Add new SentenceTransformer model.
Browse files- README.md +506 -164
- model.safetensors +1 -1
README.md
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---
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base_model: colorfulscoop/sbert-base-ja
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---
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#
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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Generates similarity embeddings
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** ja
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- **License:** cc-by-sa-4.0
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- **Finetuned from model [optional]:** colorfulscoop/sbert-base-ja
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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[More Information Needed]
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<!--
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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<!--
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Contact
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---
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base_model: colorfulscoop/sbert-base-ja
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library_name: sentence-transformers
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metrics:
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- cosine_accuracy
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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
|
159 |
+
- type: euclidean_precision
|
160 |
+
value: 0.8095238095238095
|
161 |
+
name: Euclidean Precision
|
162 |
+
- type: euclidean_recall
|
163 |
+
value: 0.9550561797752809
|
164 |
+
name: Euclidean Recall
|
165 |
+
- type: euclidean_ap
|
166 |
+
value: 0.8692698043262699
|
167 |
+
name: Euclidean Ap
|
168 |
+
- type: max_accuracy
|
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+
value: 0.8235294117647058
|
170 |
+
name: Max Accuracy
|
171 |
+
- type: max_accuracy_threshold
|
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+
value: 496.994384765625
|
173 |
+
name: Max Accuracy Threshold
|
174 |
+
- type: max_f1
|
175 |
+
value: 0.8762886597938143
|
176 |
+
name: Max F1
|
177 |
+
- type: max_f1_threshold
|
178 |
+
value: 496.994384765625
|
179 |
+
name: Max F1 Threshold
|
180 |
+
- type: max_precision
|
181 |
+
value: 0.8095238095238095
|
182 |
+
name: Max Precision
|
183 |
+
- type: max_recall
|
184 |
+
value: 0.9550561797752809
|
185 |
+
name: Max Recall
|
186 |
+
- type: max_ap
|
187 |
+
value: 0.8692698043262699
|
188 |
+
name: Max Ap
|
189 |
---
|
190 |
|
191 |
+
# SentenceTransformer based on colorfulscoop/sbert-base-ja
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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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|
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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
|
201 |
+
- **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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+
### Model Sources
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+
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
|
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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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|
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+
### Full Model Architecture
|
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|
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+
```
|
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+
SentenceTransformer(
|
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+
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
|
219 |
+
(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})
|
220 |
+
)
|
221 |
+
```
|
222 |
|
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+
## Usage
|
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|
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+
### Direct Usage (Sentence Transformers)
|
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|
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+
First install the Sentence Transformers library:
|
228 |
|
229 |
+
```bash
|
230 |
+
pip install -U sentence-transformers
|
231 |
+
```
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|
232 |
|
233 |
+
Then you can load this model and run inference.
|
234 |
+
```python
|
235 |
+
from sentence_transformers import SentenceTransformer
|
236 |
|
237 |
+
# Download from the 🤗 Hub
|
238 |
+
model = SentenceTransformer("sentence_transformers_model_id")
|
239 |
+
# Run inference
|
240 |
+
sentences = [
|
241 |
+
'昨日なに作ったの?',
|
242 |
+
'ゆうべはなにをたべたの?',
|
243 |
+
'布袋の中にスカーフは見当たる?',
|
244 |
+
]
|
245 |
+
embeddings = model.encode(sentences)
|
246 |
+
print(embeddings.shape)
|
247 |
+
# [3, 768]
|
248 |
|
249 |
+
# Get the similarity scores for the embeddings
|
250 |
+
similarities = model.similarity(embeddings, embeddings)
|
251 |
+
print(similarities.shape)
|
252 |
+
# [3, 3]
|
253 |
+
```
|
254 |
|
255 |
+
<!--
|
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+
### Direct Usage (Transformers)
|
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|
258 |
+
<details><summary>Click to see the direct usage in Transformers</summary>
|
259 |
|
260 |
+
</details>
|
261 |
+
-->
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|
262 |
|
263 |
+
<!--
|
264 |
+
### Downstream Usage (Sentence Transformers)
|
265 |
|
266 |
+
You can finetune this model on your own dataset.
