| --- |
| 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 <sup>1</sup> (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: 0.9958745183830993 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.96406478662103 |
| 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: 0.9960950217535739 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.9647914507837114 |
| 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) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 --> |
| - **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) |
| <!-- - **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-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] |
| ``` |
|
|
| <!-- |
| ### 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-search-v1-3-dev` and `allstat-semantic-search-v1-3-test` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](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 | 0.9959 | 0.9961 | |
| | **spearman_cosine** | **0.9641** | **0.9648** | |
| |
| <!-- |
| ## 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-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: <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: 11.46 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 14.47 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.05</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:---------------------------------------------------------------|:-----------------------------------------------------------------------|:------------------| |
| | <code>aDta industri besar dan sedang Indonesia 2008</code> | <code>Statistik Industri Besar dan Sedang Indonesia 2008</code> | <code>0.9</code> | |
| | <code>profil bisnis konstruksi individu jawa barat 2022</code> | <code>Statistik Industri Manufaktur Indonesia 2015 - Bahan Baku</code> | <code>0.15</code> | |
| | <code>data statistik ekonomi indonesia</code> | <code>Nilai Tukar Valuta Asing di Indonesia 2014</code> | <code>0.08</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-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: <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: 11.38 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.63 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:-------------------------------------------------------------------|:---------------------------------------------------------------------------|:------------------| |
| | <code>tahun berapa ekspor naik 2,37% dan impor naik 30,30%?</code> | <code>Bulan November 2006 Ekspor Naik 2,37 % dan Impor Naik 30,30 %</code> | <code>1.0</code> | |
| | <code>Berapa produksi padi pada tahun 2023?</code> | <code>Produksi padi tahun lainnya</code> | <code>0.0</code> | |
| | <code>data statistik solus per aqua 2015</code> | <code>Statistik Solus Per Aqua (SPA) 2015</code> | <code>0.97</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`: 16 |
| - `warmup_ratio`: 0.1 |
| - `fp16`: 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`: 16 |
| - `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 |
|
|
| </details> |
|
|
| ### Training Logs |
| <details><summary>Click to expand</summary> |
