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README.md
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---
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base_model: colorfulscoop/sbert-base-ja
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- cosine_accuracy_threshold
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- cosine_f1
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- cosine_f1_threshold
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- cosine_precision
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- cosine_recall
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- cosine_ap
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- dot_accuracy
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- dot_accuracy_threshold
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- dot_f1
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- dot_f1_threshold
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- dot_precision
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- dot_recall
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- dot_ap
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- manhattan_accuracy
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- manhattan_accuracy_threshold
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- manhattan_f1
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- manhattan_f1_threshold
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- manhattan_precision
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- manhattan_recall
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- manhattan_ap
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- euclidean_accuracy
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- euclidean_accuracy_threshold
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- euclidean_f1
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- euclidean_f1_threshold
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- euclidean_precision
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- euclidean_recall
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- euclidean_ap
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- max_accuracy
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- max_accuracy_threshold
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- max_f1
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- max_f1_threshold
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- max_precision
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- max_recall
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- max_ap
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:4265
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- loss:CosineSimilarityLoss
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widget:
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- source_sentence: 晩飯の時
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sentences:
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- ぶさいく
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- ワゴンにある?
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- スカーフは木材の山の中?
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- source_sentence: どっちも要らない
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sentences:
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- 屋根裏部屋の猫のぬいぐるみ
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- 他は?
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- ジョウロの中にスカーフはある?
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- source_sentence: 自分がやった
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sentences:
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- やりたくない
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- かみゆ
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- どこ?
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- source_sentence: 井戸を使った
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sentences:
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- 井戸はどこ?
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- 何か思い出せることは?
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- 家の外へ行こう
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- source_sentence: 他にはないの?
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sentences:
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- 長老
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- 井戸へ訪れたことがある?
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- ジャック
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model-index:
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- name: SentenceTransformer based on colorfulscoop/sbert-base-ja
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results:
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- task:
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type: binary-classification
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name: Binary Classification
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dataset:
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name: custom arc semantics data jp
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type: custom-arc-semantics-data-jp
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metrics:
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- type: cosine_accuracy
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value: 0.9615745079662605
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name: Cosine Accuracy
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- type: cosine_accuracy_threshold
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value: 0.5470845699310303
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name: Cosine Accuracy Threshold
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- type: cosine_f1
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value: 0.922201138519924
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name: Cosine F1
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- type: cosine_f1_threshold
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value: 0.5470845699310303
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name: Cosine F1 Threshold
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- type: cosine_precision
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value: 0.94921875
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name: Cosine Precision
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- type: cosine_recall
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value: 0.8966789667896679
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name: Cosine Recall
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- type: cosine_ap
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value: 0.9450208727716535
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name: Cosine Ap
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- type: dot_accuracy
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value: 0.9597000937207123
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name: Dot Accuracy
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- type: dot_accuracy_threshold
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value: 291.93450927734375
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name: Dot Accuracy Threshold
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- type: dot_f1
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value: 0.9184060721062619
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name: Dot F1
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- type: dot_f1_threshold
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value: 291.93450927734375
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name: Dot F1 Threshold
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- type: dot_precision
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value: 0.9453125
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name: Dot Precision
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- type: dot_recall
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value: 0.8929889298892989
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name: Dot Recall
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- type: dot_ap
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value: 0.9552306119316933
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name: Dot Ap
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- type: manhattan_accuracy
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value: 0.9625117150890347
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name: Manhattan Accuracy
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- type: manhattan_accuracy_threshold
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value: 464.1397399902344
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name: Manhattan Accuracy Threshold
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- type: manhattan_f1
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value: 0.9233716475095786
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name: Manhattan F1
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- type: manhattan_f1_threshold
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value: 464.1397399902344
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name: Manhattan F1 Threshold
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- type: manhattan_precision
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value: 0.9601593625498008
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name: Manhattan Precision
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- type: manhattan_recall
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value: 0.8892988929889298
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name: Manhattan Recall
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- type: manhattan_ap
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value: 0.9449650812915468
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name: Manhattan Ap
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- type: euclidean_accuracy
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value: 0.9625117150890347
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name: Euclidean Accuracy
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- type: euclidean_accuracy_threshold
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value: 20.998559951782227
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name: Euclidean Accuracy Threshold
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- type: euclidean_f1
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value: 0.9233716475095786
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name: Euclidean F1
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- type: euclidean_f1_threshold
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value: 20.998559951782227
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name: Euclidean F1 Threshold
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- type: euclidean_precision
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value: 0.9601593625498008
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name: Euclidean Precision
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- type: euclidean_recall
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value: 0.8892988929889298
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name: Euclidean Recall
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- type: euclidean_ap
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value: 0.9460565635114587
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name: Euclidean Ap
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- type: max_accuracy
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value: 0.9625117150890347
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name: Max Accuracy
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- type: max_accuracy_threshold
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value: 464.1397399902344
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name: Max Accuracy Threshold
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- type: max_f1
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value: 0.9233716475095786
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name: Max F1
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- type: max_f1_threshold
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value: 464.1397399902344
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name: Max F1 Threshold
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- type: max_precision
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value: 0.9601593625498008
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name: Max Precision
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- type: max_recall
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value: 0.8966789667896679
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name: Max Recall
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- type: max_ap
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value: 0.9552306119316933
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name: Max Ap
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---
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#
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja). 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.
