--- base_model: indobenchmark/indobert-base-p2 datasets: - afaji/indonli language: - id 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:6915 - loss:SoftmaxLoss widget: - source_sentence: Pesta Olahraga Asia Tenggara atau Southeast Asian Games, biasa disingkat SEA Games, adalah ajang olahraga yang diadakan setiap dua tahun dan melibatkan 11 negara Asia Tenggara. sentences: - Sekarang tahun 2017. - Warna kulit tidak mempengaruhi waktu berjemur yang baik untuk mengatifkan pro-vitamin D3. - Pesta Olahraga Asia Tenggara diadakan setiap tahun. - source_sentence: Menjalani aktivitas Ramadhan di tengah wabah Corona tentunya tidak mudah. sentences: - Tidak ada observasi yang pernah dilansir oleh Business Insider. - Wabah Corona membuat aktivitas Ramadhan tidak mudah dijalani. - Piala Sudirman pertama digelar pada tahun 1989. - source_sentence: Dalam bidang politik, partai ini memperjuangkan agar kekuasaan sepenuhnya berada di tangan rakyat. sentences: - Galileo tidak berhasil mengetes hasil dari Hukum Inert. - Kudeta 14 Februari 1946 gagal merebut kekuasaan Belanda. - Partai ini berusaha agar kekuasaan sepenuhnya berada di tangan rakyat. - source_sentence: Keluarga mendiang Prince menuduh layanan musik streaming Tidal memasukkan karya milik sang penyanyi legendaris tanpa izin . sentences: - Rosier adalah pelayan setia Lord Voldemort. - Bangunan ini digunakan untuk penjualan. - Keluarga mendiang Prince sudah memberi izin kepada TImbal untuk menggunakan lagu milik Prince. - source_sentence: Tujuan dari acara dengar pendapat CRTC adalah untuk mengumpulkan respons dari pada pemangku kepentingan industri ini dan dari masyarakat umum. sentences: - Pembuat Rooms hanya bisa membuat meeting yang terbuka. - Masyarakat umum dilibatkan untuk memberikan respon dalam acara dengar pendapat CRTC. - Eminem dirasa tidak akan memulai kembali kariernya tahun ini. 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: 0.5829898836235055 name: Pearson Cosine - type: spearman_cosine value: 0.5604880880211627 name: Spearman Cosine - type: pearson_manhattan value: 0.5703534992812126 name: Pearson Manhattan - type: spearman_manhattan value: 0.5499989364166947 name: Spearman Manhattan - type: pearson_euclidean value: 0.5753323630988341 name: Pearson Euclidean - type: spearman_euclidean value: 0.552442969754755 name: Spearman Euclidean - type: pearson_dot value: 0.5620113473718095 name: Pearson Dot - type: spearman_dot value: 0.5624324325309726 name: Spearman Dot - type: pearson_max value: 0.5829898836235055 name: Pearson Max - type: spearman_max value: 0.5624324325309726 name: Spearman Max - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.27661444766220145 name: Pearson Cosine - type: spearman_cosine value: 0.25397061268923804 name: Spearman Cosine - type: pearson_manhattan value: 0.22893950626786405 name: Pearson Manhattan - type: spearman_manhattan value: 0.2295445814901059 name: Spearman Manhattan - type: pearson_euclidean value: 0.23773763148887356 name: Pearson Euclidean - type: spearman_euclidean value: 0.23225044424139019 name: Spearman Euclidean - type: pearson_dot value: 0.2930559400528471 name: Pearson Dot - type: spearman_dot value: 0.28163535345836893 name: Spearman Dot - type: pearson_max value: 0.2930559400528471 name: Pearson Max - type: spearman_max value: 0.28163535345836893 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) on the [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) 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:** [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) - **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': 512, '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("cassador/2bs32lr2") # Run inference sentences = [ 'Tujuan dari acara dengar pendapat CRTC adalah untuk mengumpulkan respons dari pada pemangku kepentingan industri ini dan dari masyarakat umum.', 'Masyarakat umum dilibatkan untuk memberikan respon dalam acara dengar pendapat CRTC.', 'Pembuat Rooms hanya bisa membuat meeting