| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:123637 |
| - loss:CosineSimilarityLoss |
| base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
| widget: |
| - source_sentence: Analisis biaya hidup di tiga kota Banten thn 2018 |
| sentences: |
| - Indikator Konstruksi Triwulan I-2007 |
| - Survei Biaya Hidup (SBH) 2018 Bengkulu |
| - Indikator Ekonomi Februari 2002 |
| - source_sentence: Grafik ekspor hasil minyak Indonesia ke berbagai negara dari tahun |
| 2000 hingga 2023. |
| sentences: |
| - Sistem Neraca Sosial Ekonomi Indonesia Tahun 2022 dalam Format SNA 1968 (65x65) |
| - Harga Produsen Gabah dan Beras Januari 2020 |
| - Profil Usaha Konstruksi Perorangan Provinsi Papua 2016 |
| - source_sentence: Tren konstruksi Indonesia tahun 2007 Q4 |
| sentences: |
| - Laporan Bulanan Data Sosial Ekonomi Desember 2018 |
| - Indeks Unit Value Ekspor Menurut Kode SITC Bulan Februari 2023 |
| - Inflasi Februari 2008 sebesar 0,5 persen |
| - source_sentence: Informasi tentang kepemilikan dan penggunaan AC di rumah tangga |
| Indonesia tahun 2013? |
| sentences: |
| - Data dan Informasi Kemiskinan Kabupaten/Kota Tahun 2014 |
| - Rata-rata Upah/Gaji Bersih Sebulan Buruh/Karyawan/Pegawai Menurut Kelompok Umur |
| dan Jenis Pekerjaan, 2022-2023 |
| - Indikator Konstruksi, Triwulan II-2022 |
| - source_sentence: Statistik harga Ternate 2012 |
| sentences: |
| - Statistik Perhubungan 2005 |
| - Indeks Unit Value Ekspor Menurut Kode SITC Bulan Januari 2019 |
| - Indikator Ekonomi Agustus 2002 |
| datasets: |
| - yahyaabd/allstats-semantic-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 base v1 eval |
| type: allstats-semantic-base-v1-eval |
| metrics: |
| - type: pearson_cosine |
| value: 0.9868927327091045 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.9277441071536588 |
| name: Spearman Cosine |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: allstat semantic base v1 test |
| type: allstat-semantic-base-v1-test |
| metrics: |
| - type: pearson_cosine |
| value: 0.9867639981224826 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.9256998894451143 |
| 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-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-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) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1) |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### 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-base-v1-2") |
| # Run inference |
| sentences = [ |
| 'Statistik harga Ternate 2012', |
| 'Indikator Ekonomi Agustus 2002', |
| 'Indeks Unit Value Ekspor Menurut Kode SITC Bulan Januari 2019', |
| ] |
| 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] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Semantic Similarity |
|
|
| * Datasets: `allstats-semantic-base-v1-eval` and `allstat-semantic-base-v1-test` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | allstats-semantic-base-v1-eval | allstat-semantic-base-v1-test | |
| |:--------------------|:-------------------------------|:------------------------------| |
| | pearson_cosine | 0.9869 | 0.9868 | |
