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nmlemus
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setfit-paraphrase-mpnet-base-v2-surepath-chatgtp-dataset

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
Model card Files Community
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Add SetFit model

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  1. 1_Pooling/config.json +10 -0
  2. README.md +1228 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +49 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +51 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +66 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
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+ ---
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+ base_model: sentence-transformers/paraphrase-mpnet-base-v2
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+ library_name: setfit
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+ metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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+ tags:
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+ - setfit
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+ - sentence-transformers
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+ - text-classification
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+ - generated_from_setfit_trainer
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+ widget:
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+ - text: Why should companies invest in UX design?
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+ - text: Evaluate the efficiency of the current workflow.
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+ - text: I need a resume for a finance analyst.
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+ - text: Generate ideas for improving employee satisfaction.
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+ - text: Generate a campaign for increasing our Instagram followers.
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+ inference: true
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+ model-index:
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+ - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Text Classification
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+ dataset:
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+ name: Unknown
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+ type: unknown
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+ split: test
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+ metrics:
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+ - type: accuracy
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+ value: 0.9977272727272727
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+ name: Accuracy
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+ ---
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+
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+ # SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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+
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+ This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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+ 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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+ 2. Training a classification head with features from the fine-tuned Sentence Transformer.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
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+ - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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+ - **Maximum Sequence Length:** 512 tokens
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+ - **Number of Classes:** 44 classes
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+ <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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+ <!-- - **Language:** Unknown -->
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+ <!-- - **License:** Unknown -->
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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+ - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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+ - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:-------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | analyze | <ul><li>'Analyze the results from the A/B testing.'</li><li>'Evaluate the effectiveness of the new strategy.'</li><li>'What are the key insights from the customer survey?'</li></ul> |
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+ | analyze advantages | <ul><li>'Analyze the advantages of social media marketing for startups.'</li><li>'Analyze the advantages of electric vehicles over gas-powered cars.'</li><li>'What are the benefits of a plant-based diet for health?'</li></ul> |
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+ | analyze best practices | <ul><li>'What are the industry standards for data security?'</li><li>'Evaluate best practices for customer service.'</li><li>'Analyze best practices for social media marketing.'</li></ul> |
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+ | analyze business proposal | <ul><li>'Analyze the competitive analysis in the business plan.'</li><li>'Evaluate the team structure mentioned in the proposal.'</li><li>'What are the key points in the executive summary?'</li></ul> |
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+ | analyze data | <ul><li>'What does the data tell us about user engagement?'</li><li>'Analyze the sales data for the last quarter.'</li><li>'Analyze the data to determine customer preferences.'</li></ul> |
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+ | analyze data backup and recovery | <ul><li>'Evaluate the effectiveness of the backup strategy.'</li><li>'Analyze the current data backup procedures.'</li><li>'What are the risks associated with our data recovery plan?'</li></ul> |
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+ | analyze data visualization | <ul><li>'What does this bar chart tell us about customer demographics?'</li><li>'Interpret the data in this line chart.'</li><li>'Analyze the distribution shown in this histogram.'</li></ul> |
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+ | analyze feedback | <ul><li>'Analyze customer feedback from the recent survey.'</li><li>'Analyze the feedback received from the beta testers.'</li><li>'Evaluate the feedback from the focus group.'</li></ul> |
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+ | analyze information | <ul><li>'What are the main points from the research findings?'</li><li>'Evaluate the information from the competitor analysis.'</li><li>'What conclusions can be drawn from the survey results?'</li></ul> |
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+ | analyze information technology security policy | <ul><li>'Evaluate the risks mentioned in the security policy.'</li><li>"Analyze the company's IT security policy."</li><li>'What are the strengths of our IT security measures?'</li></ul> |
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+ | analyze job descriptions | <ul><li>'Analyze the job description for the new position.'</li><li>'What are the key responsibilities listed in the job description?'</li><li>'Evaluate the job description for inclusivity.'</li></ul> |