yang terbuka.', ] 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 | 0.583 | | **spearman_cosine** | **0.5605** | | pearson_manhattan | 0.5704 | | spearman_manhattan | 0.55 | | pearson_euclidean | 0.5753 | | spearman_euclidean | 0.5524 | | pearson_dot | 0.562 | | spearman_dot | 0.5624 | | pearson_max | 0.583 | | spearman_max | 0.5624 | #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:----------| | pearson_cosine | 0.2766 | | **spearman_cosine** | **0.254** | | pearson_manhattan | 0.2289 | | spearman_manhattan | 0.2295 | | pearson_euclidean | 0.2377 | | spearman_euclidean | 0.2323 | | pearson_dot | 0.2931 | | spearman_dot | 0.2816 | | pearson_max | 0.2931 | | spearman_max | 0.2816 | ## Training Details ### Training Dataset #### afaji/indonli * Dataset: [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) * Size: 6,915 training samples * Columns: premise, hypothesis, and label * Approximate statistics based on the first 1000 samples: | | premise | hypothesis | label | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | premise | hypothesis | label | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------|:---------------| | Presiden Joko Widodo (Jokowi) menyampaikan prediksi bahwa wabah virus Corona (COVID-19) di Indonesia akan selesai akhir tahun ini. | Prediksi akhir wabah tidak disampaikan Jokowi. | 0 | | Meski biasanya hanya digunakan di fasilitas kesehatan, saat ini masker dan sarung tangan sekali pakai banyak dipakai di tingkat rumah tangga. | Masker sekali pakai banyak dipakai di tingkat rumah tangga. | 1 | | Seperti namanya, paket internet sahur Telkomsel ini ditujukan bagi pengguna yang menginginkan kuota ekstra, untuk menemani momen sahur sepanjang bulan puasa. | Paket internet sahur tidak ditujukan untuk saat sahur. | 0 | * Loss: [SoftmaxLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) ### Evaluation Dataset #### afaji/indonli * Dataset: [afaji/indonli](https://huggingface.co/datasets/afaji/indonli) * Size: 1,556 evaluation samples * Columns: premise, hypothesis, and label * Approximate statistics based on the first 1000 samples: | | premise | hypothesis | label | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | premise | hypothesis | label | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:---------------| | Manuskrip tersebut berisi tiga catatan yang menceritakan bagaimana peristiwa jatuhnya meteorit serta laporan kematian akibat kejadian tersebut seperti dilansir dari Science Alert, Sabtu (25/4/2020). | Manuskrip tersebut tidak mencatat laporan kematian. | 0 | | Dilansir dari Business Insider, menurut observasi dari Mauna Loa Observatory di Hawaii pada karbon dioksida (CO2) di level mencapai 410 ppm tidak langsung memberikan efek pada pernapasan, karena tubuh manusia juga masih membutuhkan CO2 dalam kadar tertentu. | Tidak ada observasi yang pernah dilansir oleh Business Insider. | 0 | | Seorang wanita asal New York mengaku sangat benci air putih. | Tidak ada orang dari New York yang membenci air putih. | 0 | * Loss: [SoftmaxLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss) ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `learning_rate`: 2e-05 - `num_train_epochs`: 2 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `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 - `learning_rate`: 2e-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`: 2 - `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`: 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`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:| | 0 | 0 | - | - | 0.1277 | - | | 0.4608 | 100 | 0.5694 | - | - | - | | 0.9217 | 200 | 0.4754 | - | - | - | | 1.0 | 217 | - | 0.4349 | 0.5410 | - | | 1.3825 | 300 | 0.3829 | - | - | - | | 1.8433 | 400 | 0.3507 | - | - | - | | 2.0 | 434 | - | 0.4254 | 0.5605 | 0.2540 | ### 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: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers and SoftmaxLoss ```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", } ```