| | **spearman_cosine** | **0.9277** | **0.9257** | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Dataset |
| |
| #### allstats-semantic-synthetic-dataset-v1 |
| |
| * Dataset: [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1) at [e73718f](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1/tree/e73718fb155f47b2c5cf8c4e00f0690d37bac9fa) |
| * Size: 123,637 training samples |
| * Columns: <code>query</code>, <code>doc</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | doc | label | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 10.59 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.29 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:--------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------|:------------------| |
| | <code>Analisis upah tenaga kerja ekonomi kreatif</code> | <code>Upah Tenaga Kerja Ekonomi Kreatif 2011-2016</code> | <code>0.88</code> | |
| | <code>cari data persentase rumah tangga yang menggunakan listrik pln menurut provinsi dari 1993 sampai 2022.</code> | <code>Persentase Rumah Tangga menurut Provinsi dan Sumber Penerangan Listrik PLN, 1993-2022</code> | <code>0.93</code> | |
| | <code>apakah ada tabel yang menunjukkan ekspor minyak mentah ke negara tujuan utama tahun 2000-2023?</code> | <code>IHK dan Rata-rata Upah per Bulan Buruh Peternakan dan Perikanan di Bawah Mandor (Supervisor), 2012-2014 (2012=100)</code> | <code>0.13</code> | |
| * Loss: [<code>CosineSimilarityLoss</code>](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-synthetic-dataset-v1 |
| |
| * Dataset: [allstats-semantic-synthetic-dataset-v1](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1) at [e73718f](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1/tree/e73718fb155f47b2c5cf8c4e00f0690d37bac9fa) |
| * Size: 26,494 evaluation samples |
| * Columns: <code>query</code>, <code>doc</code>, and <code>label</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | query | doc | label | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | string | string | float | |
| | details | <ul><li>min: 5 tokens</li><li>mean: 10.66 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 13.94 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.49</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:--------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------| |
| | <code>SBH Aceh 2018: Meulaboh, Banda Aceh, Lhokseumawe</code> | <code>Survei Biaya Hidup (SBH) 2018 Meulaboh, Banda Aceh, dan Lhokseumawe</code> | <code>0.9</code> | |
| | <code>ekspor produk indonesia juli 2018 per negara</code> | <code>Direktori Perusahaan Pertambangan Besar 2013</code> | <code>0.07</code> | |
| | <code>peternakan sapi di jawa tengah 2011</code> | <code>Laporan Bulanan Data Sosial Ekonomi Juli 2024</code> | <code>0.07</code> | |
| * Loss: [<code>CosineSimilarityLoss</code>](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`: 64 |
| - `per_device_eval_batch_size`: 64 |
| - `num_train_epochs`: 24 |
| - `warmup_ratio`: 0.1 |
| - `fp16`: True |
| - `dataloader_num_workers`: 4 |
| - `load_best_model_at_end`: True |
| - `label_smoothing_factor`: 0.1 |
| - `eval_on_start`: True |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: steps |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 64 |
