036 | 0.0046 | 0.8990 | - | | 4.5079 | 30000 | 0.0038 | 0.0051 | 0.8934 | - | | 4.5830 | 30500 | 0.0037 | 0.0049 | 0.9011 | - | | 4.6582 | 31000 | 0.0036 | 0.0049 | 0.9000 | - | | 4.7333 | 31500 | 0.0041 | 0.0052 | 0.8938 | - | | 4.8084 | 32000 | 0.004 | 0.0049 | 0.8971 | - | | 4.8835 | 32500 | 0.0038 | 0.0043 | 0.9023 | - | | 4.9587 | 33000 | 0.0036 | 0.0044 | 0.9006 | - | | 5.0338 | 33500 | 0.0032 | 0.0043 | 0.9042 | - | | 5.1089 | 34000 | 0.0031 | 0.0042 | 0.9054 | - | | 5.1841 | 34500 | 0.0028 | 0.0042 | 0.9052 | - | | 5.2592 | 35000 | 0.0028 | 0.0043 | 0.9065 | - | | 5.3343 | 35500 | 0.003 | 0.0041 | 0.9093 | - | | 5.4095 | 36000 | 0.0029 | 0.0042 | 0.9084 | - | | 5.4846 | 36500 | 0.0029 | 0.0044 | 0.9078 | - | | 5.5597 | 37000 | 0.0027 | 0.0043 | 0.9062 | - | | 5.6349 | 37500 | 0.003 | 0.0039 | 0.9101 | - | | 5.7100 | 38000 | 0.0027 | 0.0041 | 0.9092 | - | | 5.7851 | 38500 | 0.0025 | 0.0039 | 0.9140 | - | | 5.8603 | 39000 | 0.0027 | 0.0037 | 0.9138 | - | | 5.9354 | 39500 | 0.0027 | 0.0037 | 0.9137 | - | | 6.0105 | 40000 | 0.0027 | 0.0036 | 0.9162 | - | | 6.0856 | 40500 | 0.002 | 0.0035 | 0.9209 | - | | 6.1608 | 41000 | 0.0021 | 0.0037 | 0.9180 | - | | 6.2359 | 41500 | 0.0023 | 0.0036 | 0.9183 | - | | 6.3110 | 42000 | 0.0024 | 0.0035 | 0.9218 | - | | 6.3862 | 42500 | 0.002 | 0.0033 | 0.9216 | - | | 6.4613 | 43000 | 0.0024 | 0.0035 | 0.9220 | - | | 6.5364 | 43500 | 0.0018 | 0.0034 | 0.9232 | - | | 6.6116 | 44000 | 0.0021 | 0.0033 | 0.9236 | - | | 6.6867 | 44500 | 0.0021 | 0.0035 | 0.9225 | - | | 6.7618 | 45000 | 0.0027 | 0.0031 | 0.9227 | - | | 6.8370 | 45500 | 0.0019 | 0.0032 | 0.9242 | - | | 6.9121 | 46000 | 0.0022 | 0.0033 | 0.9224 | - | | 6.9872 | 46500 | 0.0022 | 0.0030 | 0.9252 | - | | 7.0624 | 47000 | 0.0017 | 0.0029 | 0.9294 | - | | 7.1375 | 47500 | 0.0014 | 0.0028 | 0.9304 | - | | 7.2126 | 48000 | 0.0015 | 0.0028 | 0.9324 | - | | 7.2878 | 48500 | 0.0014 | 0.0030 | 0.9313 | - | | 7.3629 | 49000 | 0.0015 | 0.0029 | 0.9333 | - | | 7.4380 | 49500 | 0.0015 | 0.0028 | 0.9342 | - | | 7.5131 | 50000 | 0.0018 | 0.0030 | 0.9261 | - | | 7.5883 | 50500 | 0.0016 | 0.0030 | 0.9329 | - | | 7.6634 | 51000 | 0.0019 | 0.0026 | 0.9334 | - | | 7.7385 | 51500 | 0.0018 | 0.0029 | 0.9336 | - | | 7.8137 | 52000 | 0.0016 | 0.0026 | 0.9353 | - | | 7.8888 | 52500 | 0.0016 | 0.0026 | 0.9351 | - | | 7.9639 | 53000 | 0.0017 | 0.0024 | 0.9356 | - | | 8.0391 | 53500 | 0.0013 | 0.0023 | 0.9396 | - | | 8.1142 | 54000 | 0.0012 | 0.0024 | 0.9390 | - | | 8.1893 | 54500 | 0.001 | 0.0024 | 0.9421 | - | | 8.2645 | 55000 | 0.0012 | 0.0024 | 0.9406 | - | | 8.3396 | 55500 | 0.0012 | 