|
267 |
|
268 |
+
<details><summary>Click to expand</summary>
|
269 |
|
270 |
+
</details>
|
271 |
+
-->
|
272 |
|
273 |
+
<!--
|
274 |
+
### Out-of-Scope Use
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|
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+
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
277 |
+
-->
|
278 |
|
279 |
## Evaluation
|
280 |
|
281 |
+
### Metrics
|
282 |
+
|
283 |
+
#### Binary Classification
|
284 |
+
* Dataset: `custom-arc-semantics-data-jp`
|
285 |
+
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
|
286 |
+
|
287 |
+
| Metric | Value |
|
288 |
+
|:-----------------------------|:-----------|
|
289 |
+
| cosine_accuracy | 0.8088 |
|
290 |
+
| cosine_accuracy_threshold | 0.5397 |
|
291 |
+
| cosine_f1 | 0.866 |
|
292 |
+
| cosine_f1_threshold | 0.5397 |
|
293 |
+
| cosine_precision | 0.8 |
|
294 |
+
| cosine_recall | 0.9438 |
|
295 |
+
| cosine_ap | 0.8673 |
|
296 |
+
| dot_accuracy | 0.8015 |
|
297 |
+
| dot_accuracy_threshold | 335.5763 |
|
298 |
+
| dot_f1 | 0.8526 |
|
299 |
+
| dot_f1_threshold | 305.3472 |
|
300 |
+
| dot_precision | 0.802 |
|
301 |
+
| dot_recall | 0.9101 |
|
302 |
+
| dot_ap | 0.8585 |
|
303 |
+
| manhattan_accuracy | 0.8162 |
|
304 |
+
| manhattan_accuracy_threshold | 496.9944 |
|
305 |
+
| manhattan_f1 | 0.8718 |
|
306 |
+
| manhattan_f1_threshold | 496.9944 |
|
307 |
+
| manhattan_precision | 0.8019 |
|
308 |
+
| manhattan_recall | 0.9551 |
|
309 |
+
| manhattan_ap | 0.8673 |
|
310 |
+
| euclidean_accuracy | 0.8235 |
|
311 |
+
| euclidean_accuracy_threshold | 22.5211 |
|
312 |
+
| euclidean_f1 | 0.8763 |
|
313 |
+
| euclidean_f1_threshold | 22.5211 |
|
314 |
+
| euclidean_precision | 0.8095 |
|
315 |
+
| euclidean_recall | 0.9551 |
|
316 |
+
| euclidean_ap | 0.8693 |
|
317 |
+
| max_accuracy | 0.8235 |
|
318 |
+
| max_accuracy_threshold | 496.9944 |
|
319 |
+
| max_f1 | 0.8763 |
|
320 |
+
| max_f1_threshold | 496.9944 |
|
321 |
+
| max_precision | 0.8095 |
|
322 |
+
| max_recall | 0.9551 |
|
323 |
+
| **max_ap** | **0.8693** |
|
324 |
+
|
325 |
+
<!--
|
326 |
+
## Bias, Risks and Limitations
|
327 |
+
|
328 |
+
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
329 |
+
-->
|
330 |
+
|
331 |
+
<!--
|
332 |
+
### Recommendations
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|
333 |
|
334 |
+
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
335 |
+
-->
|
336 |
|
337 |
+
## Training Details
|
338 |
|
339 |
+
### Training Dataset
|
340 |
+
|
341 |
+
#### csv
|
342 |
+
|
343 |
+
* Dataset: csv
|
344 |
+
* Size: 680 training samples
|
345 |
+
* Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
|
346 |
+
* Approximate statistics based on the first 680 samples:
|
347 |
+
| | text1 | text2 | label |
|
348 |
+
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
|
349 |
+
| type | string | string | int |
|
350 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 8.36 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.07 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~43.01%</li><li>1: ~56.99%</li></ul> |
|
351 |
+
* Samples:
|
352 |
+
| text1 | text2 | label |
|
353 |
+
|:---------------------------|:-------------------------|:---------------|
|
354 |
+
| <code>他の選択肢をちょうだい</code> | <code>どこを探したい?</code> | <code>0</code> |
|
355 |
+
| <code>ビーフシチュー食べた?</code> | <code>ビーフシチュー作った?</code> | <code>1</code> |
