--- language: - id tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6198 - loss:CoSENTLoss base_model: intfloat/multilingual-e5-base datasets: - Pustekhan-ITB/stsb-indo-edu pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine model-index: - name: SentenceTransformer based on intfloat/multilingual-e5-base results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: stsb indo edu dev type: stsb-indo-edu-dev metrics: - type: pearson_cosine value: 0.1930033858243812 name: Pearson Cosine - type: spearman_cosine value: 0.17647076252403324 name: Spearman Cosine - task: type: semantic-similarity name: Semantic Similarity dataset: name: stsb indo edu test type: stsb-indo-edu-test metrics: - type: pearson_cosine value: 0.15065000397563194 name: Pearson Cosine - type: spearman_cosine value: 0.1512326380689479 name: Spearman Cosine --- # SentenceTransformer based on intfloat/multilingual-e5-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on the [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) 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:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) - **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: XLMRobertaModel (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): Normalize() ) ``` ## 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("ewideplus/indoedu-e5-base") # Run inference sentences = [ 'The weather is lovely today.', "It's so sunny outside!", 'He drove to the stadium.', ] 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 * Datasets: `stsb-indo-edu-dev` and `stsb-indo-edu-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | stsb-indo-edu-dev | stsb-indo-edu-test | |:--------------------|:------------------|:-------------------| | pearson_cosine | 0.193 | 0.1507 | | **spearman_cosine** | **0.1765** | **0.1512** | ## Training Details ### Training Dataset #### stsb-indo-edu * Dataset: [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) at [f84d4d6](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu/tree/f84d4d6eaca768507bd0f298aef6f3f1a98ddefc) * Size: 6,198 training samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | list | list | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:-------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:------------------| | ['query: P', 'query: e', 'query: l', 'query: a', 'query: j', ...] | ['passage: T', 'passage: a', 'passage: r', 'passage: i', 'passage: a', ...] | 0.76 | | ['query: S', 'query: e', 'query: b', 'query: e', 'query: l', ...] | ['passage: U', 'passage: p', 'passage: a', 'passage: y', 'passage: a', ...] | 0.85 | | ['query: B', 'query: e', 'query: b', 'query: e', 'query: r', ...] | ['passage: I', 'passage: n', 'passage: i', 'passage: ', 'passage: m', ...] | 0.63 | * Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Evaluation Dataset #### stsb-indo-edu * Dataset: [stsb-indo-edu](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu) at [f84d4d6](https://huggingface.co/datasets/Pustekhan-ITB/stsb-indo-edu/tree/f84d4d6eaca768507bd0f298aef6f3f1a98ddefc) * Size: 1,536 evaluation samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | list | list | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:-------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:------------------| | ['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...] | ['passage: S', 'passage: e', 'passage: o', 'passage: r', 'passage: a', ...] | 1.0 | | ['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...] | ['passage: S', 'passage: e', 'passage: o', 'passage: r', 'passage: a', ...] | 0.95 | | ['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...] | ['passage: P', 'passage: r', 'passage: i', 'passage: a', 'passage: ', ...] | 1.0 | * Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `learning_rate`: 1e-05 - `weight_decay`: 0.01 - `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`: 32 - `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`: 1e-05 - `weight_decay`: 0.01 - `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 | Epoch | Step | Training Loss | Validation Loss | stsb-indo-edu-dev_spearman_cosine | stsb-indo-edu-test_spearman_cosine | |:------:|:----:|:-------------:|:---------------:|:---------------------------------:|:----------------------------------:| | -1 | -1 | - | - | 0.0995 | - | | 0.5155 | 100 | 6.2244 | 4.7594 | 0.1027 | - | | 1.0309 | 200 | 6.1605 | 4.7518 | 0.1502 | - | | 1.5464 | 300 | 6.16 | 4.7553 | 0.1564 | - | | 2.0619 | 400 | 6.1609 | 4.7527 | 0.1714 | - | | 2.5773 | 500 | 6.1593 | 4.7698 | 0.1495 | - | | 3.0928 | 600 | 6.1517 | 4.7516 | 0.1657 | - | | 3.6082 | 700 | 6.1555 | 4.7463 | 0.1787 | - | | 4.1237 | 800 | 6.1452 | 4.7548 | 0.1665 | - | | 4.6392 | 900 | 6.1523 | 4.7494 | 0.1765 | - | | -1 | -1 | - | - | - | 0.1512 | ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.4.1 - Transformers: 4.48.3 - PyTorch: 2.5.1+cu124 - Accelerate: 1.3.0 - Datasets: 3.3.2 - 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", } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ```