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Add new SentenceTransformer model.

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  ---
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  base_model: colorfulscoop/sbert-base-ja
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- language: ja
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- license: cc-by-sa-4.0
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- model_name: LeoChiuu/sbert-base-ja-arc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for LeoChiuu/sbert-base-ja-arc
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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  ## Model Details
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  ### Model Description
 
 
 
 
 
 
 
 
 
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- <!-- Provide a longer summary of what this model is. -->
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-
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- Generates similarity embeddings
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-
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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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-
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- ### Model Sources [optional]
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-
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- <!-- Provide the basic links for the model. -->
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-
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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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-
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- ## Uses
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-
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
 
 
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
 
 
 
 
 
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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-
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
 
 
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
 
 
 
 
 
 
 
 
 
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- [More Information Needed]
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-
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- ### Recommendations
 
 
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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-
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- Use the code below to get started with the model.
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-
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- [More Information Needed]
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-
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- ## Training Details
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- ### Training Data
 
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- <!-- 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. -->
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- [More Information Needed]
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- ### Training Procedure
 
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-
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- #### Preprocessing [optional]
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-
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- [More Information Needed]
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-
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-
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- #### Training Hyperparameters
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-
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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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-
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- #### Speeds, Sizes, Times [optional]
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-
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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103
- [More Information Needed]
 
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105
  ## Evaluation
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107
- <!-- This section describes the evaluation protocols and provides the results. -->
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-
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- ### Testing Data, Factors & Metrics
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-
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- #### Testing Data
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-
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- <!-- This should link to a Dataset Card if possible. -->
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-
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- [More Information Needed]
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-
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- #### Factors
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-
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-
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- [More Information Needed]
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-
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- #### Metrics
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-
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-
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- [More Information Needed]
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-
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- ### Results
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-
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- [More Information Needed]
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-
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- #### Summary
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-
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-
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-
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- ## Model Examination [optional]
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-
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- <!-- Relevant interpretability work for the model goes here -->
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-
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- [More Information Needed]
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-
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- ## Environmental Impact
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-
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-
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- 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).
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-
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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-
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- ## Technical Specifications [optional]
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-
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- ### Model Architecture and Objective
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-
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- [More Information Needed]
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-
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- ### Compute Infrastructure
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-
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- [More Information Needed]
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-
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- #### Hardware
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-
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- [More Information Needed]
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-
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- #### Software
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-
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- [More Information Needed]
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-
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- ## Citation [optional]
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-
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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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-
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- **BibTeX:**
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-
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- [More Information Needed]
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-
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- **APA:**
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-
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- [More Information Needed]
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-
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- ## Glossary [optional]
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-
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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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-
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- [More Information Needed]
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-
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- ## More Information [optional]
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-
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- [More Information Needed]
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- ## Model Card Authors [optional]
 
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- [More Information Needed]
198
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
199
  ## Model Card Contact
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201
- [More Information Needed]
 