| - `per_device_eval_batch_size`: 64 |
| - `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`: 24 |
| - `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`: 4 |
| - `dataloader_prefetch_factor`: None |
| - `past_index`: -1 |
| - `disable_tqdm`: False |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `load_best_model_at_end`: True |
| - `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`: 0.1 |
| - `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`: True |
| - `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 |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-base-v1-eval_spearman_cosine | allstat-semantic-base-v1-test_spearman_cosine | |
| |:-----------:|:---------:|:-------------:|:---------------:|:----------------------------------------------:|:---------------------------------------------:| |
| | 0 | 0 | - | 0.0942 | 0.6574 | - | |
| | 0.2588 | 500 | 0.0449 | 0.0262 | 0.7353 | - | |
| | 0.5176 | 1000 | 0.0232 | 0.0185 | 0.7592 | - | |
| | 0.7764 | 1500 | 0.0172 | 0.0154 | 0.7760 | - | |
| | 1.0352 | 2000 | 0.0153 | 0.0137 | 0.7905 | - | |
| | 1.2940 | 2500 | 0.0124 | 0.0130 | 0.7920 | - | |
| | 1.5528 | 3000 | 0.0119 | 0.0120 | 0.8048 | - | |
| | 1.8116 | 3500 | 0.0121 | 0.0121 | 0.8021 | - | |
| | 2.0704 | 4000 | 0.0114 | 0.0112 | 0.8018 | - | |
| | 2.3292 | 4500 | 0.0093 | 0.0117 | 0.7996 | - | |
| | 2.5880 | 5000 | 0.0097 | 0.0105 | 0.8133 | - | |
| | 2.8468 | 5500 | 0.0092 | 0.0103 | 0.8137 | - | |
| | 3.1056 | 6000 | 0.0085 | 0.0094 | 0.8247 | - | |
| | 3.3644 | 6500 | 0.0068 | 0.0090 | 0.8326 | - | |
| | 3.6232 | 7000 | 0.0073 | 0.0092 | 0.8273 | - | |
| | 3.8820 | 7500 | 0.007 | 0.0084 | 0.8404 | - | |
| | 4.1408 | 8000 | 0.0061 | 0.0083 | 0.8381 | - | |
| | 4.3996 | 8500 | 0.0057 | 0.0082 | 0.8382 | - | |
| | 4.6584 | 9000 | 0.0056 | 0.0074 | 0.8458 | - | |
| | 4.9172 | 9500 | 0.0057 | 0.0073 | 0.8468 | - | |
| | 5.1760 | 10000 | 0.0045 | 0.0071 | 0.8508 | - | |
| | 5.4348 | 10500 | 0.0041 | 0.0069 | 0.8579 | - | |
| | 5.6936 | 11000 | 0.0047 | 0.0069 | 0.8471 | - | |
| | 5.9524 | 11500 | 0.0046 | 0.0067 | 0.8554 | - | |
| | 6.2112 | 12000 | 0.0034 | 0.0062 | 0.8616 | - | |
| | 6.4700 | 12500 | 0.0034 | 0.0063 | 0.8636 | - | |
| | 6.7288 | 13000 | 0.0036 | 0.0062 | 0.8649 | - | |
| | 6.9876 | 13500 | 0.0037 | 0.0063 | 0.8641 | - | |
| | 7.2464 | 14000 | 0.0027 | 0.0059 | 0.8691 | - | |
| | 7.5052 | 14500 | 0.0027 | 0.0060 | 0.8733 | - | |
| | 7.7640 | 15000 | 0.0031 | 0.0060 | 0.8748 | - | |
| | 8.0228 | 15500 | 0.0028 | 0.0058 | 0.8736 | - | |
| | 8.2816 | 16000 | 0.0023 | 0.0055 | 0.8785 | - | |
| | 8.5404 | 16500 | 0.0025 | 0.0054 | 0.8801 | - | |
| | 8.7992 | 17000 | 0.0024 | 0.0058 | 0.8809 | - | |
| | 9.0580 | 17500 | 0.0026 | 0.0058 | 0.8811 | - | |
| | 9.3168 | 18000 | 0.002 | 0.0055 | 0.8824 | - | |
| | 9.5756 | 18500 | 0.002 | 0.0053 | 0.8859 | - | |
| | 9.8344 | 19000 | 0.0021 | 0.0053 | 0.8851 | - | |
| | 10.0932 | 19500 | 0.0019 | 0.0055 | 0.8904 | - | |
| | 10.3520 | 20000 | 0.0016 | 0.0052 | 0.8946 | - | |
| | 10.6108 | 20500 | 0.0017 | 0.0057 | 0.8884 | - | |
| | 10.8696 | 21000 | 0.0019 | 0.0055 | 0.8889 | - | |
