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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]
```
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You can finetune this model on your own dataset.
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## 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}
}
```
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