|
356 |
+
| <code>なんでしなきゃいけないの?</code> | <code>なんですべきなの?</code> | <code>1</code> |
|
357 |
+
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
|
358 |
+
```json
|
359 |
+
{
|
360 |
+
"scale": 5,
|
361 |
+
"similarity_fct": "pairwise_cos_sim"
|
362 |
+
}
|
363 |
+
```
|
364 |
+
|
365 |
+
### Training Hyperparameters
|
366 |
+
|
367 |
+
#### All Hyperparameters
|
368 |
+
<details><summary>Click to expand</summary>
|
369 |
+
|
370 |
+
- `overwrite_output_dir`: False
|
371 |
+
- `do_predict`: False
|
372 |
+
- `eval_strategy`: no
|
373 |
+
- `prediction_loss_only`: True
|
374 |
+
- `per_device_train_batch_size`: 8
|
375 |
+
- `per_device_eval_batch_size`: 8
|
376 |
+
- `per_gpu_train_batch_size`: None
|
377 |
+
- `per_gpu_eval_batch_size`: None
|
378 |
+
- `gradient_accumulation_steps`: 1
|
379 |
+
- `eval_accumulation_steps`: None
|
380 |
+
- `torch_empty_cache_steps`: None
|
381 |
+
- `learning_rate`: 5e-05
|
382 |
+
- `weight_decay`: 0.0
|
383 |
+
- `adam_beta1`: 0.9
|
384 |
+
- `adam_beta2`: 0.999
|
385 |
+
- `adam_epsilon`: 1e-08
|
386 |
+
- `max_grad_norm`: 1.0
|
387 |
+
- `num_train_epochs`: 3.0
|
388 |
+
- `max_steps`: -1
|
389 |
+
- `lr_scheduler_type`: linear
|
390 |
+
- `lr_scheduler_kwargs`: {}
|
391 |
+
- `warmup_ratio`: 0.0
|
392 |
+
- `warmup_steps`: 0
|
393 |
+
- `log_level`: passive
|
394 |
+
- `log_level_replica`: warning
|
395 |
+
- `log_on_each_node`: True
|
396 |
+
- `logging_nan_inf_filter`: True
|
397 |
+
- `save_safetensors`: True
|
398 |
+
- `save_on_each_node`: False
|
399 |
+
- `save_only_model`: False
|
400 |
+
- `restore_callback_states_from_checkpoint`: False
|
401 |
+
- `no_cuda`: False
|
402 |
+
- `use_cpu`: False
|
403 |
+
- `use_mps_device`: False
|
404 |
+
- `seed`: 42
|
405 |
+
- `data_seed`: None
|
406 |
+
- `jit_mode_eval`: False
|
407 |
+
- `use_ipex`: False
|
408 |
+
- `bf16`: False
|
409 |
+
- `fp16`: False
|
410 |
+
- `fp16_opt_level`: O1
|
411 |
+
- `half_precision_backend`: auto
|
412 |
+
- `bf16_full_eval`: False
|
413 |
+
- `fp16_full_eval`: False
|
414 |
+
- `tf32`: None
|
415 |
+
- `local_rank`: 0
|
416 |
+
- `ddp_backend`: None
|
417 |
+
- `tpu_num_cores`: None
|
418 |
+
- `tpu_metrics_debug`: False
|
419 |
+
- `debug`: []
|
420 |
+
- `dataloader_drop_last`: False
|
421 |
+
- `dataloader_num_workers`: 0
|
422 |
+
- `dataloader_prefetch_factor`: None
|
423 |
+
- `past_index`: -1
|
424 |
+
- `disable_tqdm`: False
|
425 |
+
- `remove_unused_columns`: True
|
426 |
+
- `label_names`: None
|
427 |
+
- `load_best_model_at_end`: False
|
428 |
+
- `ignore_data_skip`: False
|
429 |
+
- `fsdp`: []
|
430 |
+
- `fsdp_min_num_params`: 0
|
431 |
+
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
|
432 |
+
- `fsdp_transformer_layer_cls_to_wrap`: None
|
433 |
+
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
|
434 |
+
- `deepspeed`: None
|
435 |
+
- `label_smoothing_factor`: 0.0
|
436 |
+
- `optim`: adamw_torch
|
437 |
+
- `optim_args`: None
|
438 |
+
- `adafactor`: False
|
439 |
+
- `group_by_length`: False
|
440 |
+
- `length_column_name`: length
|
441 |
+
- `ddp_find_unused_parameters`: None
|
442 |
+
- `ddp_bucket_cap_mb`: None
|
443 |
+
- `ddp_broadcast_buffers`: False
|
444 |
+
- `dataloader_pin_memory`: True
|
445 |
+
- `dataloader_persistent_workers`: False
|
446 |
+
