| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:123640 |
| - loss:CosineSimilarityLoss |
| base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2 |
| widget: |
| - source_sentence: data perempuan dan laki-laki di indonesia 2022 |
| sentences: |
| - Statistik Telekomunikasi Indonesia 2012 |
| - Perkembangan Indeks Produksi Triwulanan Industri Mikro dan Kecil 2023 |
| - Pada Agustus 2014, Jumlah wisman mencapai 826,8 ribu |
| - source_sentence: hasil survei kebutuhan data 2011 di indonesia |
| sentences: |
| - Analisis Survei Kebutuhan Data 2011 |
| - Produk Domestik Bruto Indonesia Triwulanan 2007-2011 |
| - Direktori Perusahaan Air Bersih, Listrik, dan Gas 2022 |
| - source_sentence: komoditas apa yang produksinya naik 3,24 persen pada tahun 2013? |
| sentences: |
| - Indikator Ekonomi Juni 2017 |
| - Produksi jagung naik pada tahun 2013. |
| - Statistik Keuangan Pemerintah Desa 2018 |
| - source_sentence: buku-buku statistik tahun 2007 |
| sentences: |
| - Statistik Keuangan Badan Usaha Milik Negara dan Badan Usaha Milik Daerah 2019 |
| - Statistik Harga Konsumen Perdesaan Kelompok Makanan 2011 |
| - Buletin Statistik Perdagangan Luar Negeri Impor Mei 2019 |
| - source_sentence: analisis kinerja ekspor indonesia feb 2014 |
| sentences: |
| - Kajian Big Data Sinyal Pemulihan Indonesia dari Pandemi Covid-19 |
| - Laporan Bulanan Data Sosial Ekonomi Januari 2019 |
| - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan |
| Negara Februari 2014 |
| 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: 0.9866451272402678 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.9032950863870964 |
| 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: 0.9876833290128094 |
| name: Pearson Cosine |
| - type: spearman_cosine |
| value: 0.9063327700749637 |
| 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") |
| # Run inference |
| sentences = [ |
| 'analisis kinerja ekspor indonesia feb 2014', |
| 'Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara Februari 2014', |
| 'Laporan Bulanan Data Sosial Ekonomi 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 | 0.9866 | 0.9877 | |
| | **spearman_cosine** | **0.9033** | **0.9063** | |
| |
| <!-- |
| ## 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 [d59a245](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1/tree/d59a24585b2ee30e806569dc6a091becd5fcac0c) |
| * Size: 123,640 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: 6 tokens</li><li>mean: 10.64 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.06 tokens</li><li>max: 76 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>Gambaran umum karakteristik usaha di Indonesia</code> | <code>Statistik Karakteristik Usaha 2022/2023</code> | <code>0.9</code> | |
| | <code>Tabel data jumlah sekolah, guru, dan murid MA di bawah Kementerian Agama per provinsi.</code> | <code>Jumlah Sekolah, Guru, dan Murid Madrasah Aliyah (MA) di Bawah Kementerian Agama Menurut Provinsi, tahun ajaran 2005/2006-2015/2016</code> | <code>0.96</code> | |
