| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:49962 |
| - loss:MultipleNegativesRankingLoss |
| base_model: intfloat/multilingual-e5-large |
| widget: |
| - source_sentence: 'query: 잡지는 인종차별적이고 성차별적인 유머를 제공했다.' |
| sentences: |
| - 'passage: 잡지는 인종과 성별에 대해 편견적인 농담을 실었다.' |
| - 'passage: 잡지는 모든 사람을 존중하는 유머를 제공했다.' |
| - 'passage: 그들은 중요하지만 활용도가 낮은 데이터를 보유하고 있었다.' |
| - source_sentence: 'query: 칠천 명의 남자들이 도시를 봉쇄했다.' |
| sentences: |
| - 'passage: 이것으로 인해 몇몇 사람들이 반대했습니다.' |
| - 'passage: 칠천 명의 남자들이 도시의 봉쇄를 풀었다.' |
| - 'passage: 칠천 명의 사람들이 도시를 포위했다.' |
| - source_sentence: 'query: 그들은 얕은 사람들이다.' |
| sentences: |
| - 'passage: 그들은 깊이 있는 사람들이다.' |
| - 'passage: 당신은 사실들을 쉽게 확인할 수 있습니다.' |
| - 'passage: 그들은 깊이가 없는 사람들이다.' |
| - source_sentence: 'query: CFO 법 보고서에는 24개의 부서와 기관이 명시되어 있습니다.' |
| sentences: |
| - 'passage: CFO 법 보고서에는 명시된 부서와 기관이 없습니다.' |
| - 'passage: CFO 법 보고서에는 명시된 부서와 기관이 24개 있습니다.' |
| - 'passage: 준비해야 할 게 너무 많아요.' |
| - source_sentence: 'query: 그의 사망 약 30년 후에 극장들은 파괴되었다.' |
| sentences: |
| - 'passage: 그가 사망한 지 약 30년 후에 극장들이 무너졌다.' |
| - 'passage: 그 리조트와 호텔은 소란스럽다는 평가를 받았습니다.' |
| - 'passage: 그의 사망 약 30년 후에 극장들은 번창했다.' |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # SentenceTransformer based on intfloat/multilingual-e5-large |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large). It maps sentences & paragraphs to a 1024-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-large](https://huggingface.co/intfloat/multilingual-e5-large) <!-- at revision ab10c1a7f42e74530fe7ae5be82e6d4f11a719eb --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Output Dimensionality:** 1024 tokens |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **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': 1024, '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("PerRing/e5_ko") |
| # Run inference |
| sentences = [ |
| 'query: 그의 사망 약 30년 후에 극장들은 파괴되었다.', |
| 'passage: 그가 사망한 지 약 30년 후에 극장들이 무너졌다.', |
| 'passage: 그의 사망 약 30년 후에 극장들은 번창했다.', |
| ] |
| embeddings = model.encode(sentences) |
| print(embeddings.shape) |
| # [3, 1024] |
| |
| # 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.* |
| --> |
|
|
| <!-- |
| ## 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 |
|
|
| #### Unnamed Dataset |
|
|
|
|
| * Size: 49,962 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 8 tokens</li><li>mean: 20.11 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 19.17 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 18.47 tokens</li><li>max: 48 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:---------------------------------------------------------|:--------------------------------------------------------|:---------------------------------------------------------| |
| | <code>query: 사람들이 기억하면 당신은 다음 수준으로 물건을 잃게 됩니다.</code> | <code>passage: 사람들이 상기하면 당신은 다음 단계로 물건을 잃습니다.</code> | <code>passage: 사람들이 기억하지 않으면 당신은 물건을 잃지 않고 유지합니다.</code> | |
