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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:680
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- loss:CoSENTLoss
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widget:
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- source_sentence: どっちをさがせばいい?
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sentences:
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- 木材の山の中にスカーフはある?
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- はじめにどっちをさがせばいい?
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- チキンヌードル食べた?
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- source_sentence: あの木に引っかかってるやつ
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sentences:
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- 夜ご飯を作る前
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- 花壇の中にスカーフはある?
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- 信用できない
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- source_sentence: 猫好きな人
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sentences:
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- カーテンが風に吹かれているから
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- 猫好き
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- 他にはある?
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- source_sentence: 家の外
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sentences:
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- 魔法の残り香
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- 欲しくない
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- 家の外へ行こう
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- source_sentence: 昨日なに作ったの?
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sentences:
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- 布袋の中にスカーフは見当たる?
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- 昨日なに作ったの?
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- ゆうべはなにをたべたの?
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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: 0.8088235294117647
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name: Cosine Accuracy
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- type: cosine_accuracy_threshold
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value: 0.5396817326545715
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name: Cosine Accuracy Threshold
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- type: cosine_f1
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value: 0.8659793814432991
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name: Cosine F1
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- type: cosine_f1_threshold
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value: 0.5396817326545715
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name: Cosine F1 Threshold
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- type: cosine_precision
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value: 0.8
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name: Cosine Precision
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- type: cosine_recall
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value: 0.9438202247191011
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name: Cosine Recall
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- type: cosine_ap
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value: 0.8673399071218862
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name: Cosine Ap
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- type: dot_accuracy
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value: 0.8014705882352942
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name: Dot Accuracy
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- type: dot_accuracy_threshold
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value: 335.5762634277344
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name: Dot Accuracy Threshold
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- type: dot_f1
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value: 0.8526315789473684
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name: Dot F1
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- type: dot_f1_threshold
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value: 305.34722900390625
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name: Dot F1 Threshold
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- type: dot_precision
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value: 0.801980198019802
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name: Dot Precision
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- type: dot_recall
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value: 0.9101123595505618
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name: Dot Recall
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- type: dot_ap
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value: 0.8584929148669156
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name: Dot Ap
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- type: manhattan_accuracy
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value: 0.8161764705882353
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name: Manhattan Accuracy
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- type: manhattan_accuracy_threshold
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value: 496.994384765625
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name: Manhattan Accuracy Threshold
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- type: manhattan_f1
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value: 0.8717948717948718
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name: Manhattan F1
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- type: manhattan_f1_threshold
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value: 496.994384765625
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name: Manhattan F1 Threshold
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- type: manhattan_precision
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value: 0.8018867924528302
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name: Manhattan Precision
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- type: manhattan_recall
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value: 0.9550561797752809
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name: Manhattan Recall
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- type: manhattan_ap
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value: 0.8672919211890922
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name: Manhattan Ap
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- type: euclidean_accuracy
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value: 0.8235294117647058
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name: Euclidean Accuracy
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- type: euclidean_accuracy_threshold
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value: 22.521053314208984
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name: Euclidean Accuracy Threshold
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- type: euclidean_f1
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value: 0.8762886597938143
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name: Euclidean F1
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- type: euclidean_f1_threshold
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value: 22.521053314208984
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name: Euclidean F1 Threshold
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- type: euclidean_precision
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value: 0.8095238095238095
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name: Euclidean Precision
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- type: euclidean_recall
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value: 0.9550561797752809
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name: Euclidean Recall
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- type: euclidean_ap
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value: 0.8692698043262699
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name: Euclidean Ap
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- type: max_accuracy
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value: 0.8235294117647058
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name: Max Accuracy
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- type: max_accuracy_threshold
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value: 496.994384765625
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name: Max Accuracy Threshold
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- type: max_f1
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value: 0.8762886597938143
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name: Max F1
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- type: max_f1_threshold
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value: 496.994384765625
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name: Max F1 Threshold
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- type: max_precision
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value: 0.8095238095238095
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name: Max Precision
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- type: max_recall
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value: 0.9550561797752809
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name: Max Recall
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- type: max_ap
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value: 0.8692698043262699
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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) on the csv 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.