 
1
  ---
2
  base_model: colorfulscoop/sbert-base-ja
3
+ library_name: sentence-transformers
4
+ metrics:
5
+ - cosine_accuracy
6
+ - cosine_accuracy_threshold
7
+ - cosine_f1
8
+ - cosine_f1_threshold
9
+ - cosine_precision
10
+ - cosine_recall
11
+ - cosine_ap
12
+ - dot_accuracy
13
+ - dot_accuracy_threshold
14
+ - dot_f1
15
+ - dot_f1_threshold
16
+ - dot_precision
17
+ - dot_recall
18
+ - dot_ap
19
+ - manhattan_accuracy
20
+ - manhattan_accuracy_threshold
21
+ - manhattan_f1
22
+ - manhattan_f1_threshold
23
+ - manhattan_precision
24
+ - manhattan_recall
25
+ - manhattan_ap
26
+ - euclidean_accuracy
27
+ - euclidean_accuracy_threshold
28
+ - euclidean_f1
29
+ - euclidean_f1_threshold
30
+ - euclidean_precision
31
+ - euclidean_recall
32
+ - euclidean_ap
33
+ - max_accuracy
34
+ - max_accuracy_threshold
35
+ - max_f1
36
+ - max_f1_threshold
37
+ - max_precision
38
+ - max_recall
39
+ - max_ap
40
+ pipeline_tag: sentence-similarity
41
+ tags:
42
+ - sentence-transformers
43
+ - sentence-similarity
44
+ - feature-extraction
45
+ - generated_from_trainer
46
+ - dataset_size:680
47
+ - loss:CoSENTLoss
48
+ widget:
49
+ - source_sentence: どっちをさがせばいい?
50
+ sentences:
51
+ - 木材の山の中にスカーフはある?
52
+ - はじめにどっちをさがせばいい?
53
+ - チキンヌードル食べた?
54
+ - source_sentence: あの木に引っかかってるやつ
55
+ sentences:
56
+ - 夜ご飯を作る前
57
+ - 花壇の中にスカーフはある?
58
+ - 信用できない
59
+ - source_sentence: 猫好きな人
60
+ sentences:
61
+ - カーテンが風に吹かれているから
62
+ - 猫好き
63
+ - 他にはある?
64
+ - source_sentence: 家の外
65
+ sentences:
66
+ - 魔法の残り香
67
+ - 欲しくない
68
+ - 家の外へ行こう
69
+ - source_sentence: 昨日なに作ったの?
70
+ sentences:
71
+ - 布袋の中にスカーフは見当たる?
72
+ - 昨日なに作ったの?
73
+ - ゆうべはなにをたべたの?
74
+ model-index:
75
+ - name: SentenceTransformer based on colorfulscoop/sbert-base-ja
76
+ results:
77
+ - task:
78
+ type: binary-classification
79
+ name: Binary Classification
80
+ dataset:
81
+ name: custom arc semantics data jp
82
+ type: custom-arc-semantics-data-jp
83
+ metrics:
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+ - type: cosine_accuracy
85
+ 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
93
+ - type: cosine_f1_threshold
94
+ 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
98
+ name: Cosine Precision
99
+ - type: cosine_recall
100
+ 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
105
+ - type: dot_accuracy
106
+ value: 0.8014705882352942
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+ name: Dot Accuracy
108
+ - 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
118
+ value: 0.801980198019802
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+ name: Dot Precision
120
+ - 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
129
+ - type: manhattan_accuracy_threshold
130
+ 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
136
+ value: 496.994384765625
137
+ name: Manhattan F1 Threshold
138
+ - type: manhattan_precision
139
+ value: 0.8018867924528302
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+ name: Manhattan Precision
141
+ - type: manhattan_recall
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+ value: 0.9550561797752809
143
+ name: Manhattan Recall
144
+ - type: manhattan_ap
145
+ value: 0.8672919211890922
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+ name: Manhattan Ap
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+ - type: euclidean_accuracy
148
+ value: 0.8235294117647058
149
+ name: Euclidean Accuracy
150
+ - 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
154
+ value: 0.8762886597938143
155
+ name: Euclidean F1
156
+ - type: euclidean_f1_threshold
157
+ 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
169
+ value: 0.8235294117647058
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+ name: Max Accuracy
171
+ - type: max_accuracy_threshold
172
+ 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
 
 
 
192
 
193
+ 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.
194
 
195
  ## Model Details
196
 
197
  ### Model Description
198
+ - **Model Type:** Sentence Transformer
199
+ - **Base model:** [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) <!-- at revision ecb8a98cd5176719ff7ab0d770a27420118732cf -->
200
+ - **Maximum Sequence Length:** 512 tokens
201
+ - **Output Dimensionality:** 768 tokens
202
+ - **Similarity Function:** Cosine Similarity
203
+ - **Training Dataset:**
204
+ - csv
205
+ <!-- - **Language:** Unknown -->
206
+ <!-- - **License:** Unknown -->
207
 
208
+ ### Model Sources
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
209
 
210
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
211
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
212
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
213
 
