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Miya67/aiq-scoring-e5-small

sourceHugging Faceupdated 7mo agoView on Hugging Face

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Model Card

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

—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:

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("Miya67/aiq-scoring-e5-small")
# Run inference
sentences = [
    'query: 問題: 「有機物」「無機物」などというときの有機・無機とは、どんな物質の有無をいったものでしょう? 回答: たんそ',
    'query: 問題: 「有機物」「無機物」などというときの有機・無機とは、どんな物質の有無をいったものでしょう? 回答: たんそ',
    'query: 問題: 「有機物」「無機物」などというときの有機・無機とは、どんな物質の有無をいったものでしょう? 回答: しちっそ',
]
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, 1.0000, 0.4022],
#         [1.0000, 1.0000, 0.4022],
#         [0.4022, 0.4022, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

Unnamed Dataset

—Size: 31,992 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: 30 tokens</li><li>mean: 47.97 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 47.2 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 48.67 tokens</li><li>max: 73 tokens</li></ul> |
—Samples: | sentence0 | sentence1 | sentence_2 | |:-----------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | <code>query: 問題: バスケットボールで、NBAの76ersの本拠地はフィラデルフィアですが、bjリーグの89ERSがあるのはどこでしょう? 回答: 仙台市</code> | <code>query: 問題: バスケットボールで、NBAの76ersの本拠地はフィラデルフィアですが、bjリーグの89ERSがあるのはどこでしょう? 回答: 仙台市</code> | <code>query: 問題: バスケットボールで、NBAの76ersの本拠地はフィラデルフィアですが、bjリーグの89ERSがあるのはどこでしょう? 回答: 大衡村</code> | | <code>query: 問題: この地を訪れた頼山陽が命名した、大沼、三保の松原とともに新日本三景の一つに数えられている大分県中津市にある渓谷は何でしょう? 回答: 耶馬渓</code> | <code>query: 問題: この地を訪れた頼山陽が命名した、大沼、三保の松原とともに新日本三景の一つに数えられている大分県中津市にある渓谷は何でしょう? 回答: 耶馬渓</code> | <code>query: 問題: この地を訪れた頼山陽が命名した、大沼、三保の松原とともに新日本三景の一つに数えられている大分県中津市にある渓谷は何でしょう? 回答: 東椎屋の滝</code> | | <code>query: 問題: 「朕は国家なり」という言葉で有名な、「太陽王」とも呼ばれたフランスの王は誰でしょう? 回答: るい14よ (ふらんすおう)</code> | <code>query: 問題: 「朕は国家なり」という言葉で有名な、「太陽王」とも呼ばれたフランスの王は誰でしょう? 回答: るい14よ</code> | <code>query: 問題: 「朕は国家なり」という言葉で有名な、「太陽王」とも呼ばれたフランスの王は誰でしょう? 回答: せばすてぃあん・る・ぷれすとる・ど・ゔぉーばん</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

  {
      "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

<details><summary>Click to expand</summary>

—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: adamwtorchfused
—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: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_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: {}

</details>

Training Logs

Epoch	Step	Training Loss
1.0	500	0.1407
2.0	1000	0.0046

Framework Versions

—Python: 3.12.12
—Sentence Transformers: 5.2.3
—Transformers: 5.2.0
—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}
}

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