| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:25580 |
| - loss:OnlineContrastiveLoss |
| base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
| widget: |
| - source_sentence: ikhtisar arus kas triwulan 1, 2004 (miliar) |
| sentences: |
| - Balita (0-59 Bulan) Menurut Status Gizi, Tahun 1998-2005 |
| - Perbandingan Indeks dan Tingkat Inflasi Desember 2023 Kota-kota di Luar Pulau |
| Jawa dan Sumatera dengan Nasional (2018=100) |
| - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan |
| dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Tengah, 2018-2023 |
| - source_sentence: BaIgaimana gambaran neraca arus dana dUi Indonesia pada kuartal |
| kedua tahun 2015? |
| sentences: |
| - Jumlah Sekolah, Guru, dan Murid Sekolah Menengah Pertama (SMP) di Bawah Kementrian |
| Pendidikan dan Kebudayaan Menurut Provinsi 2011/2012-2015/2016 |
| - Ringkasan Neraca Arus Dana Triwulan III Tahun 2003 (Miliar Rupiah) |
| - Rata-rata Konsumsi dan Pengeluaran Perkapita Seminggu Menurut Komoditi Makanan |
| dan Golongan Pengeluaran per Kapita Seminggu di Provinsi Sulawesi Tenggara, 2018-2023 |
| - source_sentence: Berapa persen pengeluaran orang di kotaa untuk makanan vs non-makanan, |
| per provinsi, 2018? |
| sentences: |
| - Ekspor Tanaman Obat, Aromatik, dan Rempah-Rempah menurut Negara Tujuan Utama, |
| 2012-2023 |
| - Rata-rata Pendapatan Bersih Pekerja Bebas Menurut Provinsi dan Pendidikan Tertinggi |
| yang Ditamatkan (ribu rupiah), 2017 |
| - IHK dan Rata-rata Upah per Bulan Buruh Industri di Bawah Mandor (Supervisor), |
| 1996-2014 (1996=100) |
| - source_sentence: Negara-negara asal impor crude oil dan produk turunannya tahun |
| 2002-2023 |
| sentences: |
| - Persentase Pengeluaran Rata-rata per Kapita Sebulan Menurut Kelompok Barang, Indonesia, |
| 1999, 2002-2023 |
| - Rata-rata Pendapatan Bersih Berusaha Sendiri menurut Provinsi dan Pendidikan yang |
| Ditamatkan (ribu rupiah), 2016 |
| - Perkembangan Beberapa Agregat Pendapatan dan Pendapatan per Kapita Atas Dasar |
| Harga Berlaku, 2010-2016 |
| - source_sentence: Arus dana Q3 2006 |
| sentences: |
| - Posisi Simpanan Berjangka Rupiah pada Bank Umum dan BPR Menurut Golongan Pemilik |
| (miliar rupiah), 2005-2018 |
| - Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah) |
| - Rata-Rata Pengeluaran per Kapita Sebulan di Daerah Perkotaan Menurut Kelompok |
| Barang dan Golongan Pengeluaran per Kapita Sebulan, 2000-2012 |
| datasets: |
| - yahyaabd/query-hard-pos-neg-doc-pairs-statictable |
| 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 search multilingual base v1 eval |
| type: allstats-search-multilingual-base-v1-eval |
| metrics: |
| - type: pearson_cosine |
| value: 0.8700002079644513 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.8061513951134361 |
| name: Spearman Cosine |
| - task: |
| type: semantic-similarity |
| name: Semantic Similarity |
| dataset: |
| name: allstats search multilingual base v1 test |
| type: allstats-search-multilingual-base-v1-test |
| metrics: |
| - type: pearson_cosine |
| value: 0.9023194252531408 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.8092675333588865 |
| 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 [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) 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:** |
| - [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) |
| <!-- - **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-search-multilingual-base-v1") |
| # Run inference |
| sentences = [ |
| 'Arus dana Q3 2006', |
| 'Ringkasan Neraca Arus Dana, Triwulan III, 2006, (Miliar Rupiah)', |
| 'Rata-Rata Pengeluaran per Kapita Sebulan di Daerah Perkotaan Menurut Kelompok Barang dan Golongan Pengeluaran per Kapita Sebulan, 2000-2012', |
