This is a sentence-transformers model finetuned from intfloat/multilingual-e5-large on the train dataset. 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: 256 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- train

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': 256, 'do_lower_case': False, 'architecture': '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'query: 3D-TSV가 반도체 설계에 미치는 영향은 무엇인가요?',
    'passage: 3 Dimension-Through Silicon Via (기술)',
    'passage: Available Bit Rate (Applicational)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.8098, -0.1741],
#         [ 0.8098,  1.0000, -0.2449],
#         [-0.1741, -0.2449,  1.0000]])

Evaluation

Metrics

Information Retrieval

Dataset: e5-eval-real
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9683
cosine_accuracy@3	0.9981
cosine_accuracy@5	0.9997
cosine_accuracy@10	0.9999
cosine_precision@1	0.9683
cosine_precision@3	0.3327
cosine_precision@5	0.1999
cosine_precision@10	0.1
cosine_recall@1	0.9683
cosine_recall@3	0.9981
cosine_recall@5	0.9997
cosine_recall@10	0.9999
cosine_ndcg@10	0.9874
cosine_mrr@10	0.983
cosine_map@100	0.983

Training Details

Training Dataset

train

Dataset: train
Size: 43,318 training samples
Columns: 0 and 1
Approximate statistics based on the first 1000 samples:
0 1
type string string
details
min: 11 tokens
mean: 17.56 tokens
max: 32 tokens
min: 9 tokens
mean: 14.13 tokens
max: 27 tokens

Samples:

0	1
`query: ABPL은 ATM의 기초 속도를 지원하는 물리 계층 장치로 어떻게 구성되나요?`	`passage: ATM Base Rate Physical Layer Unit (기술)`
`query: How is the ABPL configured as a physical layer unit supporting the ATM base rate?`	`passage: ATM Base Rate Physical Layer Unit (Technical)`
`query: ABPL의 역할은 ATM 네트워크에서 무엇을 의미하나요?`	`passage: ATM Base Rate Physical Layer Unit (개념)`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 1e-05
weight_decay: 0.01
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
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: 3
max_steps: -1
lr_scheduler_type: cosine
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: True
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
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: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	e5-eval-real_cosine_ndcg@10
0.0015	1	2.8346	-
0.1477	100	1.1145	-
0.2954	200	0.0332	0.9633
0.4431	300	0.0185	-
0.5908	400	0.0154	0.9782
0.7386	500	0.0116	-
0.8863	600	0.0107	0.9810
1.0340	700	0.0078	-
1.1817	800	0.0076	0.9830
1.3294	900	0.0045	-
1.4771	1000	0.0043	0.9851
1.6248	1100	0.0034	-
1.7725	1200	0.0037	0.9862
1.9202	1300	0.0031	-
2.0679	1400	0.0034	0.9870
2.2157	1500	0.0029	-
2.3634	1600	0.0025	0.9872
2.5111	1700	0.0027	-
2.6588	1800	0.0022	0.9875
2.8065	1900	0.0027	-
2.9542	2000	0.0025	0.9875
-1	-1	-	0.9874

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: 3.6.0
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