|
|
| | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-search-v1-3-dev_spearman_cosine | allstat-semantic-search-v1-3-test_spearman_cosine | |
| |:-------:|:-----:|:-------------:|:---------------:|:-------------------------------------------------:|:-------------------------------------------------:| |
| | 0.1502 | 500 | 0.0579 | 0.0351 | 0.7132 | - | |
| | 0.3005 | 1000 | 0.03 | 0.0225 | 0.7589 | - | |
| | 0.4507 | 1500 | 0.0219 | 0.0185 | 0.7834 | - | |
| | 0.6010 | 2000 | 0.0181 | 0.0163 | 0.7946 | - | |
| | 0.7512 | 2500 | 0.0162 | 0.0147 | 0.7941 | - | |
| | 0.9014 | 3000 | 0.015 | 0.0147 | 0.8050 | - | |
| | 1.0517 | 3500 | 0.014 | 0.0131 | 0.7946 | - | |
| | 1.2019 | 4000 | 0.0119 | 0.0126 | 0.8038 | - | |
| | 1.3522 | 4500 | 0.0121 | 0.0128 | 0.8213 | - | |
| | 1.5024 | 5000 | 0.0117 | 0.0116 | 0.8268 | - | |
| | 1.6526 | 5500 | 0.0124 | 0.0117 | 0.8269 | - | |
| | 1.8029 | 6000 | 0.0111 | 0.0109 | 0.8421 | - | |
| | 1.9531 | 6500 | 0.0105 | 0.0108 | 0.8278 | - | |
| | 2.1034 | 7000 | 0.0091 | 0.0093 | 0.8460 | - | |
| | 2.2536 | 7500 | 0.0085 | 0.0091 | 0.8469 | - | |
| | 2.4038 | 8000 | 0.0079 | 0.0083 | 0.8595 | - | |
| | 2.5541 | 8500 | 0.0075 | 0.0085 | 0.8495 | - | |
| | 2.7043 | 9000 | 0.0073 | 0.0082 | 0.8614 | - | |
| | 2.8546 | 9500 | 0.0068 | 0.0077 | 0.8696 | - | |
| | 3.0048 | 10000 | 0.0066 | 0.0076 | 0.8669 | - | |
| | 3.1550 | 10500 | 0.0058 | 0.0072 | 0.8678 | - | |
| | 3.3053 | 11000 | 0.0056 | 0.0067 | 0.8703 | - | |
| | 3.4555 | 11500 | 0.0054 | 0.0067 | 0.8766 | - | |
| | 3.6058 | 12000 | 0.0054 | 0.0063 | 0.8678 | - | |
| | 3.7560 | 12500 | 0.0051 | 0.0061 | 0.8786 | - | |
| | 3.9062 | 13000 | 0.0052 | 0.0077 | 0.8699 | - | |
| | 4.0565 | 13500 | 0.005 | 0.0055 | 0.8859 | - | |
| | 4.2067 | 14000 | 0.0041 | 0.0054 | 0.8900 | - | |
| | 4.3570 | 14500 | 0.0038 | 0.0052 | 0.8892 | - | |
| | 4.5072 | 15000 | 0.0039 | 0.0050 | 0.8895 | - | |
| | 4.6575 | 15500 | 0.004 | 0.0052 | 0.8972 | - | |
| | 4.8077 | 16000 | 0.0042 | 0.0051 | 0.8927 | - | |
| | 4.9579 | 16500 | 0.0041 | 0.0052 | 0.8930 | - | |
| | 5.1082 | 17000 | 0.0034 | 0.0053 | 0.8998 | - | |
| | 5.2584 | 17500 | 0.003 | 0.0047 | 0.9023 | - | |
| | 5.4087 | 18000 | 0.0032 | 0.0045 | 0.9039 | - | |
| | 5.5589 | 18500 | 0.0032 | 0.0044 | 0.8996 | - | |
| | 5.7091 | 19000 | 0.0032 | 0.0041 | 0.9085 | - | |
| | 5.8594 | 19500 | 0.0032 | 0.0047 | 0.9072 | - | |
| | 6.0096 | 20000 | 0.0029 | 0.0037 | 0.9104 | - | |
| | 6.1599 | 20500 | 0.0024 | 0.0037 | 0.9112 | - | |
| | 6.3101 | 21000 | 0.0026 | 0.0039 | 0.9112 | - | |
| | 6.4603 | 21500 | 0.0024 | 0.0037 | 0.9157 | - | |
| | 6.6106 | 22000 | 0.0022 | 0.0038 | 0.9122 | - | |
| | 6.7608 | 22500 | 0.0025 | 0.0034 | 0.9170 | - | |
| | 6.9111 | 23000 | 0.0023 | 0.0034 | 0.9179 | - | |
| | 7.0613 | 23500 | 0.002 | 0.0031 | 0.9244 | - | |
| | 7.2115 | 24000 | 0.0019 | 0.0030 | 0.9250 | - | |
| | 7.3618 | 24500 | 0.0018 | 0.0032 | 0.9249 | - | |
| | 7.5120 | 25000 | 0.0022 | 0.0031 | 0.9162 | - | |
| | 7.6623 | 25500 | 0.0019 | 0.0030 | 0.9266 | - | |
| | 7.8125 | 26000 | 0.0019 | 0.0028 | 0.9297 | - | |
| | 7.9627 | 26500 | 0.0018 | 0.0028 | 0.9282 | - | |