- `skip_memory_metrics`: True
|
447 |
+
- `use_legacy_prediction_loop`: False
|
448 |
+
- `push_to_hub`: False
|
449 |
+
- `resume_from_checkpoint`: None
|
450 |
+
- `hub_model_id`: None
|
451 |
+
- `hub_strategy`: every_save
|
452 |
+
- `hub_private_repo`: False
|
453 |
+
- `hub_always_push`: False
|
454 |
+
- `gradient_checkpointing`: False
|
455 |
+
- `gradient_checkpointing_kwargs`: None
|
456 |
+
- `include_inputs_for_metrics`: False
|
457 |
+
- `eval_do_concat_batches`: True
|
458 |
+
- `fp16_backend`: auto
|
459 |
+
- `push_to_hub_model_id`: None
|
460 |
+
- `push_to_hub_organization`: None
|
461 |
+
- `mp_parameters`:
|
462 |
+
- `auto_find_batch_size`: False
|
463 |
+
- `full_determinism`: False
|
464 |
+
- `torchdynamo`: None
|
465 |
+
- `ray_scope`: last
|
466 |
+
- `ddp_timeout`: 1800
|
467 |
+
- `torch_compile`: False
|
468 |
+
- `torch_compile_backend`: None
|
469 |
+
- `torch_compile_mode`: None
|
470 |
+
- `dispatch_batches`: None
|
471 |
+
- `split_batches`: None
|
472 |
+
- `include_tokens_per_second`: False
|
473 |
+
- `include_num_input_tokens_seen`: False
|
474 |
+
- `neftune_noise_alpha`: None
|
475 |
+
- `optim_target_modules`: None
|
476 |
+
- `batch_eval_metrics`: False
|
477 |
+
- `eval_on_start`: False
|
478 |
+
- `eval_use_gather_object`: False
|
479 |
+
- `batch_sampler`: batch_sampler
|
480 |
+
- `multi_dataset_batch_sampler`: proportional
|
481 |
+
|
482 |
+
</details>
|
483 |
+
|
484 |
+
### Training Logs
|
485 |
+
| Epoch | Step | custom-arc-semantics-data-jp_max_ap |
|
486 |
+
|:-----:|:----:|:-----------------------------------:|
|
487 |
+
| 0 | 0 | 0.8693 |
|
488 |
+
|
489 |
+
|
490 |
+
### Framework Versions
|
491 |
+
- Python: 3.10.14
|
492 |
+
- Sentence Transformers: 3.1.0
|
493 |
+
- Transformers: 4.44.2
|
494 |
+
- PyTorch: 2.4.1+cu121
|
495 |
+
- Accelerate: 0.34.2
|
496 |
+
- Datasets: 2.20.0
|
497 |
+
- Tokenizers: 0.19.1
|
498 |
+
|
499 |
+
## Citation
|
500 |
+
|
501 |
+
### BibTeX
|
502 |
+
|
503 |
+
#### Sentence Transformers
|
504 |
+
```bibtex
|
505 |
+
@inproceedings{reimers-2019-sentence-bert,
|
506 |
+
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
|
507 |
+
author = "Reimers, Nils and Gurevych, Iryna",
|
508 |
+
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
|
509 |
+
month = "11",
|
510 |
+
year = "2019",
|
511 |
+
publisher = "Association for Computational Linguistics",
|
512 |
+
url = "https://arxiv.org/abs/1908.10084",
|
513 |
+
}
|
514 |
+
```
|
515 |
+
|
516 |
+
#### CoSENTLoss
|
517 |
+
```bibtex
|
518 |
+
@online{kexuefm-8847,
|
519 |
+
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
|
520 |
+
author={Su Jianlin},
|
521 |
+
year={2022},
|
522 |
+
month={Jan},
|
523 |
+
url={https://kexue.fm/archives/8847},
|
524 |
+
}
|
525 |
+
```
|
526 |
+
|
527 |
+
<!--
|
528 |
+
## Glossary
|
529 |
+
|
530 |
+
*Clearly define terms in order to be accessible across audiences.*
|
531 |
+
-->
|
532 |
+
|
533 |
+
<!--
|
534 |
+
## Model Card Authors
|
535 |
+
|
536 |
+
*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 |
+
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
543 |
+
-->
|
model.safetensors
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 442491744
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:11caf32cc38b441840798f90707b23c9a5c790edc8fd4aa7181437bf364dcfc9
|
3 |
size 442491744
|