214
+ ### Full Model Architecture
215
 
216
+ ```
217
+ SentenceTransformer(
218
+ (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
 
223
+ ## Usage
224
 
225
+ ### Direct Usage (Sentence Transformers)
226
 
227
+ First install the Sentence Transformers library:
228
 
229
+ ```bash
230
+ pip install -U sentence-transformers
231
+ ```
 
 
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
+ <!--
256
+ ### Direct Usage (Transformers)
257
 
258
+ <details><summary>Click to see the direct usage in Transformers</summary>
259
 
260
+ </details>
261
+ -->
 
 
 
 
 
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
 
 
 
 
 
 
 
 
 
 
 
 
275
 
276
+ *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
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ {
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+ "scale": 5,
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+ "similarity_fct": "pairwise_cos_sim"
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+ }
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+ ```
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+
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+ ### Training Hyperparameters
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+
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+ #### All Hyperparameters
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+ <details><summary>Click to expand</summary>
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+
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+ - `overwrite_output_dir`: False
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+ - `do_predict`: False
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+ - `eval_strategy`: no
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+ - `prediction_loss_only`: True
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+ - `per_device_train_batch_size`: 8
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+ - `per_device_eval_batch_size`: 8
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+ - `per_gpu_train_batch_size`: None
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+ - `per_gpu_eval_batch_size`: None
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+ - `gradient_accumulation_steps`: 1
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+ - `eval_accumulation_steps`: None
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+ - `torch_empty_cache_steps`: None
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+ - `learning_rate`: 5e-05
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+ - `weight_decay`: 0.0
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+ - `adam_beta1`: 0.9
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+ - `adam_beta2`: 0.999
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+ - `adam_epsilon`: 1e-08
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+ - `max_grad_norm`: 1.0
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+ - `num_train_epochs`: 3.0
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+ - `max_steps`: -1
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+ - `lr_scheduler_type`: linear
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+ - `lr_scheduler_kwargs`: {}
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+ - `warmup_ratio`: 0.0
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+ - `warmup_steps`: 0
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+ - `log_level`: passive
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+ - `log_level_replica`: warning
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+ - `log_on_each_node`: True
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+ - `logging_nan_inf_filter`: True
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+ - `save_safetensors`: True
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+ - `save_on_each_node`: False
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+ - `save_only_model`: False
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+ - `restore_callback_states_from_checkpoint`: False
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+ - `no_cuda`: False
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+ - `use_cpu`: False
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+ - `use_mps_device`: False
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+ - `seed`: 42
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+ - `data_seed`: None
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+ - `jit_mode_eval`: False
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+ - `use_ipex`: False
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+ - `bf16`: False
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+ - `fp16`: False
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+ - `fp16_opt_level`: O1
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+ - `half_precision_backend`: auto
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+ - `bf16_full_eval`: False
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+ - `fp16_full_eval`: False
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+ - `tf32`: None
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+ - `local_rank`: 0
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+ - `ddp_backend`: None
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+ - `tpu_num_cores`: None
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+ - `tpu_metrics_debug`: False
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+ - `debug`: []
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+ - `dataloader_drop_last`: False
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+ - `dataloader_num_workers`: 0
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+ - `dataloader_prefetch_factor`: None
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+ - `past_index`: -1
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+ - `disable_tqdm`: False
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+ - `remove_unused_columns`: True
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+ - `label_names`: None
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+ - `load_best_model_at_end`: False
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+ - `ignore_data_skip`: False
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+ - `fsdp`: []
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+ - `fsdp_min_num_params`: 0
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+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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+ - `fsdp_transformer_layer_cls_to_wrap`: None
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+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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+ - `deepspeed`: None
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+ - `label_smoothing_factor`: 0.0
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+ - `optim`: adamw_torch
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+ - `optim_args`: None
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+ - `adafactor`: False
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+ - `group_by_length`: False
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+ - `length_column_name`: length
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+ - `ddp_find_unused_parameters`: None
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+ - `ddp_bucket_cap_mb`: None
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+ - `ddp_broadcast_buffers`: False
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+ - `dataloader_pin_memory`: True
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+ - `dataloader_persistent_workers`: False
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+ - `skip_memory_metrics`: True
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+ - `use_legacy_prediction_loop`: False
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+ - `push_to_hub`: False
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+ - `resume_from_checkpoint`: None
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+ - `hub_model_id`: None
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+ - `hub_strategy`: every_save
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+ - `hub_private_repo`: False
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+ - `hub_always_push`: False
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+ - `gradient_checkpointing`: False
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+ - `gradient_checkpointing_kwargs`: None
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+ - `include_inputs_for_metrics`: False
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+ - `eval_do_concat_batches`: True
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+ - `fp16_backend`: auto
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+ - `push_to_hub_model_id`: None
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+ - `push_to_hub_organization`: None
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+ - `mp_parameters`:
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+ - `auto_find_batch_size`: False
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+ - `full_determinism`: False
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+ - `torchdynamo`: None
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+ - `ray_scope`: last
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+ - `ddp_timeout`: 1800
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+ - `torch_compile`: False
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+ - `torch_compile_backend`: None
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+ - `torch_compile_mode`: None
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+ - `dispatch_batches`: None
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+ - `split_batches`: None
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+ - `include_tokens_per_second`: False
473
+ - `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
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+ - `eval_use_gather_object`: False
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+ - `batch_sampler`: batch_sampler
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+ - `multi_dataset_batch_sampler`: proportional
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+
482
+ </details>
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+
484
+ ### Training Logs
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+ | Epoch | Step | custom-arc-semantics-data-jp_max_ap |
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+ |:-----:|:----:|:-----------------------------------:|
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+ | 0 | 0 | 0.8693 |
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+
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+
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+ ### Framework Versions
491
+ - Python: 3.10.14
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+ - Sentence Transformers: 3.1.0
493
+ - Transformers: 4.44.2
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+ - PyTorch: 2.4.1+cu121
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+ - Accelerate: 0.34.2
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+ - Datasets: 2.20.0
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+ - Tokenizers: 0.19.1
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+
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+ ## Citation
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+
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+ ### BibTeX
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+
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+ #### Sentence Transformers
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+ ```bibtex
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+ @inproceedings{reimers-2019-sentence-bert,
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+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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+ author = "Reimers, Nils and Gurevych, Iryna",
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+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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+ month = "11",
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+ year = "2019",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://arxiv.org/abs/1908.10084",
513
+ }
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+ ```
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+
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+ #### CoSENTLoss
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+ ```bibtex
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+ @online{kexuefm-8847,
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+ title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
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+ author={Su Jianlin},
521
+ year={2022},
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+ month={Jan},
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+ url={https://kexue.fm/archives/8847},
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+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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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.*
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+ -->
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+
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+ <!--
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  ## Model Card Contact
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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