| | <code>bagaimana kinerja sektor konstruksi indonesia di triwulan ketiga tahun 2008?</code> | <code>Statistik Restoran/Rumah Makan 2007</code> | <code>0.09</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 [d59a245](https://huggingface.co/datasets/yahyaabd/allstats-semantic-synthetic-dataset-v1/tree/d59a24585b2ee30e806569dc6a091becd5fcac0c) |
| * 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: 10.48 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.86 tokens</li><li>max: 58 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>Harga barang konsumsi Indonesia 2022: data per kota</code> | <code>Harga Konsumen Beberapa Barang Kelompok Makanan, Minuman, dan Tembakau 90 Kota di Indonesia 2022</code> | <code>0.92</code> | |
| | <code>data biaya hidup bali 2018</code> | <code>Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara, Maret 2018</code> | <code>0.1</code> | |
| | <code>ekspor barang indonesia november 2011: data lengkap</code> | <code>Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara Februari 2013</code> | <code>0.12</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`: 32 |
| - `per_device_eval_batch_size`: 32 |
| - `num_train_epochs`: 10 |
| - `warmup_ratio`: 0.1 |
| - `fp16`: True |
| - `load_best_model_at_end`: 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`: 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`: 10 |
| - `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`: 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`: 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 |
| | Epoch | Step | Training Loss | Validation Loss | allstats-semantic-base-v1-eval_spearman_cosine | allstat-semantic-base-v1-test_spearman_cosine | |
| |:----------:|:---------:|:-------------:|:---------------:|:----------------------------------------------:|:---------------------------------------------:| |
| | 0.1294 | 500 | 0.0454 | 0.0267 | 0.7374 | - | |
| | 0.2588 | 1000 | 0.0243 | 0.0205 | 0.7527 | - | |
| | 0.3882 | 1500 | 0.0199 | 0.0169 | 0.7720 | - | |
| | 0.5176 | 2000 | 0.0186 | 0.0164 | 0.7733 | - | |
| | 0.6470 | 2500 | 0.0179 | 0.0158 | 0.7806 | - | |
| | 0.7764 | 3000 | 0.0158 | 0.0155 | 0.7826 | - | |
| | 0.9058 | 3500 | 0.0159 | 0.0155 | 0.7771 | - | |
| | 1.0352 | 4000 | 0.0155 | 0.0143 | 0.7847 | - | |
| | 1.1646 | 4500 | 0.0133 | 0.0141 | 0.7935 | - | |
| | 1.2940 | 5000 | 0.0128 | 0.0132 | 0.7986 | - | |
| | 1.4234 | 5500 | 0.0121 | 0.0120 | 0.8148 | - | |
| | 1.5528 | 6000 | 0.012 | 0.0118 | 0.8030 | - | |
| | 1.6822 | 6500 | 0.0118 | 0.0121 | 0.8132 | - | |
| | 1.8116 | 7000 | 0.0119 | 0.0109 | 0.8130 | - | |
| | 1.9410 | 7500 | 0.0107 | 0.0108 | 0.8132 | - | |
| | 2.0704 | 8000 | 0.009 | 0.0098 | 0.8181 | - | |
| | 2.1998 | 8500 | 0.0082 | 0.0099 | 0.8221 | - | |
| | 2.3292 | 9000 | 0.008 | 0.0100 | 0.8221 | - | |
| | 2.4586 | 9500 | 0.008 | 0.0095 | 0.8302 | - | |
| | 2.5880 | 10000 | 0.0083 | 0.0090 | 0.8284 | - | |
| | 2.7174 | 10500 | 0.0084 | 0.0093 | 0.8261 | - | |
| | 2.8468 | 11000 | 0.0084 | 0.0089 | 0.8283 | - | |
| | 2.9762 | 11500 | 0.0083 | 0.0093 | 0.8259 | - | |
