--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:10330 - loss:MultipleNegativesRankingLoss base_model: indobenchmark/indobert-base-p2 datasets: [] metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max pipeline_tag: sentence-similarity model-index: - name: SentenceTransformer based on indobenchmark/indobert-base-p2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: -0.0979039836743928 name: Pearson Cosine - type: spearman_cosine value: -0.10370853946172742 name: Spearman Cosine - type: pearson_manhattan value: -0.0986716229567464 name: Pearson Manhattan - type: spearman_manhattan value: -0.10051590980192249 name: Spearman Manhattan - type: pearson_euclidean value: -0.09806801008727767 name: Pearson Euclidean - type: spearman_euclidean value: -0.09978077307233649 name: Spearman Euclidean - type: pearson_dot value: -0.08215757856369725 name: Pearson Dot - type: spearman_dot value: -0.08205505573726227 name: Spearman Dot - type: pearson_max value: -0.08215757856369725 name: Pearson Max - type: spearman_max value: -0.08205505573726227 name: Spearman Max - type: pearson_cosine value: -0.02784985879772803 name: Pearson Cosine - type: spearman_cosine value: -0.03497736614462515 name: Spearman Cosine - type: pearson_manhattan value: -0.03551617173397621 name: Pearson Manhattan - type: spearman_manhattan value: -0.03865758617690966 name: Spearman Manhattan - type: pearson_euclidean value: -0.0355939001168591 name: Pearson Euclidean - type: spearman_euclidean value: -0.03886934284409788 name: Spearman Euclidean - type: pearson_dot value: -0.009209251203106355 name: Pearson Dot - type: spearman_dot value: -0.006641745341724743 name: Spearman Dot - type: pearson_max value: -0.009209251203106355 name: Pearson Max - type: spearman_max value: -0.006641745341724743 name: Spearman Max --- # SentenceTransformer based on indobenchmark/indobert-base-p2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2). 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:** [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) - **Maximum Sequence Length:** 200 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity ### 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': 200, 'do_lower_case': False}) with Transformer model: BertModel (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("sentence_transformers_model_id") # Run inference sentences = [ 'Penduduk kabupaten Raja Ampat mayoritas memeluk agama Kristen.', 'Masyarakat kabupaten Raja Ampat mayoritas memeluk agama Islam.', 'Gereja Baptis biasanya cenderung membentuk kelompok sendiri.', ] 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] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:-------------------|:------------| | pearson_cosine | -0.0979 | | spearman_cosine | -0.1037 | | pearson_manhattan | -0.0987 | | spearman_manhattan | -0.1005 | | pearson_euclidean | -0.0981 | | spearman_euclidean | -0.0998 | | pearson_dot | -0.0822 | | spearman_dot | -0.0821 | | pearson_max | -0.0822 | | **spearman_max** | **-0.0821** | #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:-------------------|:------------| | pearson_cosine | -0.0278 | | spearman_cosine | -0.035 | | pearson_manhattan | -0.0355 | | spearman_manhattan | -0.0387 | | pearson_euclidean | -0.0356 | | spearman_euclidean | -0.0389 | | pearson_dot | -0.0092 | | spearman_dot | -0.0066 | | pearson_max | -0.0092 | | **spearman_max** | **-0.0066** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 10,330 training samples * Columns: sentence_0, sentence_1, and label * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | label | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:-----------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------| | Ini