LeoChiuu commited on
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Add new SentenceTransformer model.

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README.md CHANGED
@@ -1,201 +1,526 @@
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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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- [More Information Needed]
 
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  ## Evaluation
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- <!-- 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]
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  ## Model Card Contact
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- [More Information Needed]
 
 
1
  ---
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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:53
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+ - loss:OnlineContrastiveLoss
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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
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+ name: Cosine Accuracy
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+ - type: cosine_accuracy_threshold
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+ value: -1
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+ name: Cosine Accuracy Threshold
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+ - type: cosine_f1
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+ value: 0
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+ name: Cosine F1
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+ - type: cosine_f1_threshold
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+ value: 0
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+ name: Cosine F1 Threshold
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+ - type: cosine_precision
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+ value: 0
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+ name: Cosine Precision
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+ - type: cosine_recall
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+ value: 0
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+ name: Cosine Recall
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+ - type: cosine_ap
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+ value: -0.0
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+ name: Cosine Ap
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+ - type: dot_accuracy
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+ value: 0
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+ name: Dot Accuracy
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+ - type: dot_accuracy_threshold
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+ value: -1
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+ name: Dot Accuracy Threshold
85
+ - type: dot_f1
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+ value: 0
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+ name: Dot F1
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+ - type: dot_f1_threshold
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+ value: 0
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+ name: Dot F1 Threshold
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+ - type: dot_precision
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+ value: 0
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+ name: Dot Precision
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+ - type: dot_recall
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+ value: 0
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+ name: Dot Recall
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+ - type: dot_ap
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+ value: -0.0
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+ name: Dot Ap
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+ - type: manhattan_accuracy
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+ value: 0
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+ name: Manhattan Accuracy
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+ - type: manhattan_accuracy_threshold
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+ value: -1
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+ name: Manhattan Accuracy Threshold
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+ - type: manhattan_f1
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+ value: 0
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+ name: Manhattan F1
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+ - type: manhattan_f1_threshold
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+ value: 0
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+ name: Manhattan F1 Threshold
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+ - type: manhattan_precision
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+ value: 0
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+ name: Manhattan Precision
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+ - type: manhattan_recall
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+ value: 0
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+ name: Manhattan Recall
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+ - type: manhattan_ap
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+ value: -0.0
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+ name: Manhattan Ap
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+ - type: euclidean_accuracy
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+ value: 0
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+ name: Euclidean Accuracy
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+ - type: euclidean_accuracy_threshold
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+ value: -1
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+ name: Euclidean Accuracy Threshold
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+ - type: euclidean_f1
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+ value: 0
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+ name: Euclidean F1
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+ - type: euclidean_f1_threshold
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+ value: 0
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+ name: Euclidean F1 Threshold
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+ - type: euclidean_precision
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+ value: 0
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+ name: Euclidean Precision
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+ - type: euclidean_recall
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+ value: 0
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+ name: Euclidean Recall
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+ - type: euclidean_ap
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+ value: -0.0
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+ name: Euclidean Ap
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+ - type: max_accuracy
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+ value: 0
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+ name: Max Accuracy
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+ - type: max_accuracy_threshold
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+ value: -1
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+ name: Max Accuracy Threshold
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+ - type: max_f1
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+ value: 0
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+ name: Max F1
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+ - type: max_f1_threshold
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+ value: 0
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+ name: Max F1 Threshold
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+ - type: max_precision
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+ value: 0
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+ name: Max Precision
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+ - type: max_recall
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+ value: 0
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+ name: Max Recall
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+ - type: max_ap
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+ value: -0.0
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+ name: Max Ap
163
  ---
164
 
165
+ # SentenceTransformer based on colorfulscoop/sbert-base-ja
 
 
 
166
 
167
+ 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.
168
 
169
  ## Model Details
170
 
171
  ### Model Description
172
+ - **Model Type:** Sentence Transformer
173
+ - **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
177
+ - **Training Dataset:**
178
+ - csv
179
+ <!-- - **Language:** Unknown -->
180
+ <!-- - **License:** Unknown -->
181
 
182
+ ### Model Sources
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
183
 
184
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
185
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
186
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
187
 
188
+ ### Full Model Architecture
189
 
190
+ ```
191
+ SentenceTransformer(
192
+ (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})
194
+ )
195
+ ```
196
 
197
+ ## Usage
198
 
199
+ ### Direct Usage (Sentence Transformers)
200
 
201
+ First install the Sentence Transformers library:
202
 
203
+ ```bash
204
+ pip install -U sentence-transformers
205
+ ```
 
 
206
 
207
+ Then you can load this model and run inference.
208
+ ```python
209
+ from sentence_transformers import SentenceTransformer
210
 
211
+ # Download from the 🤗 Hub
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+ model = SentenceTransformer("sentence_transformers_model_id")
213
+ # Run inference
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+ sentences = [
215
+ 'The weather is lovely today.',
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+ "It's so sunny outside!",
217
+ 'He drove to the stadium.',
218
+ ]
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+ embeddings = model.encode(sentences)
220
+ print(embeddings.shape)
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+ # [3, 768]
222
 
223
+ # Get the similarity scores for the embeddings
224
+ similarities = model.similarity(embeddings, embeddings)
225
+ print(similarities.shape)
226
+ # [3, 3]
227
+ ```
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229
+ <!--
230
+ ### Direct Usage (Transformers)
231
 
232
+ <details><summary>Click to see the direct usage in Transformers</summary>
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+ </details>
235
+ -->
 
 
 
 
 
