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: 512 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': 512, '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})
  (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("Miya67/aiq-scoring-e5-small-wiki")
# Run inference
sentences = [
    'query: 問題: 17世紀初めにシャムに渡り頭領として活躍するも、傷口に毒を塗られて殺害されたという日本人は誰でしょう? 回答: 山田長政',
    'query: 問題: 17世紀初めにシャムに渡り頭領として活躍するも、傷口に毒を塗られて殺害されたという日本人は誰でしょう? 回答: 山田仁左衛門',
    'query: 問題: 17世紀初めにシャムに渡り頭領として活躍するも、傷口に毒を塗られて殺害されたという日本人は誰でしょう? 回答: 川喜多長政',
]
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, 0.8598, 0.8739],
#         [0.8598, 1.0000, 0.7174],
#         [0.8739, 0.7174, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 68,138 training samples
Columns: sentence_0, sentence_1, and sentence_2
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 sentence_2
type string string string
details
min: 28 tokens
mean: 46.08 tokens
max: 70 tokens
min: 27 tokens
mean: 46.4 tokens
max: 74 tokens
min: 27 tokens
mean: 47.51 tokens
max: 75 tokens

Samples:

sentence_0	sentence_1	sentence_2
`query: 問題: トルコが面する2つの海・黒海とマルマラ海を結んでいる海峡は何海峡でしょう? 回答: ボスポラス海峡`	`query: 問題: トルコが面する2つの海・黒海とマルマラ海を結んでいる海峡は何海峡でしょう? 回答: イスタンブル海峡`	`query: 問題: トルコが面する2つの海・黒海とマルマラ海を結んでいる海峡は何海峡でしょう? 回答: 西アジア`
`query: 問題: シャンパンを飲むときなどに用いられる細長いグラスを、ある楽器の名前をとって何グラスというでしょう? 回答: シャンパン・グラス`	`query: 問題: シャンパンを飲むときなどに用いられる細長いグラスを、ある楽器の名前をとって何グラスというでしょう? 回答: シャンパングラス`	`query: 問題: シャンパンを飲むときなどに用いられる細長いグラスを、ある楽器の名前をとって何グラスというでしょう? 回答: カクテル・グラス`
`query: 問題: 年に2度収穫できる「二度芋」といえばジャガイモのことですが、年に3度収穫できることから「三度豆」と呼ばれているのは何でしょう? 回答: インゲンマメ`	`query: 問題: 年に2度収穫できる「二度芋」といえばジャガイモのことですが、年に3度収穫できることから「三度豆」と呼ばれているのは何でしょう? 回答: 隠元豆`	`query: 問題: 年に2度収穫できる「二度芋」といえばジャガイモのことですが、年に3度収穫できることから「三度豆」と呼ばれているのは何でしょう? 回答: マメ科`

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

per_device_train_batch_size: 64
num_train_epochs: 2
per_device_eval_batch_size: 64
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

per_device_train_batch_size: 64
num_train_epochs: 2
max_steps: -1
learning_rate: 5e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1
label_smoothing_factor: 0.0
bf16: False
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: trackio
eval_strategy: no
per_device_eval_batch_size: 64
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: False
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
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
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.4695	500	0.5994
0.9390	1000	0.3018
1.4085	1500	0.2309
1.8779	2000	0.2088

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.2.3
Transformers: 5.3.0
PyTorch: 2.10.0+cu128
Accelerate: 1.12.0
Datasets: 4.0.0
Tokenizers: 0.22.2

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}
}

	sentence_0	sentence_1	sentence_2
type	string	string	string
details	min: 28 tokens mean: 46.08 tokens max: 70 tokens	min: 27 tokens mean: 46.4 tokens max: 74 tokens	min: 27 tokens mean: 47.51 tokens max: 75 tokens