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## Model Details
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) <!-- at revision ecb8a98cd5176719ff7ab0d770a27420118732cf -->
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- **Maximum Sequence Length:** 512 tokens
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- **Output Dimensionality:** 768 tokens
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- **Similarity Function:** Cosine Similarity
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<!-- - **Training Dataset:** Unknown -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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(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})
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)
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```
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pip install -U sentence-transformers
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```
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("sentence_transformers_model_id")
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# Run inference
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sentences = [
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'他にはないの?',
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'ジャック',
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'長老',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 768]
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similarities = model.similarity(embeddings, embeddings)
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print(similarities.shape)
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# [3, 3]
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```
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### Direct Usage (Transformers)
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-->
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### Downstream Usage (Sentence Transformers)
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-->
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<!--
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### Out-of-Scope Use
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-->
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## Evaluation
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### Metrics
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#### Binary Classification
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* Dataset: `custom-arc-semantics-data-jp`
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* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
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| Metric | Value |
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|:-----------------------------|:-----------|
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| cosine_accuracy | 0.9616 |
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| cosine_accuracy_threshold | 0.5471 |
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| cosine_f1 | 0.9222 |
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| cosine_f1_threshold | 0.5471 |
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| cosine_precision | 0.9492 |
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| cosine_recall | 0.8967 |
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| cosine_ap | 0.945 |
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| dot_accuracy | 0.9597 |
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| dot_accuracy_threshold | 291.9345 |
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| dot_f1 | 0.9184 |
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| dot_f1_threshold | 291.9345 |
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| dot_precision | 0.9453 |
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| dot_recall | 0.893 |
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| dot_ap | 0.9552 |
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| manhattan_accuracy | 0.9625 |
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| manhattan_accuracy_threshold | 464.1397 |
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| manhattan_f1 | 0.9234 |
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| manhattan_f1_threshold | 464.1397 |
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| manhattan_precision | 0.9602 |
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| manhattan_recall | 0.8893 |
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| manhattan_ap | 0.945 |
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| euclidean_accuracy | 0.9625 |
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| euclidean_accuracy_threshold | 20.9986 |
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| euclidean_f1 | 0.9234 |
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| euclidean_f1_threshold | 20.9986 |
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| euclidean_precision | 0.9602 |
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| euclidean_recall | 0.8893 |
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| euclidean_ap | 0.9461 |
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| max_accuracy | 0.9625 |
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| max_accuracy_threshold | 464.1397 |
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| max_f1 | 0.9234 |
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| max_f1_threshold | 464.1397 |
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| max_precision | 0.9602 |
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| max_recall | 0.8967 |
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| **max_ap** | **0.9552** |
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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<!--
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### Recommendations
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## Training Details
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### Training
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- `label_names`: None
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- `load_best_model_at_end`: False
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- `fsdp`: []