0.0023 | 0.9407 | - | | 8.4147 | 56000 | 0.0012 | 0.0024 | 0.9398 | - | | 8.4899 | 56500 | 0.0012 | 0.0024 | 0.9412 | - | | 8.5650 | 57000 | 0.0014 | 0.0024 | 0.9397 | - | | 8.6401 | 57500 | 0.0013 | 0.0023 | 0.9411 | - | | 8.7153 | 58000 | 0.0013 | 0.0023 | 0.9418 | - | | 8.7904 | 58500 | 0.0014 | 0.0022 | 0.9432 | - | | 8.8655 | 59000 | 0.0011 | 0.0022 | 0.9448 | - | | 8.9406 | 59500 | 0.0012 | 0.0022 | 0.9455 | - | | 9.0158 | 60000 | 0.0012 | 0.0021 | 0.9453 | - | | 9.0909 | 60500 | 0.0009 | 0.0021 | 0.9461 | - | | 9.1660 | 61000 | 0.0009 | 0.0021 | 0.9465 | - | | 9.2412 | 61500 | 0.0009 | 0.0021 | 0.9471 | - | | 9.3163 | 62000 | 0.0009 | 0.0021 | 0.9477 | - | | 9.3914 | 62500 | 0.0008 | 0.0020 | 0.9482 | - | | 9.4666 | 63000 | 0.0012 | 0.0020 | 0.9478 | - | | 9.5417 | 63500 | 0.0009 | 0.0020 | 0.9479 | - | | 9.6168 | 64000 | 0.0009 | 0.0020 | 0.9485 | - | | 9.6920 | 64500 | 0.0011 | 0.0020 | 0.9492 | - | | 9.7671 | 65000 | 0.0008 | 0.0019 | 0.9497 | - | | 9.8422 | 65500 | 0.001 | 0.0019 | 0.9504 | - | | 9.9174 | 66000 | 0.0009 | 0.0019 | 0.9518 | - | | 9.9925 | 66500 | 0.0009 | 0.0019 | 0.9510 | - | | 10.0676 | 67000 | 0.0008 | 0.0018 | 0.9517 | - | | 10.1427 | 67500 | 0.0007 | 0.0018 | 0.9524 | - | | 10.2179 | 68000 | 0.0007 | 0.0018 | 0.9521 | - | | 10.2930 | 68500 | 0.0008 | 0.0019 | 0.9526 | - | | 10.3681 | 69000 | 0.0007 | 0.0019 | 0.9529 | - | | 10.4433 | 69500 | 0.0008 | 0.0018 | 0.9541 | - | | 10.5184 | 70000 | 0.0007 | 0.0017 | 0.9551 | - | | 10.5935 | 70500 | 0.0007 | 0.0018 | 0.9550 | - | | 10.6687 | 71000 | 0.0008 | 0.0017 | 0.9554 | - | | 10.7438 | 71500 | 0.0007 | 0.0017 | 0.9558 | - | | 10.8189 | 72000 | 0.0007 | 0.0018 | 0.9558 | - | | 10.8941 | 72500 | 0.0007 | 0.0018 | 0.9562 | - | | 10.9692 | 73000 | 0.0009 | 0.0017 | 0.9559 | - | | 11.0443 | 73500 | 0.0005 | 0.0017 | 0.9571 | - | | 11.1195 | 74000 | 0.0006 | 0.0017 | 0.9570 | - | | 11.1946 | 74500 | 0.0005 | 0.0017 | 0.9573 | - | | 11.2697 | 75000 | 0.0005 | 0.0017 | 0.9574 | - | | 11.3449 | 75500 | 0.0006 | 0.0017 | 0.9576 | - | | 11.4200 | 76000 | 0.0006 | 0.0017 | 0.9577 | - | | 11.4951 | 76500 | 0.0006 | 0.0017 | 0.9577 | - | | 11.5702 | 77000 | 0.0005 | 0.0016 | 0.9582 | - | | 11.6454 | 77500 | 0.0006 | 0.0017 | 0.9583 | - | | 11.7205 | 78000 | 0.0005 | 0.0016 | 0.9584 | - | | 11.7956 | 78500 | 0.0005 | 0.0016 | 0.9587 | - | | 11.8708 | 79000 | 0.0005 | 0.0016 | 0.9588 | - | | 11.9459 | 79500 | 0.0005 | 0.0016 | 0.9588 | - | | 12.0 | 79860 | - | - | - | 0.9583 |
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.2.2+cu121 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ```