--- language: - id tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:42138 - loss:CosineSimilarityLoss base_model: sentence-transformers/distiluse-base-multilingual-cased-v2 widget: - source_sentence: Informasi importir Indonesia 2014 (Jilid Kedua) sentences: - Indikator Konstruksi Triwulan IV-2011 - Benchmark Indeks Konstruksi (2010=100), 1990-2013 - Statistik Upah Q-2 2002-Q-2 2004 - source_sentence: Direktori Perusahaan Penggiling Padi Aceh 2012 sentences: - Direktori Perusahaan Industri Penggilingan Padi Tahun 2012 Provinsi Aceh - Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut Kelompok Komoditi dan Negara, Agustus 2024 - Statistik Harga Produsen Pertanian Subsektor Tanaman Pangan, Hortikultura, dan Tanaman Perkebunan Rakyat 2022 - source_sentence: Neraca pemerintahan pusat triwulanan 2015-2021:2 sentences: - Tinjauan Regional Berdasarkan PDRB Kabupaten/Kota 2017- 2021, Buku 1 Pulau Sumatera - Statistik Tebu Indonesia 2020 - Indikator Pasar Tenaga Kerja Indonesia Agustus 2011 - source_sentence: Data pembangunan kuartal kedua 2014 sentences: - Katalog Publikasi BPS 2018 - Indikator Konstruksi Triwulan II-2014 - Produk Domestik Regional Bruto Provinsi-provinsi di Indonesia Menurut Penggunaan 2004-2008 - source_sentence: Laporan keuangan pemerintah provinsi periode 2003-2006 sentences: - Statistik Perdagangan Luar Negeri Indonesia Ekspor Menurut Kode ISIC 2013-2014 - Statistik Keuangan Provinsi 2003-2006 - Statistik Industri Manufaktur Indonesia 2013 datasets: - yahyaabd/bps-publication-title-pairs pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on sentence-transformers/distiluse-base-multilingual-cased-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: allstat semantic dev type: allstat-semantic-dev metrics: - type: pearson_cosine value: 0.9659430111615187 name: Pearson Cosine - type: spearman_cosine value: 0.8744991009318857 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: allstat semantic test type: allstat-semantic-test metrics: - type: pearson_cosine value: 0.9645449367522956 name: Pearson Cosine - type: spearman_cosine value: 0.8645918683015844 name: Spearman Cosine --- # SentenceTransformer based on sentence-transformers/distiluse-base-multilingual-cased-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2) on the [bps-publication-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-title-pairs) dataset. It maps sentences & paragraphs to a 512-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/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2) - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 512 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [bps-publication-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-title-pairs) - **Language:** id ### 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: DistilBertModel (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}) (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## 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/f-sts") # Run inference sentences = [ 'Laporan keuangan pemerintah provinsi periode 2003-2006', 'Statistik Keuangan Provinsi 2003-2006', 'Statistik Perdagangan Luar Negeri Indonesia Ekspor Menurut