| | <code>query: 제 팀의 한 멤버가 당신의 명령을 매우 정확하게 수행할 것입니다.</code> | <code>passage: 제 팀원 중 한 명이 당신의 지시를 철저히 이행할 것입니다.</code> | <code>passage: 제 팀의 누구도 당신의 명령을 정확히 수행하지 않을 것입니다.</code> | |
| | <code>query: 이 정보는 그들의 것입니다.</code> | <code>passage: 이 정보는 그들 소유입니다.</code> | <code>passage: 이 정보는 우리 것입니다.</code> | |
| * Loss: [<code>MultipleNegativesRankingLoss</code>](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`: 2048 |
| - `per_device_eval_batch_size`: 2048 |
| - `num_train_epochs`: 2 |
| - `warmup_ratio`: 0.1 |
| - `gradient_checkpointing`: True |
| - `batch_sampler`: no_duplicates |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: no |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 2048 |
| - `per_device_eval_batch_size`: 2048 |
| - `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`: 1.0 |
| - `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`: 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`: True |
| - `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 |
| - `use_liger_kernel`: False |
| - `eval_use_gather_object`: False |
| - `batch_sampler`: no_duplicates |
| - `multi_dataset_batch_sampler`: proportional |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | |
| |:------:|:----:|:-------------:| |
| | 0.04 | 1 | 5.7804 | |
| | 0.08 | 2 | 5.7809 | |
| | 0.12 | 3 | 4.8376 | |
| | 0.16 | 4 | 3.6166 | |
| | 0.2 | 5 | 2.4258 | |
| | 0.24 | 6 | 1.2571 | |
| | 0.28 | 7 | 0.7152 | |
| | 0.32 | 8 | 0.5181 | |
| | 0.36 | 9 | 0.359 | |
| | 0.4 | 10 | 0.3295 | |
| | 0.44 | 11 | 0.2502 | |
| | 0.48 | 12 | 0.2256 | |
| | 0.52 | 13 | 0.1842 | |
| | 0.56 | 14 | 0.1588 | |
| | 0.6 | 15 | 0.1614 | |
| | 0.64 | 16 | 0.1535 | |
| | 0.68 | 17 | 0.14 | |
| | 0.72 | 18 | 0.152 | |
| | 0.76 | 19 | 0.1221 | |
| | 0.8 | 20 | 0.1179 | |
| | 0.84 | 21 | 0.1354 | |
| | 0.88 | 22 | 0.1349 | |
| | 0.92 | 23 | 0.1364 | |
| | 0.96 | 24 | 0.1169 | |
| | 1.0 | 25 | 0.0863 | |
| | 1.04 | 26 | 0.1161 | |
| | 1.08 | 27 | 0.1105 | |
| | 1.12 | 28 | 0.1067 | |
| | 1.16 | 29 | 0.1087 | |
| | 1.2 | 30 | 0.1165 | |
| | 1.24 | 31 | 0.1082 | |
| | 1.28 | 32 | 0.0866 | |
| | 1.32 | 33 | 0.0854 | |
| | 1.3600 | 34 | 0.0905 | |
| | 1.4 | 35 | 0.0809 | |
| | 1.44 | 36 | 0.079 | |
| | 1.48 | 37 | 0.0665 | |
| | 1.52 | 38 | 0.0542 | |
| | 1.56 | 39 | 0.06 | |
| | 1.6 | 40 | 0.0624 | |
| | 1.6400 | 41 | 0.0671 | |
| | 1.6800 | 42 | 0.0721 | |
| | 1.72 | 43 | 0.0781 | |
| | 1.76 | 44 | 0.0531 | |
| | 1.8 | 45 | 0.0556 | |
| | 1.8400 | 46 | 0.0732 | |
| | 1.88 | 47 | 0.0748 | |
| | 1.92 | 48 | 0.0773 | |
| | 1.96 | 49 | 0.068 | |
| | 2.0 | 50 | 0.0347 | |
|
|
|
|
| ### Framework Versions |
| - Python: 3.10.13 |
| - Sentence Transformers: 3.2.1 |
| - Transformers: 4.45.2 |
| - PyTorch: 2.4.1+cu121 |
| - Accelerate: 1.1.0 |
| - Datasets: 3.1.0 |
| - Tokenizers: 0.20.2 |
|
|
| ## 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} |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