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- `fsdp_min_num_params`: 0
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- `optim`: adamw_torch
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- `optim_args`: None
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- `adafactor`: False
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- `ddp_bucket_cap_mb`: None
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- `ddp_broadcast_buffers`: False
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475 |
-
- `dataloader_pin_memory`: True
|
476 |
-
- `dataloader_persistent_workers`: False
|
477 |
-
- `skip_memory_metrics`: True
|
478 |
-
- `use_legacy_prediction_loop`: False
|
479 |
-
- `push_to_hub`: False
|
480 |
-
- `resume_from_checkpoint`: None
|
481 |
-
- `hub_model_id`: None
|
482 |
-
- `hub_strategy`: every_save
|
483 |
-
- `hub_private_repo`: False
|
484 |
-
- `hub_always_push`: False
|
485 |
-
- `gradient_checkpointing`: False
|
486 |
-
- `gradient_checkpointing_kwargs`: None
|
487 |
-
- `include_inputs_for_metrics`: False
|
488 |
-
- `eval_do_concat_batches`: True
|
489 |
-
- `fp16_backend`: auto
|
490 |
-
- `push_to_hub_model_id`: None
|
491 |
-
- `push_to_hub_organization`: None
|
492 |
-
- `mp_parameters`:
|
493 |
-
- `auto_find_batch_size`: False
|
494 |
-
- `full_determinism`: False
|
495 |
-
- `torchdynamo`: None
|
496 |
-
- `ray_scope`: last
|
497 |
-
- `ddp_timeout`: 1800
|
498 |
-
- `torch_compile`: False
|
499 |
-
- `torch_compile_backend`: None
|
500 |
-
- `torch_compile_mode`: None
|
501 |
-
- `dispatch_batches`: None
|
502 |
-
- `split_batches`: None
|
503 |
-
- `include_tokens_per_second`: False
|
504 |
-
- `include_num_input_tokens_seen`: False
|
505 |
-
- `neftune_noise_alpha`: None
|
506 |
-
- `optim_target_modules`: None
|
507 |
-
- `batch_eval_metrics`: False
|
508 |
-
- `eval_on_start`: False
|
509 |
-
- `eval_use_gather_object`: False
|
510 |
-
- `batch_sampler`: no_duplicates
|
511 |
-
- `multi_dataset_batch_sampler`: proportional
|
512 |
-
|
513 |
-
</details>
|
514 |
-
|
515 |
-
### Training Logs
|
516 |
-
| Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
|
517 |
-
|:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
|
518 |
-
| 1.0 | 534 | 0.0799 | 0.0400 | 0.9552 |
|
519 |
-
|
520 |
-
|
521 |
-
### Framework Versions
|
522 |
-
- Python: 3.10.14
|
523 |
-
- Sentence Transformers: 3.1.1
|
524 |
-
- Transformers: 4.44.2
|
525 |
-
- PyTorch: 2.4.1+cu121
|
526 |
-
- Accelerate: 0.34.2
|
527 |
-
- Datasets: 2.20.0
|
528 |
-
- Tokenizers: 0.19.1
|
529 |
-
|
530 |
-
## Citation
|
531 |
-
|
532 |
-
### BibTeX
|
533 |
-
|
534 |
-
#### Sentence Transformers
|
535 |
-
```bibtex
|
536 |
-
@inproceedings{reimers-2019-sentence-bert,
|
537 |
-
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
538 |
-
author = "Reimers, Nils and Gurevych, Iryna",
|
539 |
-
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
540 |
-
month = "11",
|
541 |
-
year = "2019",
|
542 |
-
publisher = "Association for Computational Linguistics",
|
543 |
-
url = "https://arxiv.org/abs/1908.10084",
|
544 |
-
}
|
545 |
-
```
|
546 |
-
|
547 |
-
<!--
|
548 |
-
## Glossary
|
549 |
-
|
550 |
-
*Clearly define terms in order to be accessible across audiences.*
|
551 |
-
-->
|
552 |
-
|
553 |
-
<!--
|
554 |
-
## Model Card Authors
|
555 |
-
|
556 |
-
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
557 |
-
-->
|
558 |
-
|
559 |
-
<!--
|
560 |
## Model Card Contact
|
561 |
|
562 |
-
|
563 |
-
-->
|
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|
1 |
---
|
2 |
base_model: colorfulscoop/sbert-base-ja
|
3 |
+
language: ja
|
4 |
+
license: cc-by-sa-4.0
|
5 |
+
model_name: LeoChiuu/sbert-base-ja-arc
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|
6 |
---
|
7 |
|
8 |
+
# Model Card for LeoChiuu/sbert-base-ja-arc
|
9 |
+
|
10 |
+
<!-- Provide a quick summary of what the model is/does. -->
|
11 |
+
|
12 |
|
|
|
13 |
|
14 |
## Model Details
|
15 |
|
16 |
### Model Description
|
|
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|
17 |
|
18 |
+
<!-- Provide a longer summary of what this model is. -->
|
19 |
|
20 |
+
Generates similarity embeddings
|
|
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|
21 |
|
22 |
+
- **Developed by:** [More Information Needed]
|
23 |
+
- **Funded by [optional]:** [More Information Needed]
|
24 |
+
- **Shared by [optional]:** [More Information Needed]
|
25 |
+
- **Model type:** [More Information Needed]
|
26 |
+
- **Language(s) (NLP):** ja
|
27 |
+
- **License:** cc-by-sa-4.0
|
28 |
+
- **Finetuned from model [optional]:** colorfulscoop/sbert-base-ja
|
29 |
|
30 |
+
### Model Sources [optional]
|
|
|
|
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|
|
|
|
|
|
31 |
|
32 |
+
<!-- Provide the basic links for the model. -->
|
33 |
|
34 |
+
- **Repository:** [More Information Needed]
|
35 |
+
- **Paper [optional]:** [More Information Needed]
|
36 |
+
- **Demo [optional]:** [More Information Needed]
|
37 |
|
38 |
+
## Uses
|
39 |
|
40 |
+
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
|
|
|
|
41 |
|
42 |
+
### Direct Use
|
|
|
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|
43 |
|
44 |
+
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
|
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|
45 |
|
46 |
+
[More Information Needed]
|
|
|
|
|
|
|
|
|
47 |
|
48 |
+
### Downstream Use [optional]
|
|
|
49 |
|
50 |
+
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
51 |
|
52 |
+
[More Information Needed]
|
|
|
53 |
|
54 |
+
### Out-of-Scope Use
|
|
|
55 |
|
56 |
+
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
57 |
|
58 |
+
[More Information Needed]
|
59 |
|
60 |
+
## Bias, Risks, and Limitations
|
|
|
61 |
|
62 |
+
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
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|
63 |
|
64 |
+
[More Information Needed]
|
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|
65 |
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|
66 |
### Recommendations
|
67 |
|
68 |
+
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
69 |
+
|
70 |
+
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
|
71 |
+
|
72 |
+
## How to Get Started with the Model
|
73 |
+
|
74 |
+
Use the code below to get started with the model.