adalah coup de grâce dan dorongan yang dibutuhkan oleh para pendatang untuk mendapatkan kemerdekaan mereka. | Pendatang tidak mendapatkan kemerdekaan. | 2 | | Dua bayi almarhum Raja, Diana dan Suharna, diculik. | Jumlah bayi raja yang diculik sudah mencapai 2 bayi. | 1 | | Sebuah penelitian menunjukkan bahwa mengkonsumsi makanan yang tinggi kadar gulanya bisa meningkatkan rasa haus. | Tidak ada penelitian yang bertopik makanan yang kadar gulanya tinggi. | 2 | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `num_train_epochs`: 20 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_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 - `num_train_epochs`: 20 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `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`: False - `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`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `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 - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs
Click to expand | Epoch | Step | Training Loss | sts-dev_spearman_max | |:-------:|:-----:|:-------------:|:--------------------:| | 0.0998 | 129 | - | -0.0821 | | 0.0999 | 258 | - | -0.0541 | | 0.1936 | 500 | 0.0322 | - | | 0.1998 | 516 | - | -0.0474 | | 0.2997 | 774 | - | -0.0369 | | 0.3871 | 1000 | 0.0157 | - | | 0.3995 | 1032 | - | -0.0371 | | 0.4994 | 1290 | - | -0.0388 | | 0.5807 | 1500 | 0.0109 | - | | 0.5993 | 1548 | - | -0.0284 | | 0.6992 | 1806 | - | -0.0293 | | 0.7743 | 2000 | 0.0112 | - | | 0.7991 | 2064 | - | -0.0176 | | 0.8990 | 2322 | - | -0.0290 | | 0.9679 | 2500 | 0.0104 | - | | 0.9988 | 2580 | - | -0.0128 | | 1.0 | 2583 | - | -0.0123 | | 1.0987 | 2838 | - | -0.0200 | | 1.1614 | 3000 | 0.0091 | - | | 1.1986 | 3096 | - | -0.0202 | | 1.2985 | 3354 | - | -0.0204 | | 1.3550 | 3500 | 0.0052 | - | | 1.3984 | 3612 | - | -0.0231 | | 1.4983 | 3870 | - | -0.0312 | | 1.5486 | 4000 | 0.0017 | - | | 1.5981 | 4128 | - | -0.0277 | | 1.6980 | 4386 | - | -0.0366 | | 1.7422 | 4500 | 0.0054 | - | | 1.7979 | 4644 | - | -0.0192 | | 1.8978 | 4902 | - | -0.0224 | | 1.9357 | 5000 | 0.0048 | - | | 1.9977 | 5160 | - | -0.0240 | | 2.0 | 5166 | - | -0.0248 | | 2.0976 | 5418 | - | -0.0374 | | 2.1293 | 5500 | 0.0045 | - | | 2.1974 | 5676 | - | -0.0215 | | 2.2973 | 5934 | - | -0.0329 | | 2.3229 | 6000 | 0.0047 | - | | 2.3972 | 6192 | - | -0.0284 | | 2.4971 | 6450 | - | -0.0370 | | 2.5165 | 6500 | 0.0037 | - | | 2.5970 | 6708 | - | -0.0390 | | 2.6969 | 6966 | - | -0.0681 | | 2.7100 | 7000 | 0.0128 | - | | 2.7967 | 7224 | - | -0.0343 | | 2.8966 | 7482 | - | -0.0413 | | 2.9036 | 7500 | 0.0055 | - | | 2.9965 | 7740 | - | -0.0416 | | 3.0 | 7749 | - | -0.0373 | | 3.0964 | 7998 | - | -0.0630 | | 3.0972 | 8000 | 0.0016 | - | | 3.1963 | 8256 | - | -0.0401 | | 3.2907 | 8500 | 0.0018 | - | | 3.2962 | 8514 | - | -0.0303 | | 3.3961 | 8772 | - | -0.0484 | | 3.4843 | 9000 | 0.0017 | - | | 3.4959 | 9030 | - | -0.0619 | | 3.5958 | 9288 | - | -0.0411 | | 3.6779 | 9500 | 0.007 | - | | 3.6957 | 9546 | - | -0.0408 | | 3.7956 | 9804 | - | -0.0368 | | 3.8715 | 10000 | 0.0029 | - | | 3.8955 | 10062 | - | -0.0429 | | 3.9954 | 10320 | - | -0.0526 | | 4.0 | 10332 | - | -0.0494 | | 4.0650 | 10500 | 0.0004 | - | | 4.0952 | 10578 | - | -0.0385 | | 4.1951 | 10836 | - | -0.0467 | | 4.2586 | 11000 | 0.0004 | - | | 4.2950 | 11094 | - | -0.0500 | | 4.3949 | 11352 | - | -0.0458 | | 4.4522 | 11500 | 0.0011 | - | | 4.4948 | 11610 | - | -0.0389 | | 4.5947 | 11868 | - | -0.0401 | | 4.6458 | 12000 | 0.0046 | - | | 4.6945 | 12126 | - | -0.0370 | | 4.7944 | 12384 | - | -0.0495 | | 4.8393 | 12500 | 0.0104 | - | | 4.8943 | 12642 | - | -0.0504 | | 4.9942 | 12900 | - | -0.0377 | | 5.0 | 12915 | - | -0.0379 | | 5.0329 | 13000 | 0.0005 | - | | 5.0941 | 13158 | - | -0.0617 | | 5.1940 | 13416 | - | -0.0354 | | 5.2265 | 13500 | 0.0006 | - | | 5.2938 | 13674 | - | -0.0514 | | 5.3937 | 13932 | - | -0.0615 | | 5.4201 | 14000 | 0.0014 | - | | 5.4936 | 14190 | - | -0.0574 | | 5.5935 | 14448 | - | -0.0503 | | 5.6136 | 14500 | 0.0025 | - | | 5.6934 | 14706 | - | -0.0512 | | 5.7933 | 14964 | - | -0.0316 | | 5.8072 | 15000 | 0.0029 | - | | 5.8931 | 15222 | - | -0.0475 | | 5.9930 | 15480 | - | -0.0429 | | 6.0 | 15498 | - | -0.0377 | | 6.0008 | 15500 | 0.0003 | - | | 6.0929 | 15738 | - | -0.0486 | | 6.1928 | 15996 | - | -0.0512 | | 6.1943 | 16000 | 0.0002 | - | | 6.2927 | 16254 | - | -0.0383 | | 6.3879 | 16500 | 0.0017 | - | | 6.3926 | 16512 | - | -0.0460 | | 6.4925 | 16770 | - | -0.0439 | | 6.5815 | 17000 | 0.0046 | - | | 6.5923 | 17028 | - | -0.0378 | | 6.6922 | 17286 | - | -0.0289 | | 6.7751 | 17500 | 0.0081 | - | | 6.7921 | 17544 | - | -0.0415 | | 6.8920 | 17802 | - | -0.0451 | | 6.9686 | 18000 | 0.0021 | - | | 6.9919 | 18060 | - | -0.0386 | | 7.0 | 18081 | - | -0.0390 | | 7.0918 | 18318 | - | -0.0460 | | 7.1622 | 18500 | 0.0001 | - | | 7.1916 | 18576 | - | -0.0510 | | 7.2915 | 18834 | - | -0.0566 | | 7.3558 | 19000 | 0.0009 | - | | 7.3914 | 19092 | - | -0.0479 | | 7.4913 | 19350 | - | -0.0456 | | 7.5494 | 19500 | 0.0019 | - | | 7.5912 | 19608 | - | -0.0371 | | 7.6911 | 19866 | - | -0.0184 | | 7.7429 | 20000 | 0.003 | - | | 7.7909 | 20124 | - | -0.0312 | | 7.8908 | 20382 | - | -0.0307 | | 7.9365 | 20500 | 0.0008 | - | | 7.9907 | 20640 | - | -0.0291 | | 8.0 | 20664 | - | -0.0298 | | 8.0906 | 20898 | - | -0.0452 | | 8.1301 | 21000 | 0.0001 | - | | 8.1905 | 21156 | - | -0.0405 | | 8.2904 | 21414 | - | -0.0417 | | 8.3237 | 21500 | 0.0007 | - | | 8.3902 | 21672 | - | -0.0430 | | 8.4901 | 21930 | - | -0.0487 | | 8.5172 | 22000 | 0.0 | - | | 8.5900 | 22188 | - | -0.0471 | | 8.6899 | 22446 | - | -0.0361 | | 8.7108 | 22500 | 0.0037 | - | | 8.7898 | 22704 | - | -0.0443 | | 8.8897 | 22962 | - | -0.0404 | | 8.9044 | 23000 | 0.0009 | - | | 8.9895 | 23220 | - | -0.0421 | | 9.0 | 23247 | - | -0.0425 | | 9.0894 | 23478 | - | -0.0451 | | 9.0979 | 23500 | 0.0001 | - | | 9.1893 | 23736 | - | -0.0458 | | 9.2892 | 23994 | - | -0.0479 | | 9.2915 | 24000 | 0.0 | - | | 9.3891 | 24252 | - | -0.0400 | | 9.4851 | 24500 | 0.0014 | - | | 9.4890 | 24510 | - | -0.0374 | | 9.5889 | 24768 | - | -0.0454 | | 9.6787 | 25000 | 0.0075 | - | | 9.6887 | 25026 | - | -0.0230 | | 9.7886 | 25284 | - | -0.0345 | | 9.8722 | 25500 | 0.0007 | - | | 9.8885 | 25542 | - | -0.0301 | | 9.9884 | 25800 | - | -0.0363 | | 10.0 | 25830 | - | -0.0375 | | 10.0658 | 26000 | 0.0001 | - | | 10.0883 | 26058 | - | -0.0381 | | 10.1882 | 26316 | - | -0.0386 | | 10.2594 | 26500 | 0.0 | - | | 10.2880 | 26574 | - | -0.0390 | | 10.3879 | 26832 | - | -0.0366 | | 10.4530 | 27000 | 0.0007 | - | | 10.4878 | 27090 | - | -0.0464 | | 10.5877 | 27348 | - | -0.0509 | | 10.6465 | 27500 | 0.0021 | - | | 10.6876 | 27606 | - | -0.0292 | | 10.7875 | 27864 | - | -0.0514 | | 10.8401 | 28000 | 0.0017 | - | | 10.8873 | 28122 | - | -0.0485 | | 10.9872 | 28380 | - | -0.0471 | | 11.0 | 28413 | - | -0.0468 | | 11.0337 | 28500 | 0.0 | - | | 11.0871 | 28638 | - | -0.0460 | | 11.1870 | 28896 | - | -0.0450 | | 11.2273 | 29000 | 0.0 | - | | 11.2869 | 29154 | - | -0.0457 | | 11.3868 | 29412 | - | -0.0450 | | 11.4208 | 29500 | 0.0008 | - | | 11.4866 | 29670 | - | -0.0440 | | 11.5865 | 29928 | - | -0.0384 | | 11.6144 | 30000 | 0.0028 | - | | 11.6864 | 30186 | - | -0.0066 |
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.3.0+cu121 - Accelerate: 0.31.0 - Datasets: 2.19.2 - Tokenizers: 0.19.1 ## 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", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```