| | 8.1130 | 27000 | 0.0015 | 0.0025 | 0.9324 | - | |
| | 8.2632 | 27500 | 0.0014 | 0.0027 | 0.9337 | - | |
| | 8.4135 | 28000 | 0.0015 | 0.0027 | 0.9327 | - | |
| | 8.5637 | 28500 | 0.0016 | 0.0027 | 0.9313 | - | |
| | 8.7139 | 29000 | 0.0016 | 0.0027 | 0.9333 | - | |
| | 8.8642 | 29500 | 0.0015 | 0.0025 | 0.9382 | - | |
| | 9.0144 | 30000 | 0.0014 | 0.0025 | 0.9375 | - | |
| | 9.1647 | 30500 | 0.0011 | 0.0024 | 0.9398 | - | |
| | 9.3149 | 31000 | 0.0012 | 0.0025 | 0.9384 | - | |
| | 9.4651 | 31500 | 0.0014 | 0.0025 | 0.9383 | - | |
| | 9.6154 | 32000 | 0.0013 | 0.0023 | 0.9410 | - | |
| | 9.7656 | 32500 | 0.0011 | 0.0023 | 0.9409 | - | |
| | 9.9159 | 33000 | 0.0012 | 0.0021 | 0.9432 | - | |
| | 10.0661 | 33500 | 0.0011 | 0.0021 | 0.9432 | - | |
| | 10.2163 | 34000 | 0.001 | 0.0021 | 0.9442 | - | |
| | 10.3666 | 34500 | 0.0009 | 0.0022 | 0.9436 | - | |
| | 10.5168 | 35000 | 0.001 | 0.0021 | 0.9468 | - | |
| | 10.6671 | 35500 | 0.001 | 0.0020 | 0.9471 | - | |
| | 10.8173 | 36000 | 0.001 | 0.0021 | 0.9467 | - | |
| | 10.9675 | 36500 | 0.0011 | 0.0021 | 0.9478 | - | |
| | 11.1178 | 37000 | 0.0008 | 0.0020 | 0.9493 | - | |
| | 11.2680 | 37500 | 0.0008 | 0.0019 | 0.9509 | - | |
| | 11.4183 | 38000 | 0.0008 | 0.0019 | 0.9504 | - | |
| | 11.5685 | 38500 | 0.0008 | 0.0019 | 0.9512 | - | |
| | 11.7188 | 39000 | 0.0008 | 0.0019 | 0.9516 | - | |
| | 11.8690 | 39500 | 0.0007 | 0.0019 | 0.9534 | - | |
| | 12.0192 | 40000 | 0.0007 | 0.0018 | 0.9539 | - | |
| | 12.1695 | 40500 | 0.0006 | 0.0018 | 0.9555 | - | |
| | 12.3197 | 41000 | 0.0006 | 0.0019 | 0.9551 | - | |
| | 12.4700 | 41500 | 0.0007 | 0.0019 | 0.9550 | - | |
| | 12.6202 | 42000 | 0.0008 | 0.0018 | 0.9552 | - | |
| | 12.7704 | 42500 | 0.0006 | 0.0017 | 0.9559 | - | |
| | 12.9207 | 43000 | 0.0006 | 0.0017 | 0.9568 | - | |
| | 13.0709 | 43500 | 0.0006 | 0.0017 | 0.9577 | - | |
| | 13.2212 | 44000 | 0.0005 | 0.0017 | 0.9581 | - | |
| | 13.3714 | 44500 | 0.0006 | 0.0017 | 0.9586 | - | |
| | 13.5216 | 45000 | 0.0005 | 0.0017 | 0.9587 | - | |
| | 13.6719 | 45500 | 0.0005 | 0.0017 | 0.9591 | - | |
| | 13.8221 | 46000 | 0.0006 | 0.0016 | 0.9600 | - | |
| | 13.9724 | 46500 | 0.0005 | 0.0016 | 0.9603 | - | |
| | 14.1226 | 47000 | 0.0005 | 0.0016 | 0.9609 | - | |
| | 14.2728 | 47500 | 0.0005 | 0.0016 | 0.9612 | - | |
| | 14.4231 | 48000 | 0.0005 | 0.0016 | 0.9611 | - | |
| | 14.5733 | 48500 | 0.0005 | 0.0016 | 0.9616 | - | |
| | 14.7236 | 49000 | 0.0004 | 0.0015 | 0.9625 | - | |
| | 14.8738 | 49500 | 0.0004 | 0.0016 | 0.9628 | - | |
| | 15.0240 | 50000 | 0.0004 | 0.0016 | 0.9631 | - | |
| | 15.1743 | 50500 | 0.0004 | 0.0016 | 0.9632 | - | |
| | 15.3245 | 51000 | 0.0004 | 0.0016 | 0.9633 | - | |
| | 15.4748 | 51500 | 0.0004 | 0.0016 | 0.9635 | - | |
| | 15.625 | 52000 | 0.0004 | 0.0015 | 0.9638 | - | |
| | 15.7752 | 52500 | 0.0004 | 0.0015 | 0.9640 | - | |
| | 15.9255 | 53000 | 0.0004 | 0.0015 | 0.9641 | - | |
| | 16.0 | 53248 | - | - | - | 0.9648 | |
|
|
| </details> |
|
|
| ### 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", |
| } |
| ``` |
|
|
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