| ] |
| 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-search-multilingual-base-v1-eval` and `allstats-search-multilingual-base-v1-test` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) |
|
|
| | Metric | allstats-search-multilingual-base-v1-eval | allstats-search-multilingual-base-v1-test | |
| |:--------------------|:------------------------------------------|:------------------------------------------| |
| | pearson_cosine | 0.87 | 0.9023 | |
| | **spearman_cosine** | **0.8062** | **0.8093** | |
| |
| <!-- |
| ## 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 |
| |
| #### query-hard-pos-neg-doc-pairs-statictable |
| |
| * Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [7b28b96](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/7b28b964daa3073a4d012d1ffca46ecd4f26bb5f) |
| * Size: 25,580 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 | int | |
| | details | <ul><li>min: 7 tokens</li><li>mean: 20.14 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 24.9 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>0: ~70.80%</li><li>1: ~29.20%</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:-------------------------------------------------------------------------|:----------------------------------------------|:---------------| |
| | <code>Status pekerjaan utama penduduk usia 15+ yang bekerja, 2020</code> | <code>Jumlah Penghuni Lapas per Kanwil</code> | <code>0</code> | |
| | <code>status pekerjaan utama penduduk usia 15+ yang bekerja, 2020</code> | <code>Jumlah Penghuni Lapas per Kanwil</code> | <code>0</code> | |
| | <code>STATUS PEKERJAAN UTAMA PENDUDUK USIA 15+ YANG BEKERJA, 2020</code> | <code>Jumlah Penghuni Lapas per Kanwil</code> | <code>0</code> | |
| * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) |
| |
| ### Evaluation Dataset |
| |
| #### query-hard-pos-neg-doc-pairs-statictable |
| |
| * Dataset: [query-hard-pos-neg-doc-pairs-statictable](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable) at [7b28b96](https://huggingface.co/datasets/yahyaabd/query-hard-pos-neg-doc-pairs-statictable/tree/7b28b964daa3073a4d012d1ffca46ecd4f26bb5f) |
| * Size: 5,479 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 | int | |
| | details | <ul><li>min: 7 tokens</li><li>mean: 20.78 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 26.28 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~71.50%</li><li>1: ~28.50%</li></ul> | |
| * Samples: |
| | query | doc | label | |
| |:-----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|:---------------| |
| | <code>Bagaimana perbandingan PNS pria dan wanita di berbagai golongan tahun 2014?</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017</code> | <code>0</code> | |
| | <code>bagaimana perbandingan pns pria dan wanita di berbagai golongan tahun 2014?</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017</code> | <code>0</code> | |
| | <code>BAGAIMANA PERBANDINGAN PNS PRIA DAN WANITA DI BERBAGAI GOLONGAN TAHUN 2014?</code> | <code>Rata-rata Pendapatan Bersih Berusaha Sendiri Menurut Provinsi dan Lapangan Pekerjaan Utama (ribu rupiah), 2017</code> | <code>0</code> | |
| * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `eval_strategy`: steps |
| - `per_device_train_batch_size`: 64 |
| - `per_device_eval_batch_size`: 64 |
| - `warmup_ratio`: 0.05 |
| - `fp16`: True |
| - `load_best_model_at_end`: True |
| - `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`: 3 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: {} |
| - `warmup_ratio`: 0.05 |
| - `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`: 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`: 0.0 |
