--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:10554 - loss:CosineSimilarityLoss base_model: LazarusNLP/all-indo-e5-small-v4 widget: - source_sentence: Menggunakan sunscreen setiap hari sentences: - Seorang anak laki-laki yang tampak sakit disentuh wajahnya oleh seorang balita. - 'Warga Hispanik secara resmi telah menyalip warga Amerika keturunan Afrika sebagai kelompok minoritas terbesar di AS menurut laporan yang dirilis oleh Biro Sensus AS.' - Tidak pernah menggunakan sunscreen - source_sentence: Sering membeli makanan siap saji melalui aplikasi sentences: - Provinsi ini memiliki angka kepadatan penduduk 38 jiwa/km². - Kadang membeli makanan siap saji melalui aplikasi - Seorang pria sedang melakukan trik kartu. - source_sentence: University of Michigan hari ini merilis kebijakan penerimaan mahasiswa baru setelah Mahkamah Agung AS membatalkan cara penerimaan mahasiswa baru yang sebelumnya. sentences: - '"Mereka telah memblokir semua tanaman bio baru karena ketakutan yang tidak berdasar dan tidak ilmiah," kata Bush.' - Jarang membeli kopi Kenangan - University of Michigan berencana untuk merilis kebijakan penerimaan mahasiswa baru pada hari Kamis setelah persyaratan penerimaannya ditolak oleh Mahkamah Agung AS pada bulan Juni. - source_sentence: pakar non-proliferasi di institut internasional untuk studi strategis mark fitzpatrick menyatakan bahwa laporan IAEA - memiliki tenor yang sangat kuat. sentences: - Pernah membeli kopi Starbucks - rekan senior di institut internasional untuk studi strategis mark fitzpatrick menyatakan bahwa - rencana badan energi atom internasional adalah dangkal. - Korea Utara mengusulkan pembicaraan tingkat tinggi dengan AS - source_sentence: Palestina dan Yordania koordinasikan sikap dalam perundingan damai sentences: - Petinggi Hamas bantah Gaza dan PA berkoordinasi dalam perundingan damai - Tidak pernah memesan makanan lewat aplikasi - Kereta api yang melaju di atas rel. pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on LazarusNLP/all-indo-e5-small-v4 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts indo detailed type: sts-indo-detailed metrics: - type: pearson_cosine value: 0.8612625897174441 name: Pearson Cosine - type: spearman_cosine value: 0.8586969176298713 name: Spearman Cosine --- # SentenceTransformer based on LazarusNLP/all-indo-e5-small-v4 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [LazarusNLP/all-indo-e5-small-v4](https://huggingface.co/LazarusNLP/all-indo-e5-small-v4). It maps sentences & paragraphs to a 384-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:** [LazarusNLP/all-indo-e5-small-v4](https://huggingface.co/LazarusNLP/all-indo-e5-small-v4) - **Maximum Sequence Length:** 384 tokens - **Output Dimensionality:** 384 dimensions - **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': 384, 'do_lower_case': False, 'architecture': 'BertModel'}) (1): Pooling({'word_embedding_dimension': 384, '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 = [ 'Palestina dan Yordania koordinasikan sikap dalam perundingan damai', 'Petinggi Hamas bantah Gaza dan PA berkoordinasi dalam perundingan damai', 'Kereta api yang melaju di atas rel.