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josangho99/ko-multilingual-e5-small-multiTask

sourceHugging Faceupdated 1y agoView on Hugging Face

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SentenceTransformer based on intfloat/multilingual-e5-small

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-small. 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: intfloat/multilingual-e5-small
—Maximum Sequence Length: 128 tokens
—Output Dimensionality: 384 dimensions
—Similarity Function: Cosine Similarity

Model Sources

—Documentation: Sentence Transformers Documentation
—Repository: Sentence Transformers on GitHub
—Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, '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("josangho99/ko-multilingual-e5-small-multiTask")
# Run inference
sentences = [
    '여자와 아이가 게임을 하고 있다.',
    '여자와 어린 아이는 보드게임을 하면서 즐거운 시간을 보낸다.',
    '한 여성과 그녀의 아이가 영화를 보고 있다.',
]
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, 0.6531, 0.4611],
#         [0.6531, 1.0000, 0.3023],
#         [0.4611, 0.3023, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Semantic Similarity

—Dataset: sts-dev
—Evaluated with <code>EmbeddingSimilarityEvaluator</code>

Metric	Value
pearson_cosine	0.8491
spearman_cosine	0.8481

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Training Details

Training Datasets

Unnamed Dataset

—Size: 568,640 training samples
—Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>sentence_2</code>
—Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | sentence_2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 19.75 tokens</li><li>max: 99 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.98 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.82 tokens</li><li>max: 47 tokens</li></ul> |
—Samples: | sentence0 | sentence1 | sentence_2 | |:-----------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------| | <code>실버민 베이에는 섬의 다른 지역으로 가는 노선이 있는 버스 터미널이 있다.</code> | <code>페리는 오전 6시 10분에서 오후 10시 30분 사이에 2시간마다 센트럴에서 실버 마인 베이 (Mui Wo)로 출발하며 버스 터미널에는 섬의 모든 지역으로 버스가 있습니다.</code> | <code>실버민 만의 버스는 섬의 한 부분으로만 간다.</code> | | <code>사람이 사형선고를 받으면 국가가 형을 집행하고 그 사람을 사형에 처해야 하는데 항소절차가 없어야 한다.</code> | <code>어쨌든 내가 4 년 동안 주 교도소 시스템과 함께 일한 후, 당신이 그 사람에게 사형 선고를 계속하고 문장을 이해할 수있게 해주겠다고 결론을 내렸다는 결론은 누군가에게 사형 선고를 내리면 30 일 이내에 항소에 실패하면 한 번의 항소를 받게됩니다. 그것에 대해</code> | <code>사형수라면 형량이 감형될 때까지 원하는 만큼 항소할 수 있도록 해야 한다.</code> | | <code>웹 사이트 기능은 불편한 자원 봉사자를 지원합니다.</code> | <code>두 번째 웹 사이트 기능은 새로운 법률 분야에서 불편하고 지침과 방향이 필요한 자원 봉사자를 지원합니다.</code> | <code>두 번째 웹사이트 기능은 자원봉사자들에게 무료 아이스캔디를 제공한다.</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false
  }

Unnamed Dataset

—Size: 5,749 training samples
—Columns: <code>sentence0</code>, <code>sentence1</code>, and <code>label</code>
—Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 6 tokens</li><li>mean: 19.17 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.06 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
—Samples: | sentence0 | sentence1 | label | |:-----------------------------------------------------|:-----------------------------------------|:------------------| | <code>케냐 경찰 체포 키 알샤바브 신병 모집인 금융가</code> | <code>케냐 : 대테러 경찰에 의해 체포된 쌍</code> | <code>0.64</code> | | <code>"요정은 존재하지 않는다" - 좋아.</code> | <code>"레프리콘은 존재하지 않는다" - 좋아.</code> | <code>0.2</code> | | <code>당신이 필요로 하는 모든 것에 대한 가격은 인플레이션 때문에 상승했다.</code> | <code>당신이 소유한 모든 IE 자산의 가격이 하락했다.</code> | <code>0.4</code> |
—Loss: <code>CosineSimilarityLoss</code> with these parameters:

json

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }

Training Hyperparameters

Non-Default Hyperparameters

—eval_strategy: steps
—num_train_epochs: 5
—batch_sampler: no_duplicates
—multi_dataset_batch_sampler: round_robin

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: 8
—per_device_eval_batch_size: 8
—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: 5
—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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—fsdp_transformer_layer_cls_to_wrap: None
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
—parallelism_config: None
—deepspeed: None
—label_smoothing_factor: 0.0
—optim: adamwtorchfused
—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: no_duplicates
—multi_dataset_batch_sampler: round_robin
—router_mapping: {}
—learning_rate_mapping: {}

</details>

Training Logs

Epoch	Step	Training Loss	sts-dev_spearman_cosine
0.3477	500	0.3954	-
0.6954	1000	0.3058	0.8398
1.0	1438	-	0.8456
1.0431	1500	0.2945	-
1.3908	2000	0.2003	0.8440
1.7385	2500	0.1785	-
2.0	2876	-	0.8459
2.0862	3000	0.1774	0.8471
2.4339	3500	0.1271	-
2.7816	4000	0.1278	0.8481

Framework Versions

—Python: 3.12.11
—Sentence Transformers: 5.1.0
—Transformers: 4.56.1
—PyTorch: 2.8.0+cu126
—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",
}

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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