Kode ISIC 2013-2014', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 512] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Datasets: `allstat-semantic-dev` and `allstat-semantic-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | allstat-semantic-dev | allstat-semantic-test | |:--------------------|:---------------------|:----------------------| | pearson_cosine | 0.9659 | 0.9645 | | **spearman_cosine** | **0.8745** | **0.8646** | ## Training Details ### Training Dataset #### bps-publication-title-pairs * Dataset: [bps-publication-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-title-pairs) at [4987e97](https://huggingface.co/datasets/yahyaabd/bps-publication-title-pairs/tree/4987e97a87a10fa40313e6c3efb667ed2c54775d) * Size: 42,138 training samples * Columns: query, doc_title, and score * Approximate statistics based on the first 1000 samples: | | query | doc_title | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | query | doc_title | score | |:---------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:------------------| | Hasil riset mobilitas Jabodetabek tahun 2023 | Statistik Komuter Jabodetabek Hasil Survei Komuter Jabodetabek 2023 | 0.85 | | Indeks harga konsumen di Indonesia tahun 2017 (82 kota) | Harga Konsumen Beberapa Barang dan Jasa Kelompok Sandang di 82 Kota di Indonesia 2017 | 0.15 | | Laporan sektor bangunan Indonesia Q4 2009 | Indikator Konstruksi Triwulan IV Tahun 2009 | 0.91 | * 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 #### bps-publication-title-pairs * Dataset: [bps-publication-title-pairs](https://huggingface.co/datasets/yahyaabd/bps-publication-title-pairs) at [4987e97](https://huggingface.co/datasets/yahyaabd/bps-publication-title-pairs/tree/4987e97a87a10fa40313e6c3efb667ed2c54775d) * Size: 2,634 evaluation samples * Columns: query, doc_title, and score * Approximate statistics based on the first 1000 samples: | | query | doc_title | score | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | query | doc_title | score | |:----------------------------------------------------|:------------------------------------------------------------------|:-----------------| | Statistik tebu Indonesia tahun 2018 | Direktori Perusahaan Perkebunan Karet Indonesia 2018 | 0.1 | | Data industri makanan dan minuman 2017 | Statistik Upah Buruh Tani di Perdesaan 2018 | 0.2 | | Biaya hidup di Gorontalo tahun 2018 | Survei Biaya Hidup (SBH) 2018 Gorontalo | 0.9 | * 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`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 5 - `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`: 16 - `per_device_eval_batch_size`: 16 - `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`: 5 - `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 | allstat-semantic-dev_spearman_cosine | allstat-semantic-test_spearman_cosine | |:------:|:-----:|:-------------:|:---------------:|:------------------------------------:|:-------------------------------------:| | 0.0380 | 100 | 0.0435 | 0.0320 | 0.7989 | - | | 0.0759 | 200 | 0.0287 | 0.0246 | 0.8127 | - | | 0.1139 | 300 | 0.0261 | 0.0222 | 0.8132 | - | | 0.1519 | 400 | 0.0229 | 0.0216 | 0.8096 | - | | 0.1898 | 500 | 0.0228 | 0.0213 | 0.8090 | - | | 0.2278 | 600 | 0.0242 | 0.0210 | 0.8096 | - | | 0.2658 | 700 | 0.0214 | 0.0199 | 0.8143 | - | | 0.3037 | 800 | 0.0204 | 0.0197 | 0.8136 | - | | 0.3417 | 900 | 0.0218 | 0.0202 | 0.8097 | - | | 0.3797 | 1000 | 0.0228 | 0.0206 | 0.8077 | - | | 0.4176 | 1100 | 0.0226 | 0.0192 | 0.8109 | - | | 0.4556 | 1200 | 0.021 | 0.0202 | 0.8059 | - | | 0.4935 | 1300 | 0.0221 | 0.0204 | 0.8053 | - | | 0.5315 | 1400 | 0.0218 | 0.0203 | 0.8070 | - | | 0.5695 | 1500 | 0.0229 | 0.0213 | 0.8071 | - | | 0.6074 | 1600 | 0.0248 | 0.0202 | 0.8125 | - | | 0.6454 | 1700 | 0.0207 | 0.0189 | 0.8116 | - | | 0.6834 | 1800 | 0.0206 | 0.0195 | 0.8106 | - | | 0.7213 | 1900 | 0.0202 | 0.0200 | 0.8117 | - | | 0.7593 | 2000 | 0.0198 | 0.0193 | 0.8163 | - | | 0.7973 | 2100 | 0.0187 | 0.0176 | 0.8204 | - | | 0.8352 | 2200 | 0.0188 | 0.0177 | 0.8192 | - | | 0.8732 | 2300 | 0.0192 | 0.0191 | 0.8167 | - | | 0.9112 | 2400 | 0.0173 | 0.0176 | 0.8188 | - | | 0.9491 | 2500 | 0.0186 | 0.0183 | 0.8212 | - | | 0.9871 | 2600 | 0.0174 | 0.0182 | 0.8243 | - | | 1.0251 | 2700 | 0.0148 | 0.0158 | 0.8255 | - | | 1.0630 | 2800 | 0.0149 | 0.0162 | 0.8216 | - | | 1.1010 | 2900 | 0.0137 | 0.0161 | 0.8273 | - | | 1.1390 | 3000 | 0.0148 | 0.0166 | 0.8233 | - | | 1.1769 | 3100 | 0.0138 | 0.0155 | 0.8251 | - | | 1.2149 | 3200 | 0.0122 | 0.0154 | 0.8320 | - | | 1.2528 | 3300 | 0.0149 | 0.0158 | 0.8293 | - | | 1.2908 | 3400 | 0.0134 | 0.0150 | 0.8314 | - | | 1.3288 | 3500 | 0.0141 | 0.0148 | 0.8292 | - | | 1.3667 | 3600 | 0.0138 | 0.0140 | 0.8337 | - | | 1.4047 | 3700 | 0.0128 | 0.0158 | 0.8256 | - | | 1.4427 | 3800 | 0.0135 | 0.0154 | 0.8284 | - | | 1.4806 | 3900 | 0.0142 | 0.0151 | 0.8376 | - | | 1.5186 | 4000 | 0.0148 | 0.0145 | 0.8308 | - | | 1.5566 | 4100 | 0.013 | 0.0146 | 0.8373 | - | | 1.5945 | 4200 | 0.0137 | 0.0144 | 0.8296 | - | | 1.6325 | 4300 | 0.0126 | 0.0146 | 0.8273 | - | | 1.6705 | 4400 | 0.0138 | 0.0138 | 0.8358 | - | | 1.7084 | 4500 | 0.0141 | 0.0144 | 0.8371 | - | | 1.7464 | 4600 | 0.0127 | 0.0142 | 0.8339 | - | | 1.7844 | 4700 | 0.0124 | 0.0144 | 0.8356 | - | | 1.8223 | 4800 | 0.0126 | 0.0142 | 0.8311 | - | | 1.8603 | 4900 | 0.0145 | 0.0137 | 0.8371 | - | | 1.8983 | 5000 | 0.0125 | 0.0139 | 0.8336 | - | | 1.9362 | 5100 | 0.0137 | 0.0140 | 0.8394 | - | | 1.9742 | 5200 | 0.0127 | 0.0135 | 0.8374 | - | | 2.0121 | 5300 | 0.0111 | 0.0135 | 0.8384 | - | | 2.0501 | 5400 | 0.0086 | 0.0127 | 0.8404 | - | | 2.0881 | 5500 | 0.0089 | 0.0120 | 0.8453 | - | | 2.1260 | 5600 | 0.0091 | 0.0119 | 0.8463 | - | | 2.1640 | 5700 | 0.0094 | 0.0125 | 0.8432 | - | | 2.2020 | 5800 | 0.009 | 0.0126 | 0.8440 | - | | 2.2399 | 5900 | 0.0093 | 0.0120 | 0.8469 | - | | 2.2779 | 6000 | 0.0091 | 0.0124 | 0.8484 | - | | 2.3159 | 6100 | 0.0101 | 0.0119 | 0.8472 | - | | 2.3538 | 6200 | 0.0091 | 0.0125 | 0.8419 | - | | 2.3918 | 6300 | 0.0105 | 0.0125 | 0.8409 | - | | 2.4298 | 6400 | 0.0096 | 0.0125 | 0.8446 | - | | 2.4677 | 6500 | 0.0099 | 0.0120 | 0.8431 | - | | 2.5057 | 6600 | 0.0098 | 0.0124 | 0.8428 | - | | 2.5437 | 6700 | 0.0085 | 0.0120 | 0.8444 | - | | 2.5816 | 6800 | 0.0096 | 0.0120 | 0.8487 | - | | 2.6196 | 6900 | 0.0094 | 0.0127 | 0.8479 | - | | 2.6576 | 7000 | 0.0082 | 