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[ 1.0000, 0.5014, -0.0652], # [ 0.5014, 1.0000, -0.0518], # [-0.0652, -0.0518, 1.0000]]) ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-indo-detailed` * Evaluated with __main__.DetailedEmbeddingSimilarityEvaluator | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8613 | | **spearman_cosine** | **0.8587** | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 10,554 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 | float | | details | | | | * Samples: | sentence_0 | sentence_1 | label | |:-------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------| | Tidak pernah mengisi saldo ShopeePay | Tidak pernah mengisi saldo GoPay | 0.0 | | PM Turki mendesak untuk mengakhiri protes di Istanbul | Polisi Turki menembakkan gas air mata ke arah pengunjuk rasa di Istanbul | 0.56 | | Dua ekor kucing sedang melihat ke arah jendela. | Seekor kucing putih yang sedang melihat ke luar jendela. | 0.5199999809265137 | * 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`: 6 - `per_device_eval_batch_size`: 6 - `num_train_epochs`: 7 - `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`: 6 - `per_device_eval_batch_size`: 6 - `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`: 7 - `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} - `parallelism_config`: None - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `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 - `hub_revision`: None - `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 - `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 - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | sts-indo-detailed_spearman_cosine | |:------:|:----:|:-------------:|:---------------------------------:| | 0.0569 | 100 | - | 0.8225 | | 0.1137 | 200 | - | 0.8261 | | 0.1706 | 300 | - | 0.8263 | | 0.2274 | 400 | - | 0.8259 | | 0.2843 | 500 | 0.0764 | 0.8273 | | 0.3411 | 600 | - | 0.8305 | | 0.3980 | 700 | - | 0.8319 | | 0.4548 | 800 | - | 0.8341 | | 0.5117 | 900 | - | 0.8345 | | 0.5685 | 1000 | 0.0445 | 0.8362 | | 0.6254 | 1100 | - | 0.8384 | | 0.6822 | 1200 | - | 0.8391 | | 0.7391 | 1300 | - | 0.8464 | | 0.7959 | 1400 | - | 0.8475 | | 0.8528 | 1500 | 0.0372 | 0.8471 | | 0.9096 | 1600 | - | 0.8477 | | 0.9665 | 1700 | - | 0.8458 | | 1.0 | 1759 | - | 0.8464 | | 1.0233 | 1800 | - | 0.8443 | | 1.0802 | 1900 | - | 0.8455 | | 1.1370 | 2000 | 0.0316 | 0.8481 | | 1.1939 | 2100 | - | 0.8447 | | 1.2507 | 2200 | - | 0.8473 | | 1.3076 | 2300 | - | 0.8474 | | 1.3644 | 2400 | - | 0.8449 | | 1.4213 | 2500 | 0.0281 | 0.8515 | | 1.4781 | 2600 | - | 0.8498 | | 1.5350 | 2700 | - | 0.8506 | | 1.5918 | 2800 | - | 0.8546 | | 1.6487 | 2900 | - | 0.8534 | | 1.7055 | 3000 | 0.0271 | 0.8512 | | 1.7624 | 3100 | - | 0.8493 | | 1.8192 | 3200 | - | 0.8499 | | 1.8761 | 3300 | - | 0.8523 | | 1.9329 | 3400 | - | 0.8518 | | 1.9898 | 3500 | 0.0258 | 0.8529 | | 2.0 | 3518 | - | 0.8535 | | 2.0466 | 3600 | - | 0.8546 | | 2.1035 | 3700 | - | 0.8526 | | 2.1603 | 3800 | - | 0.8548 | | 2.2172 | 3900 | - | 0.8504 | | 2.2740 | 4000 | 0.0222 | 0.8535 | | 2.3309 | 4100 | - | 0.8533 | | 2.3877 | 4200 | - | 0.8538 | | 2.4446 | 4300 | - | 0.8518 | | 2.5014 | 4400 | - | 0.8515 | | 2.5583 | 4500 | 0.021 | 0.8515 | | 2.6151 | 4600 | - | 0.8529 | | 2.6720 | 4700 | - | 0.8548 | | 2.7288 | 4800 | - | 0.8552 | | 2.7857 | 4900 | - | 0.8542 | | 2.8425 | 5000 | 0.0209 | 0.8571 | | 2.8994 | 5100 | - | 0.8552 | | 2.9562 | 5200 | - | 0.8553 | | 3.0 | 5277 | - | 0.8552 | | 3.0131 | 5300 | - | 0.8560 | | 3.0699 | 5400 | - | 0.8531 | | 3.1268 | 5500 | 0.0199 | 0.8491 | | 3.1836 | 5600 | - | 0.8515 | | 3.2405 | 5700 | - | 0.8520 | | 3.2973 | 5800 | - | 0.8547 | | 3.3542 | 5900 | - | 0.8558 | | 3.4110 | 6000 | 0.0182 | 0.8560 | | 3.4679 | 6100 | - | 0.8561 | | 3.5247 | 6200 | - | 0.8562 | | 3.5816 | 6300 | - | 0.8547 | | 3.6384 | 6400 | - | 0.8547 | | 3.6953 | 6500 | 0.0171 | 0.8561 | | 3.7521 | 6600 | - | 0.8563 | | 3.8090 | 6700 | - | 0.8555 | | 3.8658 | 6800 | - | 0.8562 | | 3.9227 | 6900 | - | 0.8587 | ### Framework Versions - Python: 3.11.13 - Sentence Transformers: 5.1.0 - Transformers: 4.56.0 - PyTorch: 2.8.0 - Accelerate: 1.10.1 - Datasets: 4.0.0 - Tokenizers: 0.22.0 ## 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", } ```