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237
+ <!--
238
+ ### Downstream Usage (Sentence Transformers)
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+ You can finetune this model on your own dataset.
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+ <details><summary>Click to expand</summary>
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244
+ </details>
245
+ -->
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247
+ <!--
248
+ ### Out-of-Scope Use
 
 
 
 
 
 
 
 
 
 
 
 
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250
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
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+ -->
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253
  ## Evaluation
254
 
255
+ ### Metrics
256
+
257
+ #### Binary Classification
258
+ * 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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+
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+ | Metric | Value |
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+ |:-----------------------------|:---------|
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+ | cosine_accuracy | 0 |
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+ | cosine_accuracy_threshold | -1 |
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+ | cosine_f1 | 0 |
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+ | cosine_f1_threshold | 0 |
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+ | cosine_precision | 0 |
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+ | cosine_recall | 0 |
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+ | cosine_ap | -0.0 |
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+ | dot_accuracy | 0 |
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+ | dot_accuracy_threshold | -1 |
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+ | dot_f1 | 0 |
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+ | dot_f1_threshold | 0 |
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+ | dot_precision | 0 |
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+ | dot_recall | 0 |
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+ | dot_ap | -0.0 |
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+ | manhattan_accuracy | 0 |
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+ | manhattan_accuracy_threshold | -1 |
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+ | manhattan_f1 | 0 |
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+ | manhattan_f1_threshold | 0 |
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+ | manhattan_precision | 0 |
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+ | manhattan_recall | 0 |
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+ | manhattan_ap | -0.0 |
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+ | euclidean_accuracy | 0 |
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+ | euclidean_accuracy_threshold | -1 |
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+ | euclidean_f1 | 0 |
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+ | euclidean_f1_threshold | 0 |
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+ | euclidean_precision | 0 |
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+ | euclidean_recall | 0 |
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+ | euclidean_ap | -0.0 |
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+ | max_accuracy | 0 |
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+ | max_accuracy_threshold | -1 |
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+ | max_f1 | 0 |
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+ | max_f1_threshold | 0 |
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+ | max_precision | 0 |
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+ | max_recall | 0 |
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+ | **max_ap** | **-0.0** |
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+
299
+ <!--
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+ ## Bias, Risks and Limitations
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+
302
+ *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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+ <!--
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+ ### Recommendations
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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308
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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311
+ ## Training Details
312
 
313
+ ### Training Dataset
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+
315
+ #### csv
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+
317
+ * Dataset: csv
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+ * Size: 53 training samples
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+ * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
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+ * Approximate statistics based on the first 53 samples:
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+ | | text1 | text2 | label |
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+ |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
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+ | type | string | string | int |
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+ | details | <ul><li>min: 14 tokens</li><li>mean: 36.58 tokens</li><li>max: 84 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 22.12 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>0: ~34.62%</li><li>1: ~65.38%</li></ul> |
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+ * Samples:
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+ | text1 | text2 | label |
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+ |:----------------------------------------------------------------------------------------------------------|:------------------------------------------------|:---------------|
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+ | <code>白い ヘルメット と オレンジ色 の シャツ 、 ジーンズ 、 白い トラック と オレンジ色 の パイロン の 前 に 反射 ジャケット を 着た 金髪 の ストリート ワーカー 。</code> | <code>ストリート ワーカー は 保護 具 を 着用 して い ませ ん 。</code> | <code>1</code> |
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+ | <code>白い 帽子 を かぶった 女性 が 、 鮮やかな 色 の 岩 の 風景 を 描いて い ます 。 岩 層 自体 が 背景 に 見え ます 。</code> | <code>誰 か が 肖像 画 を 描いて い ます 。</code> | <code>1</code> |
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+ | <code>障害 の ある バイカー は 腕 を 使って 黄色 の スポーツ バイク を 動かし ます 。</code> | <code>バイカー は 足 を 使って 自転車 を さらに 進め ます 。</code> | <code>1</code> |
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+ * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
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+
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+ ### Evaluation Dataset
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+
335
+ #### csv
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+
337
+ * Dataset: csv
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+ * Size: 53 evaluation samples
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+ * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
340
+ * Approximate statistics based on the first 53 samples:
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+ | | text1 | text2 | label |
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+ |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------|
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+ | type | string | string | int |
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+ | details | <ul><li>min: 19 tokens</li><li>mean: 19.0 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 24.0 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
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+ * Samples:
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+ | text1 | text2 | label |
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+ |:--------------------------------|:--------------------------------------|:---------------|
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+ | <code>岩 の 多い 景色 を 見て 二 人</code> | <code>何 か を 見て いる 二 人 が い ます 。</code> | <code>0</code> |
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+ * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
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+
351
+ ### Training Hyperparameters
352
+ #### Non-Default Hyperparameters
353
+
354
+ - `eval_strategy`: epoch
355
+ - `learning_rate`: 2e-05
356
+ - `num_train_epochs`: 1
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+ - `warmup_ratio`: 0.4
358
+ - `fp16`: True
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+ - `batch_sampler`: no_duplicates
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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`: epoch
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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`: 2e-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`: 1
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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`: 0.4
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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`: True
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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
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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
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+ - `eval_on_start`: False
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+ - `eval_use_gather_object`: False
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+ - `batch_sampler`: no_duplicates
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+ - `multi_dataset_batch_sampler`: proportional
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+
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+ </details>
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+
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+ ### Training Logs
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+ | Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
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+ |:-----:|:----:|:-------------:|:----:|:-----------------------------------:|
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+ | 1.0 | 7 | 0.7283 | 0.0 | -0.0 |
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+
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+
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+ ### Framework Versions
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+ - Python: 3.10.14
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+ - Sentence Transformers: 3.1.0
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+ - 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",
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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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