0.0116 | 0.8504 | - | | 2.6955 | 7100 | 0.0098 | 0.0115 | 0.8509 | - | | 2.7335 | 7200 | 0.0088 | 0.0114 | 0.8551 | - | | 2.7715 | 7300 | 0.0081 | 0.0112 | 0.8525 | - | | 2.8094 | 7400 | 0.0099 | 0.0114 | 0.8497 | - | | 2.8474 | 7500 | 0.0085 | 0.0116 | 0.8527 | - | | 2.8853 | 7600 | 0.0098 | 0.0115 | 0.8502 | - | | 2.9233 | 7700 | 0.0093 | 0.0118 | 0.8482 | - | | 2.9613 | 7800 | 0.0093 | 0.0117 | 0.8512 | - | | 2.9992 | 7900 | 0.0087 | 0.0117 | 0.8517 | - | | 3.0372 | 8000 | 0.0064 | 0.0106 | 0.8559 | - | | 3.0752 | 8100 | 0.0059 | 0.0107 | 0.8578 | - | | 3.1131 | 8200 | 0.0062 | 0.0106 | 0.8556 | - | | 3.1511 | 8300 | 0.0071 | 0.0107 | 0.8526 | - | | 3.1891 | 8400 | 0.0059 | 0.0106 | 0.8563 | - | | 3.2270 | 8500 | 0.0065 | 0.0105 | 0.8595 | - | | 3.2650 | 8600 | 0.0068 | 0.0105 | 0.8595 | - | | 3.3030 | 8700 | 0.0068 | 0.0105 | 0.8588 | - | | 3.3409 | 8800 | 0.0061 | 0.0103 | 0.8592 | - | | 3.3789 | 8900 | 0.0067 | 0.0103 | 0.8599 | - | | 3.4169 | 9000 | 0.0061 | 0.0102 | 0.8597 | - | | 3.4548 | 9100 | 0.0058 | 0.0106 | 0.8604 | - | | 3.4928 | 9200 | 0.0068 | 0.0103 | 0.8599 | - | | 3.5308 | 9300 | 0.0058 | 0.0099 | 0.8636 | - | | 3.5687 | 9400 | 0.0061 | 0.0100 | 0.8625 | - | | 3.6067 | 9500 | 0.0064 | 0.0105 | 0.8590 | - | | 3.6446 | 9600 | 0.006 | 0.0101 | 0.8590 | - | | 3.6826 | 9700 | 0.0064 | 0.0106 | 0.8590 | - | | 3.7206 | 9800 | 0.0059 | 0.0105 | 0.8600 | - | | 3.7585 | 9900 | 0.0066 | 0.0102 | 0.8635 | - | | 3.7965 | 10000 | 0.0065 | 0.0101 | 0.8617 | - | | 3.8345 | 10100 | 0.006 | 0.0104 | 0.8628 | - | | 3.8724 | 10200 | 0.0063 | 0.0103 | 0.8629 | - | | 3.9104 | 10300 | 0.0064 | 0.0098 | 0.8659 | - | | 3.9484 | 10400 | 0.0063 | 0.0098 | 0.8669 | - | | 3.9863 | 10500 | 0.0062 | 0.0100 | 0.8647 | - | | 4.0243 | 10600 | 0.0052 | 0.0095 | 0.8666 | - | | 4.0623 | 10700 | 0.0045 | 0.0095 | 0.8665 | - | | 4.1002 | 10800 | 0.004 | 0.0097 | 0.8662 | - | | 4.1382 | 10900 | 0.0042 | 0.0095 | 0.8680 | - | | 4.1762 | 11000 | 0.0045 | 0.0096 | 0.8676 | - | | 4.2141 | 11100 | 0.0044 | 0.0096 | 0.8673 | - | | 4.2521 | 11200 | 0.0043 | 0.0097 | 0.8684 | - | | 4.2901 | 11300 | 0.0046 | 0.0094 | 0.8705 | - | | 4.3280 | 11400 | 0.0039 | 0.0095 | 0.8699 | - | | 4.3660 | 11500 | 0.0046 | 0.0094 | 0.8707 | - | | 4.4039 | 11600 | 0.0041 | 0.0094 | 0.8708 | - | | 4.4419 | 11700 | 0.0039 | 0.0093 | 0.8709 | - | | 4.4799 | 11800 | 0.0046 | 0.0092 | 0.8720 | - | | 4.5178 | 11900 | 0.0043 | 0.0093 | 0.8715 | - | | 4.5558 | 12000 | 0.004 | 0.0093 | 0.8726 | - | | 4.5938 | 12100 | 0.0043 | 0.0092 | 0.8729 | - | | 4.6317 | 12200 | 0.0042 | 0.0092 | 0.8734 | - | | 4.6697 | 12300 | 0.004 | 0.0091 | 0.8735 | - | | 4.7077 | 12400 | 0.004 | 0.0092 | 0.8733 | - | | 4.7456 | 12500 | 0.0039 | 0.0090 | 0.8743 | - | | 4.7836 | 12600 | 0.0044 | 0.0090 | 0.8749 | - | | 4.8216 | 12700 | 0.0041 | 0.0089 | 0.8752 | - | | 4.8595 | 12800 | 0.004 | 0.0090 | 0.8746 | - | | 4.8975 | 12900 | 0.004 | 0.0090 | 0.8744 | - | | 4.9355 | 13000 | 0.0041 | 0.0090 | 0.8746 | - | | 4.9734 | 13100 | 0.0039 | 0.0090 | 0.8745 | - | | 5.0 | 13170 | - | - | - | 0.8646 |
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.5.1+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", } ```