| --- |
| 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) <!-- at revision 835193815a3936a24a0ee7dc9e3d48c1fbb19c55 --> |
| - **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 |
| <!-- - **License:** Unknown --> |
|
|
| ### 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] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Semantic Similarity |
|
|
| * Datasets: `stsb-indo-edu-dev` and `stsb-indo-edu-test` |
| * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](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** | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## 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: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence1 | sentence2 | score | |
| |:--------|:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | list | list | float | |
| | details | <ul><li>min: 18 elements</li><li>mean: 58.40 elements</li><li>max: 137 elements</li></ul> | <ul><li>min: 15 elements</li><li>mean: 54.31 elements</li><li>max: 118 elements</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.46</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | score | |
| |:-------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:------------------| |
| | <code>['query: P', 'query: e', 'query: l', 'query: a', 'query: j', ...]</code> | <code>['passage: T', 'passage: a', 'passage: r', 'passage: i', 'passage: a', ...]</code> | <code>0.76</code> | |
| | <code>['query: S', 'query: e', 'query: b', 'query: e', 'query: l', ...]</code> | <code>['passage: U', 'passage: p', 'passage: a', 'passage: y', 'passage: a', ...]</code> | <code>0.85</code> | |
| | <code>['query: B', 'query: e', 'query: b', 'query: e', 'query: r', ...]</code> | <code>['passage: I', 'passage: n', 'passage: i', 'passage: ', 'passage: m', ...]</code> | <code>0.63</code> | |
| * Loss: [<code>CoSENTLoss</code>](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: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | sentence1 | sentence2 | score | |
| |:--------|:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:---------------------------------------------------------------| |
| | type | list | list | float | |
| | details | <ul><li>min: 14 elements</li><li>mean: 86.67 elements</li><li>max: 172 elements</li></ul> | <ul><li>min: 22 elements</li><li>mean: 88.94 elements</li><li>max: 177 elements</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> | |
| * Samples: |
| | sentence1 | sentence2 | score | |
| |:-------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:------------------| |
| | <code>['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...]</code> | <code>['passage: S', 'passage: e', 'passage: o', 'passage: r', 'passage: a', ...]</code> | <code>1.0</code> | |
| | <code>['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...]</code> | <code>['passage: S', 'passage: e', 'passage: o', 'passage: r', 'passage: a', ...]</code> | <code>0.95</code> | |
| | <code>['query: S', 'query: e', 'query: o', 'query: r', 'query: a', ...]</code> | <code>['passage: P', 'passage: r', 'passage: i', 'passage: a', 'passage: ', ...]</code> | <code>1.0</code> | |
| * Loss: [<code>CoSENTLoss</code>](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 |
| <details><summary>Click to expand</summary> |
|
|
| - `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 |
|
|
| </details> |
|
|
| ### 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}, |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
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| *Clearly define terms in order to be accessible across audiences.* |
| --> |
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| ## Model Card Authors |
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| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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| ## Model Card Contact |
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|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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