e5_ko / README.md

Add new SentenceTransformer model

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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 model finetuned from 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 tokens
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': 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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]

Training Details

Training Dataset

Unnamed Dataset

Size: 49,962 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 8 tokens mean: 20.11 tokens max: 68 tokens	min: 7 tokens mean: 19.17 tokens max: 62 tokens	min: 7 tokens mean: 18.47 tokens max: 48 tokens

Samples:

anchor	positive	negative
`query: 사람들이 기억하면 당신은 다음 수준으로 물건을 잃게 됩니다.`	`passage: 사람들이 상기하면 당신은 다음 단계로 물건을 잃습니다.`	`passage: 사람들이 기억하지 않으면 당신은 물건을 잃지 않고 유지합니다.`
`query: 제 팀의 한 멤버가 당신의 명령을 매우 정확하게 수행할 것입니다.`	`passage: 제 팀원 중 한 명이 당신의 지시를 철저히 이행할 것입니다.`	`passage: 제 팀의 누구도 당신의 명령을 정확히 수행하지 않을 것입니다.`
`query: 이 정보는 그들의 것입니다.`	`passage: 이 정보는 그들 소유입니다.`	`passage: 이 정보는 우리 것입니다.`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "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

Click to expand

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

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

@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

@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}
}