| | 11.1284 | 21500 | 0.0016 | 0.0052 | 0.8942 | - | |
| | 11.3872 | 22000 | 0.0014 | 0.0053 | 0.8961 | - | |
| | 11.6460 | 22500 | 0.0016 | 0.0053 | 0.8928 | - | |
| | 11.9048 | 23000 | 0.0017 | 0.0051 | 0.8947 | - | |
| | 12.1636 | 23500 | 0.0013 | 0.0050 | 0.9015 | - | |
| | 12.4224 | 24000 | 0.0012 | 0.0059 | 0.8886 | - | |
| | 12.6812 | 24500 | 0.0014 | 0.0051 | 0.9030 | - | |
| | 12.9400 | 25000 | 0.0014 | 0.0051 | 0.9012 | - | |
| | 13.1988 | 25500 | 0.0011 | 0.0050 | 0.9037 | - | |
| | 13.4576 | 26000 | 0.0011 | 0.0050 | 0.9053 | - | |
| | 13.7164 | 26500 | 0.0011 | 0.0049 | 0.9060 | - | |
| | 13.9752 | 27000 | 0.0011 | 0.0049 | 0.9086 | - | |
| | 14.2340 | 27500 | 0.001 | 0.0048 | 0.9063 | - | |
| | 14.4928 | 28000 | 0.001 | 0.0051 | 0.9056 | - | |
| | 14.7516 | 28500 | 0.001 | 0.0051 | 0.9079 | - | |
| | 15.0104 | 29000 | 0.0011 | 0.0049 | 0.9080 | - | |
| | 15.2692 | 29500 | 0.0008 | 0.0048 | 0.9126 | - | |
| | 15.5280 | 30000 | 0.0008 | 0.0049 | 0.9112 | - | |
| | 15.7867 | 30500 | 0.0008 | 0.0049 | 0.9123 | - | |
| | 16.0455 | 31000 | 0.0008 | 0.0048 | 0.9133 | - | |
| | 16.3043 | 31500 | 0.0006 | 0.0048 | 0.9103 | - | |
| | 16.5631 | 32000 | 0.0007 | 0.0049 | 0.9144 | - | |
| | 16.8219 | 32500 | 0.0008 | 0.0048 | 0.9143 | - | |
| | 17.0807 | 33000 | 0.0007 | 0.0048 | 0.9159 | - | |
| | 17.3395 | 33500 | 0.0007 | 0.0047 | 0.9174 | - | |
| | 17.5983 | 34000 | 0.0006 | 0.0048 | 0.9175 | - | |
| | 17.8571 | 34500 | 0.0007 | 0.0047 | 0.9163 | - | |
| | 18.1159 | 35000 | 0.0006 | 0.0046 | 0.9195 | - | |
| | 18.3747 | 35500 | 0.0006 | 0.0047 | 0.9190 | - | |
| | 18.6335 | 36000 | 0.0006 | 0.0047 | 0.9192 | - | |
| | 18.8923 | 36500 | 0.0006 | 0.0047 | 0.9204 | - | |
| | 19.1511 | 37000 | 0.0005 | 0.0047 | 0.9219 | - | |
| | 19.4099 | 37500 | 0.0004 | 0.0046 | 0.9218 | - | |
| | 19.6687 | 38000 | 0.0005 | 0.0047 | 0.9221 | - | |
| | 19.9275 | 38500 | 0.0005 | 0.0046 | 0.9230 | - | |
| | 20.1863 | 39000 | 0.0005 | 0.0046 | 0.9233 | - | |
| | 20.4451 | 39500 | 0.0004 | 0.0046 | 0.9240 | - | |
| | 20.7039 | 40000 | 0.0005 | 0.0047 | 0.9234 | - | |
| | 20.9627 | 40500 | 0.0004 | 0.0047 | 0.9241 | - | |
| | 21.2215 | 41000 | 0.0004 | 0.0046 | 0.9253 | - | |
| | 21.4803 | 41500 | 0.0004 | 0.0046 | 0.9259 | - | |
| | 21.7391 | 42000 | 0.0004 | 0.0046 | 0.9262 | - | |
| | **21.9979** | **42500** | **0.0004** | **0.0046** | **0.9263** | **-** | |
| | 22.2567 | 43000 | 0.0003 | 0.0046 | 0.9266 | - | |
| | 22.5155 | 43500 | 0.0003 | 0.0046 | 0.9266 | - | |
| | 22.7743 | 44000 | 0.0003 | 0.0046 | 0.9273 | - | |
| | 23.0331 | 44500 | 0.0003 | 0.0046 | 0.9273 | - | |
| | 23.2919 | 45000 | 0.0003 | 0.0046 | 0.9274 | - | |
| | 23.5507 | 45500 | 0.0003 | 0.0046 | 0.9277 | - | |
| | 23.8095 | 46000 | 0.0003 | 0.0046 | 0.9277 | - | |
| | 24.0 | 46368 | - | - | - | 0.9257 | |
|
|
| * The bold row denotes the saved checkpoint. |
|
|
| ### Framework Versions |
| - Python: 3.10.12 |
| - Sentence Transformers: 3.3.1 |
| - Transformers: 4.47.1 |
| - PyTorch: 2.5.1+cu124 |
| - 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", |
| } |
| ``` |
|
|
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