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Miya67
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aiq-scoring-e5-small-wiki

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:68138
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Model card Files
xet
Community

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: 512 tokens
  • Output Dimensionality: 384 dimensions
  • Similarity Function: Cosine Similarity

Model Sources

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.

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

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 

@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}
}
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