|
75 |
+
|
76 |
+
[More Information Needed]
|
77 |
|
78 |
## Training Details
|
79 |
|
80 |
+
### Training Data
|
81 |
+
|
82 |
+
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
|
83 |
+
|
84 |
+
[More Information Needed]
|
85 |
+
|
86 |
+
### Training Procedure
|
87 |
+
|
88 |
+
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
89 |
+
|
90 |
+
#### Preprocessing [optional]
|
91 |
+
|
92 |
+
[More Information Needed]
|
93 |
+
|
94 |
+
|
95 |
+
#### Training Hyperparameters
|
96 |
+
|
97 |
+
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
98 |
+
|
99 |
+
#### Speeds, Sizes, Times [optional]
|
100 |
+
|
101 |
+
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
102 |
+
|
103 |
+
[More Information Needed]
|
104 |
+
|
105 |
+
## Evaluation
|
106 |
+
|
107 |
+
<!-- This section describes the evaluation protocols and provides the results. -->
|
108 |
+
|
109 |
+
### Testing Data, Factors & Metrics
|
110 |
+
|
111 |
+
#### Testing Data
|
112 |
+
|
113 |
+
<!-- This should link to a Dataset Card if possible. -->
|
114 |
+
|
115 |
+
[More Information Needed]
|
116 |
+
|
117 |
+
#### Factors
|
118 |
+
|
119 |
+
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
120 |
+
|
121 |
+
[More Information Needed]
|
122 |
+
|
123 |
+
#### Metrics
|
124 |
+
|
125 |
+
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
126 |
+
|
127 |
+
[More Information Needed]
|
128 |
+
|
129 |
+
### Results
|
130 |
+
|
131 |
+
[More Information Needed]
|
132 |
+
|
133 |
+
#### Summary
|
134 |
+
|
135 |
+
|
136 |
+
|
137 |
+
## Model Examination [optional]
|
138 |
+
|
139 |
+
<!-- Relevant interpretability work for the model goes here -->
|
140 |
+
|
141 |
+
[More Information Needed]
|
142 |
+
|
143 |
+
## Environmental Impact
|
144 |
+
|
145 |
+
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
146 |
+
|
147 |
+
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
|
148 |
+
|
149 |
+
- **Hardware Type:** [More Information Needed]
|
150 |
+
- **Hours used:** [More Information Needed]
|
151 |
+
- **Cloud Provider:** [More Information Needed]
|
152 |
+
- **Compute Region:** [More Information Needed]
|
153 |
+
- **Carbon Emitted:** [More Information Needed]
|
154 |
+
|
155 |
+
## Technical Specifications [optional]
|
156 |
+
|
157 |
+
### Model Architecture and Objective
|
158 |
+
|
159 |
+
[More Information Needed]
|
160 |
+
|
161 |
+
### Compute Infrastructure
|
162 |
+
|
163 |
+
[More Information Needed]
|
164 |
+
|
165 |
+
#### Hardware
|
166 |
+
|
167 |
+
[More Information Needed]
|
168 |
+
|
169 |
+
#### Software
|
170 |
+
|
171 |
+
[More Information Needed]
|
172 |
+
|
173 |
+
## Citation [optional]
|
174 |
+
|
175 |
+
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
176 |
+
|
177 |
+
**BibTeX:**
|
178 |
+
|
179 |
+
[More Information Needed]
|
180 |
+
|
181 |
+
**APA:**
|
182 |
+
|
183 |
+
[More Information Needed]
|
184 |
+
|
185 |
+
## Glossary [optional]
|
186 |
+
|
187 |
+
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
188 |
+
|
189 |
+
[More Information Needed]
|
190 |
+
|
191 |
+
## More Information [optional]
|
192 |
+
|
193 |
+
[More Information Needed]
|
194 |
+
|
195 |
+
## Model Card Authors [optional]
|
196 |
+
|
197 |
+
[More Information Needed]
|
198 |
+
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|
199 |
## Model Card Contact
|
200 |
|
201 |
+
[More Information Needed]
|
|