vany02/hitachi-defect-classifier

SentenceTransformer

This is a sentence-transformers model trained on the all-nli-pair dataset. It maps sentences & paragraphs to a 768-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
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • all-nli-pair

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': 128, 'do_lower_case': False, 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'word_embedding_dimension': 768, '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})
)

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("vany02/hitachi-defect-classifier")
# Run inference
sentences = [
    '無線機器室扉下部 巾木 取付不良',
    '段付きが発生しているもの。段付きとスキマが同時発生しているもの、ﾊﾟﾈﾙ等貼付物以外の浮きも該当とする。',
    '調整不良(501)や干渉(104)、締結不良(401)の結果、走行時や動作確認で異音(ビビり音、キシミ音、セリ音、ｶﾞﾀﾂｷ音)が確認されたもの。',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6393, 0.2329],
#         [0.6393, 1.0000, 0.2656],
#         [0.2329, 0.2656, 1.0000]])

Training Details

Training Dataset

all-nli-pair

• Dataset: all-nli-pair
• Size: 8,690 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 5 tokens mean: 16.27 tokens max: 50 tokens	min: 13 tokens mean: 33.58 tokens max: 94 tokens

• Samples:

  anchor	positive
  ATC監視部点検蓋内下部 ｽﾎﾟﾝｼﾞはみ出し	艤装作業時の調整不足による不良、例えばスポンジ、断熱材位置など。ただし、シール切れなどは「111 シール不良」に分類する。また可動部の調整不良は「502可動部の調整不良」に分類する。
  2位空き缶ｺﾞﾐ箱扉内 ﾋﾞｽ締結点のｺﾞﾑ板切欠していない	作業漏れ、取付もれがあった場合。ただし間違った作業を行っている場合は「X02作業間違い」に分類する。
  汚物配電盤扉ｽﾄｯﾊﾟｰ裏ｺﾞﾑ上 切粉（2㎜×1ヶ）	切粉・素線など伝導性の高い異物。この場合はサイズおよび個数を記録する。

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Evaluation Dataset

all-nli-pair

• Dataset: all-nli-pair
• Size: 966 evaluation samples
• Columns: anchor and positive
• Approximate statistics based on the first 966 samples:

  	anchor	positive
  type	string	string
  details	min: 5 tokens mean: 15.85 tokens max: 43 tokens	min: 13 tokens mean: 34.0 tokens max: 94 tokens

• Samples:

  anchor	positive
  運転席用CAﾀﾞｸﾄ取付部歪み	通りが湾曲していたり、ゆがみが発生しているもの
  4位戸閉機ｷｾ 走行時異音	調整不良(501)や干渉(104)、締結不良(401)の結果、走行時や動作確認で異音(ビビり音、キシミ音、セリ音、ｶﾞﾀﾂｷ音)が確認されたもの。
  ｻｰﾋﾞｽ機器情報伝送扉下部 凹み	打痕・凹凸が許容できないサイズのもの。ただし、メーカ要因の場合は「701メーカ/部品不良」に分類する。

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

All Hyperparameters

Click to expand

• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 8
• per_device_eval_batch_size: 8
• 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: 3.0
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_ratio: None
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• enable_jit_checkpoint: False
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• use_cpu: False
• seed: 42
• data_seed: None
• bf16: False
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: -1
• ddp_backend: None
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• group_by_length: False
• length_column_name: length
• project: huggingface
• trackio_space_id: trackio
• 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
• 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_for_metrics: []
• eval_do_concat_batches: True
• auto_find_batch_size: False
• full_determinism: False
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_num_input_tokens_seen: no
• 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: True
• use_cache: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.4600	500	0.7279
0.9200	1000	0.6938
1.3799	1500	0.6359
1.8399	2000	0.5952
2.2999	2500	0.5535
2.7599	3000	0.5392

Framework Versions

• Python: 3.12.12
• Sentence Transformers: 5.2.2
• Transformers: 5.0.0
• PyTorch: 2.9.0+cu126
• 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}
}