--- base_model: indobenchmark/indobert-base-p1 datasets: [] language: [] library_name: sentence-transformers 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 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:12000 - loss:MultipleNegativesRankingLoss widget: - source_sentence: Awalnya merupakan singkatan dari John's Macintosh Project. sentences: - Sebuah formasi yang terdiri dari sekitar 50 petugas Polisi Baltimore akhirnya menempatkan diri mereka di antara para perusuh dan milisi, memungkinkan Massachusetts ke-6 untuk melanjutkan ke Stasiun Camden. - Mengecat luka dapat melindungi dari jamur dan hama. - Dulunya merupakan singkatan dari John's Macintosh Project. - source_sentence: Boueiz berprofesi sebagai pengacara. sentences: - Mereka juga gagal mengembangkan Water Cooperation Quotient yang baru. - Pada Pemilu 1970, ia ikut serta dari Partai Persatuan Nasional namun dikalahkan. - Seorang pengacara berprofesi sebagai Boueiz. - source_sentence: Fakultas Studi Oriental memiliki seorang profesor. sentences: - Di tempat lain di New Mexico, LAHS terkadang dianggap sebagai sekolah untuk orang kaya. - Laporan lain juga menunjukkan kandungannya lebih rendah dari 0,1% di Australia. - Profesor tersebut merupakan bagian dari Fakultas Studi Oriental. - source_sentence: Hal ini terjadi di sejumlah negara, termasuk Ethiopia, Republik Demokratik Kongo, dan Afrika Selatan. sentences: - Hal ini diketahui terjadi di Eritrea, Ethiopia, Kongo, Tanzania, Namibia dan Afrika Selatan. - Gugus amil digantikan oleh gugus pentil. - Dan saya beritahu Anda sesuatu, itu tidak adil. - source_sentence: Ini adalah wilayah sosial-ekonomi yang lebih rendah. sentences: - Ini adalah bengkel perbaikan mobil terbaru yang masih beroperasi di kota. - Zelinsky hanya berteori bahwa tidak ada tiga bilangan bulat berurutan yang semuanya dapat difaktorkan ulang. - Ini adalah wilayah sosial-ekonomi yang lebih tinggi. model-index: - name: SentenceTransformer based on indobenchmark/indobert-base-p1 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: str dev type: str-dev metrics: - type: pearson_cosine value: 0.4564569322733096 name: Pearson Cosine - type: spearman_cosine value: 0.48195228779003385 name: Spearman Cosine - type: pearson_manhattan value: 0.5026090402544289 name: Pearson Manhattan - type: spearman_manhattan value: 0.4959933098737397 name: Spearman Manhattan - type: pearson_euclidean value: 0.5039005057105697 name: Pearson Euclidean - type: spearman_euclidean value: 0.4974503970711054 name: Spearman Euclidean - type: pearson_dot value: 0.30898798759416635 name: Pearson Dot - type: spearman_dot value: 0.2877933490149207 name: Spearman Dot - type: pearson_max value: 0.5039005057105697 name: Pearson Max - type: spearman_max value: 0.4974503970711054 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: str test type: str-test metrics: - type: pearson_cosine value: 0.47784323630714065 name: Pearson Cosine - type: spearman_cosine value: 0.5031401179671358 name: Spearman Cosine - type: pearson_manhattan value: 0.5002126701994709 name: Pearson Manhattan - type: spearman_manhattan value: 0.49583761101885343 name: Spearman Manhattan - type: pearson_euclidean value: 0.5003980651640989 name: Pearson Euclidean - type: spearman_euclidean value: 0.49610725867890976 name: Spearman Euclidean - type: pearson_dot value: 0.3399664664461248 name: Pearson Dot - type: spearman_dot value: 0.3339252012184323 name: Spearman Dot - type: pearson_max value: 0.5003980651640989 name: Pearson Max - type: spearman_max value: 0.5031401179671358 name: Spearman Max --- # SentenceTransformer based on indobenchmark/indobert-base-p1 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1). 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-p1](https://huggingface.co/indobenchmark/indobert-base-p1) - **Maximum Sequence Length:** 32 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': 32, '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("damand2061/negasibert-mnrl") # Run inference sentences = [ 'Ini adalah wilayah sosial-ekonomi yang lebih rendah.', 'Ini adalah wilayah sosial-ekonomi yang lebih tinggi.', 'Zelinsky hanya berteori bahwa tidak ada tiga bilangan bulat berurutan yang semuanya dapat difaktorkan ulang.', ] 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: `str-dev` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:-------------------|:-----------| | pearson_cosine | 0.4565 | | spearman_cosine | 0.482 | | pearson_manhattan | 0.5026 | | spearman_manhattan | 0.496 | | pearson_euclidean | 0.5039 | | spearman_euclidean | 0.4975 | | pearson_dot | 0.309 | | spearman_dot | 0.2878 | | pearson_max | 0.5039 | | **spearman_max** | **0.4975** | #### Semantic Similarity * Dataset: `str-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:-------------------|:-----------| | pearson_cosine | 0.4778 | | spearman_cosine | 0.5031 | | pearson_manhattan | 0.5002 | | spearman_manhattan | 0.4958 | | pearson_euclidean | 0.5004 | | spearman_euclidean | 0.4961 | | pearson_dot | 0.34 | | spearman_dot | 0.3339 | | pearson_max | 0.5004 | | **spearman_max** | **0.5031** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 12,000 training samples * Columns: sentence_0 and sentence_1 * Approximate statistics based on the first 1000 samples: | | sentence_0 | sentence_1 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence_0 | sentence_1 | |:-------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------| | Pusat Peringatan Topan Gabungan (JTWC) juga mengeluarkan peringatan dalam kapasitas tidak resmi. | Pusat Peringatan Topan Gabungan (JTWC) hanya mengeluarkan peringatan dalam kapasitas yang tidak resmi. | | DNP komersial digunakan sebagai antiseptik dan pestisida bioakumulasi non-selektif. | DNP komersial tidak dapat digunakan sebagai antiseptik atau pestisida bioakumulasi non-selektif. | | Kuncian tulang belakang dan kuncian serviks diperbolehkan dan wajib dalam kompetisi jiu-jitsu Brasil IBJJF. | Kuncian tulang belakang dan kuncian serviks dilarang dalam kompetisi jiu-jitsu Brasil IBJJF. | * 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 - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `num_train_epochs`: 5 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `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 - `num_train_epochs`: 5 - `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 - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin
### Training Logs | Epoch | Step | Training Loss | str-dev_spearman_max | str-test_spearman_max | |:------:|:----:|:-------------:|:--------------------:|:---------------------:| | 1.0 | 188 | - | 0.4906 | 0.5067 | | 2.0 | 376 | - | 0.4941 | 0.5060 | | 2.6596 | 500 | 0.0995 | - | - | | 3.0 | 564 | - | 0.4935 | 0.5055 | | 4.0 | 752 | - | 0.4959 | 0.5016 | | 5.0 | 940 | - | 0.4975 | 0.5031 | ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.0.1 - Transformers: 4.44.0 - PyTorch: 2.4.0 - Accelerate: 0.33.0 - Datasets: 2.21.0 - 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} } ```