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vany02/hitachi-defect-classifier

sourceHugging Faceupdated 7mo agoView on Hugging Face

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Model Card

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:

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("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]])

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Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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Training Details

Training Dataset

all-nli-pair

—Dataset: all-nli-pair
—Size: 8,690 training samples
—Columns: <code>anchor</code> and <code>positive</code>
—Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 5 tokens</li><li>mean: 16.27 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 33.58 tokens</li><li>max: 94 tokens</li></ul> |
—Samples: | anchor | positive | |:--------------------------------------------|:---------------------------------------------------------------------------------------------------------| | <code>ATC監視部点検蓋内下部ｽﾎﾟﾝｼﾞはみ出し</code> | <code>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。</code> | | <code>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</code> | <code>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。</code> | | <code>汚物配電盤扉ｽﾄｯﾊﾟｰ裏ｺﾞﾑ上切粉（2㎜×1ヶ）</code> | <code>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。</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

  {
      "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: <code>anchor</code> and <code>positive</code>
—Approximate statistics based on the first 966 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 5 tokens</li><li>mean: 15.85 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 34.0 tokens</li><li>max: 94 tokens</li></ul> |
—Samples: | anchor | positive | |:-------------------------------|:---------------------------------------------------------------------------------------| | <code>運転席用CAﾀﾞｸﾄ取付部歪み</code> | <code>通りが湾曲していたり、ゆがみが発生しているもの</code> | | <code>4位戸閉機ｷｾ走行時異音</code> | <code>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。</code> | | <code>ｻｰﾋﾞｽ機器情報伝送扉下部凹み</code> | <code>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。</code> |
—Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

json

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

Training Hyperparameters

All Hyperparameters

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

—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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
—parallelism_config: None
—deepspeed: None
—label_smoothing_factor: 0.0
—optim: adamwtorchfused
—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: {}

</details>

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

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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