| - `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`: 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-search-multilingual-base-v1-eval_spearman_cosine | allstats-search-multilingual-base-v1-test_spearman_cosine | |
| |:-------:|:-------:|:-------------:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:| |
| | 0 | 0 | - | 1.3012 | 0.7447 | - | |
| | 0.05 | 20 | 0.9548 | 0.3980 | 0.7961 | - | |
| | 0.1 | 40 | 0.3959 | 0.3512 | 0.7993 | - | |
| | 0.15 | 60 | 0.1949 | 0.3102 | 0.8016 | - | |
| | 0.2 | 80 | 0.2126 | 0.4306 | 0.7967 | - | |
| | 0.25 | 100 | 0.2228 | 0.2865 | 0.8026 | - | |
| | 0.3 | 120 | 0.1306 | 0.2476 | 0.8035 | - | |
| | 0.35 | 140 | 0.172 | 0.2592 | 0.8014 | - | |
| | 0.4 | 160 | 0.1619 | 0.2495 | 0.8037 | - | |
| | 0.45 | 180 | 0.1416 | 0.1890 | 0.8046 | - | |
| | 0.5 | 200 | 0.1041 | 0.1717 | 0.8059 | - | |
| | 0.55 | 220 | 0.2145 | 0.2165 | 0.8049 | - | |
| | 0.6 | 240 | 0.0459 | 0.2176 | 0.8036 | - | |
| | 0.65 | 260 | 0.0627 | 0.2670 | 0.8023 | - | |
| | 0.7 | 280 | 0.1132 | 0.2309 | 0.8041 | - | |
| | 0.75 | 300 | 0.1048 | 0.2623 | 0.8028 | - | |
| | 0.8 | 320 | 0.0524 | 0.2328 | 0.8031 | - | |
| | 0.85 | 340 | 0.034 | 0.2580 | 0.8024 | - | |
| | 0.9 | 360 | 0.0664 | 0.2309 | 0.8034 | - | |
| | 0.95 | 380 | 0.0623 | 0.1746 | 0.8053 | - | |
| | 1.0 | 400 | 0.0402 | 0.2126 | 0.8041 | - | |
| | 1.05 | 420 | 0.0459 | 0.1660 | 0.8062 | - | |
| | 1.1 | 440 | 0.0739 | 0.1487 | 0.8068 | - | |
| | 1.15 | 460 | 0.0191 | 0.1595 | 0.8066 | - | |
| | 1.2 | 480 | 0.0073 | 0.1509 | 0.8066 | - | |
| | 1.25 | 500 | 0.0265 | 0.1779 | 0.8062 | - | |
| | 1.3 | 520 | 0.0325 | 0.2646 | 0.8032 | - | |
| | 1.35 | 540 | 0.0536 | 0.2818 | 0.8030 | - | |
| | 1.4 | 560 | 0.0076 | 0.1768 | 0.8057 | - | |
| | 1.45 | 580 | 0.011 | 0.1866 | 0.8054 | - | |
| | 1.5 | 600 | 0.0181 | 0.1726 | 0.8057 | - | |
| | 1.55 | 620 | 0.032 | 0.1881 | 0.8052 | - | |
| | 1.6 | 640 | 0.0459 | 0.1482 | 0.8066 | - | |
| | 1.65 | 660 | 0.041 | 0.1571 | 0.8065 | - | |
| | **1.7** | **680** | **0.0228** | **0.1298** | **0.807** | **-** | |
| | 1.75 | 700 | 0.0275 | 0.1571 | 0.8067 | - | |
| | 1.8 | 720 | 0.0 | 0.1624 | 0.8066 | - | |
| | 1.85 | 740 | 0.0218 | 0.1537 | 0.8068 | - | |
| | 1.9 | 760 | 0.0241 | 0.1699 | 0.8062 | - | |
| | 1.95 | 780 | 0.0065 | 0.1841 | 0.8059 | - | |
| | 2.0 | 800 | 0.0073 | 0.1805 | 0.8061 | - | |
| | 2.05 | 820 | 0.0 | 0.1703 | 0.8064 | - | |
| | 2.1 | 840 | 0.0 | 0.1702 | 0.8064 | - | |
| | 2.15 | 860 | 0.0 | 0.1717 | 0.8064 | - | |
| | 2.2 | 880 | 0.0 | 0.1717 | 0.8064 | - | |
| | 2.25 | 900 | 0.0 | 0.1717 | 0.8064 | - | |
| | 2.3 | 920 | 0.0097 | 0.1875 | 0.8059 | - | |
| | 2.35 | 940 | 0.0148 | 0.1868 | 0.8060 | - | |
| | 2.4 | 960 | 0.0067 | 0.2205 | 0.8051 | - | |
| | 2.45 | 980 | 0.0 | 0.2295 | 0.8049 | - | |
| | 2.5 | 1000 | 0.0154 | 0.2238 | 0.8052 | - | |
| | 2.55 | 1020 | 0.0063 | 0.2125 | 0.8055 | - | |
| | 2.6 | 1040 | 0.0 | 0.2183 | 0.8053 | - | |
| | 2.65 | 1060 | 0.0 | 0.2188 | 0.8053 | - | |
| | 2.7 | 1080 | 0.0068 | 0.2082 | 0.8056 | - | |
| | 2.75 | 1100 | 0.0384 | 0.1770 | 0.8060 | - | |
| | 2.8 | 1120 | 0.0 | 0.1645 | 0.8061 | - | |
| | 2.85 | 1140 | 0.0105 | 0.1613 | 0.8061 | - | |
| | 2.9 | 1160 | 0.0 | 0.1601 | 0.8061 | - | |
| | 2.95 | 1180 | 0.0 | 0.1601 | 0.8062 | - | |
| | 3.0 | 1200 | 0.0 | 0.1601 | 0.8062 | - | |
| | -1 | -1 | - | - | - | 0.8093 | |
|
|
| * The bold row denotes the saved checkpoint. |
|
|
| ### Framework Versions |
| - Python: 3.10.12 |
| - Sentence Transformers: 3.4.0 |
| - Transformers: 4.48.1 |
| - PyTorch: 2.5.1+cu124 |
| - Accelerate: 1.3.0 |
| - 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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