| | 3.1056 | 12000 | 0.0056 | 0.0083 | 0.8362 | - | |
| | 3.2350 | 12500 | 0.006 | 0.0081 | 0.8357 | - | |
| | 3.3644 | 13000 | 0.0057 | 0.0078 | 0.8381 | - | |
| | 3.4938 | 13500 | 0.006 | 0.0081 | 0.8399 | - | |
| | 3.6232 | 14000 | 0.0058 | 0.0078 | 0.8420 | - | |
| | 3.7526 | 14500 | 0.0068 | 0.0078 | 0.8303 | - | |
| | 3.8820 | 15000 | 0.0056 | 0.0072 | 0.8502 | - | |
| | 4.0114 | 15500 | 0.0054 | 0.0073 | 0.8483 | - | |
| | 4.1408 | 16000 | 0.004 | 0.0068 | 0.8565 | - | |
| | 4.2702 | 16500 | 0.0042 | 0.0069 | 0.8493 | - | |
| | 4.3996 | 17000 | 0.0043 | 0.0069 | 0.8507 | - | |
| | 4.5290 | 17500 | 0.0045 | 0.0069 | 0.8536 | - | |
| | 4.6584 | 18000 | 0.0042 | 0.0064 | 0.8602 | - | |
| | 4.7878 | 18500 | 0.0043 | 0.0065 | 0.8537 | - | |
| | 4.9172 | 19000 | 0.0039 | 0.0062 | 0.8623 | - | |
| | 5.0466 | 19500 | 0.0041 | 0.0065 | 0.8601 | - | |
| | 5.1760 | 20000 | 0.0032 | 0.0060 | 0.8643 | - | |
| | 5.3054 | 20500 | 0.0032 | 0.0064 | 0.8657 | - | |
| | 5.4348 | 21000 | 0.0032 | 0.0062 | 0.8669 | - | |
| | 5.5642 | 21500 | 0.0031 | 0.0065 | 0.8633 | - | |
| | 5.6936 | 22000 | 0.003 | 0.0059 | 0.8682 | - | |
| | 5.8230 | 22500 | 0.0032 | 0.0057 | 0.8713 | - | |
| | 5.9524 | 23000 | 0.0032 | 0.0057 | 0.8688 | - | |
| | 6.0818 | 23500 | 0.0026 | 0.0055 | 0.8772 | - | |
| | 6.2112 | 24000 | 0.0023 | 0.0056 | 0.8708 | - | |
| | 6.3406 | 24500 | 0.0029 | 0.0056 | 0.8734 | - | |
| | 6.4700 | 25000 | 0.0027 | 0.0054 | 0.8748 | - | |
| | 6.5994 | 25500 | 0.0022 | 0.0054 | 0.8827 | - | |
| | 6.7288 | 26000 | 0.0021 | 0.0053 | 0.8823 | - | |
| | 6.8582 | 26500 | 0.0021 | 0.0053 | 0.8832 | - | |
| | 6.9876 | 27000 | 0.0025 | 0.0052 | 0.8839 | - | |
| | 7.1170 | 27500 | 0.002 | 0.0051 | 0.8887 | - | |
| | 7.2464 | 28000 | 0.0017 | 0.0050 | 0.8869 | - | |
| | 7.3758 | 28500 | 0.0019 | 0.0052 | 0.8845 | - | |
| | 7.5052 | 29000 | 0.0017 | 0.0051 | 0.8897 | - | |
| | 7.6346 | 29500 | 0.0017 | 0.0051 | 0.8920 | - | |
| | 7.7640 | 30000 | 0.0018 | 0.0050 | 0.8889 | - | |
| | 7.8934 | 30500 | 0.0019 | 0.0050 | 0.8931 | - | |
| | 8.0228 | 31000 | 0.002 | 0.0049 | 0.8889 | - | |
| | 8.1522 | 31500 | 0.0014 | 0.0049 | 0.8912 | - | |
| | 8.2816 | 32000 | 0.0013 | 0.0049 | 0.8922 | - | |
| | 8.4110 | 32500 | 0.0014 | 0.0049 | 0.8947 | - | |
| | 8.5404 | 33000 | 0.0014 | 0.0049 | 0.8960 | - | |
| | 8.6698 | 33500 | 0.0014 | 0.0049 | 0.8972 | - | |
| | 8.7992 | 34000 | 0.0014 | 0.0048 | 0.8982 | - | |
| | 8.9286 | 34500 | 0.0013 | 0.0048 | 0.9003 | - | |
| | 9.0580 | 35000 | 0.0014 | 0.0048 | 0.9001 | - | |
| | 9.1874 | 35500 | 0.0012 | 0.0048 | 0.8995 | - | |
| | 9.3168 | 36000 | 0.0011 | 0.0048 | 0.9008 | - | |
| | 9.4462 | 36500 | 0.001 | 0.0047 | 0.9015 | - | |
| | 9.5756 | 37000 | 0.0011 | 0.0047 | 0.9026 | - | |
| | 9.7050 | 37500 | 0.0011 | 0.0047 | 0.9027 | - | |
| | 9.8344 | 38000 | 0.001 | 0.0047 | 0.9035 | - | |
| | **9.9638** | **38500** | **0.0011** | **0.0047** | **0.9033** | **-** | |
| | 10.0 | 38640 | - | - | - | 0.9063 | |
|
|
| * 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", |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