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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:**
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- csv
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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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'昨日なに作ったの?',
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'ゆうべはなにをたべたの?',
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'布袋の中にスカーフは見当たる?',
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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 | 0.8088 |
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| cosine_accuracy_threshold | 0.5397 |
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| cosine_f1 | 0.866 |
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| cosine_f1_threshold | 0.5397 |
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| cosine_precision | 0.8 |
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| cosine_recall | 0.9438 |
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| cosine_ap | 0.8673 |
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| dot_accuracy | 0.8015 |
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| dot_accuracy_threshold | 335.5763 |
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| dot_f1 | 0.8526 |
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| dot_f1_threshold | 305.3472 |
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| dot_precision | 0.802 |
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| dot_recall | 0.9101 |
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| dot_ap | 0.8585 |
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| manhattan_accuracy | 0.8162 |
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| manhattan_accuracy_threshold | 496.9944 |
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| manhattan_f1 | 0.8718 |
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| manhattan_f1_threshold | 496.9944 |
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| manhattan_precision | 0.8019 |
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| manhattan_recall | 0.9551 |
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| manhattan_ap | 0.8673 |
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| euclidean_accuracy | 0.8235 |
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| euclidean_accuracy_threshold | 22.5211 |
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| euclidean_f1 | 0.8763 |
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| euclidean_f1_threshold | 22.5211 |
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| euclidean_precision | 0.8095 |
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| euclidean_recall | 0.9551 |
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| euclidean_ap | 0.8693 |
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| max_accuracy | 0.8235 |
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| max_accuracy_threshold | 496.9944 |
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| max_f1 | 0.8763 |
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| max_f1_threshold | 496.9944 |
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| max_precision | 0.8095 |
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| max_recall | 0.9551 |
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| **max_ap** | **0.8693** |
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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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- `push_to_hub_organization`: None
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- `mp_parameters`:
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- `ray_scope`: last
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- `dispatch_batches`: None
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- `split_batches`: None
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- `include_tokens_per_second`: False
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- `include_num_input_tokens_seen`: False
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- `neftune_noise_alpha`: None
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- `optim_target_modules`: None
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- `batch_eval_metrics`: False
|
477 |
-
- `eval_on_start`: False
|
478 |
-
- `eval_use_gather_object`: False
|
479 |
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- `batch_sampler`: batch_sampler
|
480 |
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- `multi_dataset_batch_sampler`: proportional
|
481 |
-
|
482 |
-
</details>
|
483 |
-
|
484 |
-
### Training Logs
|
485 |
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| Epoch | Step | custom-arc-semantics-data-jp_max_ap |
|
486 |
-
|:-----:|:----:|:-----------------------------------:|
|
487 |
-
| 0 | 0 | 0.8693 |
|
488 |
-
|
489 |
-
|
490 |
-
### Framework Versions
|
491 |
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- Python: 3.10.14
|
492 |
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- Sentence Transformers: 3.1.0
|
493 |
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- Transformers: 4.44.2
|
494 |
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- PyTorch: 2.4.1+cu121
|
495 |
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- Accelerate: 0.34.2
|
496 |
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- Datasets: 2.20.0
|
497 |
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- Tokenizers: 0.19.1
|
498 |
-
|
499 |
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## Citation
|
500 |
-
|
501 |
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### BibTeX
|
502 |
-
|
503 |
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#### Sentence Transformers
|
504 |
-
```bibtex
|
505 |
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@inproceedings{reimers-2019-sentence-bert,
|
506 |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
507 |
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author = "Reimers, Nils and Gurevych, Iryna",
|
508 |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
509 |
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month = "11",
|
510 |
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year = "2019",
|
511 |
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publisher = "Association for Computational Linguistics",
|
512 |
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url = "https://arxiv.org/abs/1908.10084",
|
513 |
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}
|
514 |
-
```
|
515 |
-
|
516 |
-
#### CoSENTLoss
|
517 |
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```bibtex
|
518 |
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@online{kexuefm-8847,
|
519 |
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title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
|
520 |
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author={Su Jianlin},
|
521 |
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year={2022},
|
522 |
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month={Jan},
|
523 |
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url={https://kexue.fm/archives/8847},
|
524 |
-
}
|
525 |
-
```
|
526 |
-
|
527 |
-
<!--
|
528 |
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## Glossary
|
529 |
-
|
530 |
-
*Clearly define terms in order to be accessible across audiences.*
|
531 |
-
-->
|
532 |
-
|
533 |
-
<!--
|
534 |
-
## Model Card Authors
|
535 |
-
|
536 |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
537 |
-
-->
|
538 |
-
|
539 |
-
<!--
|
540 |
## Model Card Contact
|
541 |
|
542 |
-
|
543 |
-
-->
|
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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 |
|
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|
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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|
|
|
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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]
|
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|
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|
|
|
|
|
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]
|
|