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+ | analyze marketing campaign | <ul><li>'Evaluate the customer conversion rates from the Google Ads campaign.'</li><li>'What were the engagement rates for the spring sale campaign?'</li><li>'Assess the performance of the influencer marketing strategy.'</li></ul> |
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+ | analyze packaging design | <ul><li>'What are the strengths and weaknesses of the packaging?'</li><li>'Evaluate the impact of packaging on brand perception.'</li><li>'Analyze the cost-effectiveness of the packaging design.'</li></ul> |
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+ | analyze process | <ul><li>'What are the key steps in our product development process?'</li><li>'Evaluate the process for software deployment.'</li><li>'Analyze the process for onboarding new employees.'</li></ul> |
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+ | analyze product description | <ul><li>'What are the strengths of this product description?'</li><li>'Evaluate the clarity of the product description.'</li><li>'Analyze the persuasiveness of the product features.'</li></ul> |
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+ | analyze product rebranding | <ul><li>'What were the challenges faced during rebranding?'</li><li>'What are the key changes in the new branding?'</li><li>'Evaluate the customer response to the rebranding effort.'</li></ul> |
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+ | analyze product recall | <ul><li>'Analyze the customer feedback after the recall.'</li><li>'What were the financial implications of the recall?'</li><li>'Evaluate the effectiveness of the recall process.'</li></ul> |
82
+ | analyze social media campaign | <ul><li>'Evaluate the reach and impressions of the LinkedIn posts.'</li><li>'Analyze the effectiveness of the Twitter campaign.'</li><li>'What improvements can be made to our social media campaigns?'</li></ul> |
83
+ | analyze time management | <ul><li>'Analyze how I can better prioritize my tasks.'</li><li>'Analyze my current time management techniques.'</li><li>'What are the weaknesses in my time management?'</li></ul> |
84
+ | analyze trends | <ul><li>'Analyze the social media trends influencing businesses.'</li><li>'What are the current trends in digital marketing?'</li><li>'Analyze the latest trends in the tech industry.'</li></ul> |
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+ | analyze website concept | <ul><li>'Analyze the content strategy of the new website.'</li><li>'What are the key elements of a successful website concept?'</li><li>'Analyze the mobile responsiveness of the website design.'</li></ul> |
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+ | bake | <ul><li>'Bake a loaf of banana bread.'</li><li>'How do I bake a cheesecake?'</li><li>'Bake a batch of brownies.'</li></ul> |
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+ | define | <ul><li>"Define the term 'machine learning'."</li><li>"What does 'SEO' stand for?"</li><li>"Define 'data analytics'."</li></ul> |
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+ | explain | <ul><li>"Explain the importance of cybersecurity in today's world."</li><li>'Explain how machine learning works.'</li><li>'Can you clarify what SEO involves?'</li></ul> |
89
+ | explain the importance of user experience design | <ul><li>'Explain how UX design improves accessibility.'</li><li>'Why is user experience design important for websites?'</li><li>'Why should companies invest in UX design?'</li></ul> |
90
+ | generate business proposal | <ul><li>'What is the format for a business proposal?'</li><li>'Generate a business proposal for a new product line.'</li><li>'Create a proposal for a partnership with another company.'</li></ul> |
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+ | generate crisis communication plan | <ul><li>'Create a communication plan for a financial crisis.'</li><li>'Create a plan for communicating with stakeholders in a crisis.'</li><li>'Generate a plan for internal communication during a crisis.'</li></ul> |
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+ | generate ideas | <ul><li>'Generate ideas for improving employee satisfaction.'</li><li>'Come up with ideas for our company’s anniversary event.'</li><li>'What are some unique selling points for our service?'</li></ul> |
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+ | generate learning plan | <ul><li>'Create a learning plan for understanding machine learning concepts.'</li><li>'Create a plan for learning digital marketing skills.'</li><li>'What should be included in a learning plan for data science?'</li></ul> |
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+ | generate product description | <ul><li>'Generate a product description for the new smartphone.'</li><li>'Create a detailed description of the latest software.'</li><li>'What should be included in a product description?'</li></ul> |
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+ | generate product roadmap | <ul><li>'Create a roadmap for the new software development.'</li><li>'What should be included in a product roadmap?'</li><li>'Generate a product roadmap for customer feedback integration.'</li></ul> |
96
+ | generate project proposal | <ul><li>'What are the key elements of a project proposal?'</li><li>'What should be included in a project proposal?'</li><li>'Generate a proposal for a research project.'</li></ul> |
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+ | generate recommendations | <ul><li>'Provide recommendations for streamlining operations.'</li><li>'Generate recommendations for improving customer service.'</li><li>'What are your recommendations for the new marketing strategy?'</li></ul> |
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+ | generate resume | <ul><li>'I need a resume for a teaching position.'</li><li>'Generate a resume for a software engineer.'</li><li>'Generate a resume for a data scientist.'</li></ul> |
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+ | generate social media campaign | <ul><li>"Create a campaign to highlight our company's sustainability efforts."</li><li>'Generate a campaign for increasing our Instagram followers.'</li><li>'I need a campaign plan for promoting our summer sale.'</li></ul> |
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+ | generate template | <ul><li>'Can you make a template for job descriptions?'</li><li>'Create a template for a project proposal.'</li><li>'Generate a meeting agenda template.'</li></ul> |
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+ | generate training program outline | <ul><li>'Generate a training program outline for new employees.'</li><li>'Generate an outline for diversity and inclusion training.'</li><li>'Create an outline for a leadership training program.'</li></ul> |
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+ | learn a language | <ul><li>'How do I become fluent in Portuguese?'</li><li>'How can I practice English pronunciation?'</li><li>'What is the best way to learn Chinese characters?'</li></ul> |
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+ | manage time | <ul><li>'How do I balance work and personal life?'</li><li>'Tips for managing time during exams.'</li><li>'How do I create a daily schedule?'</li></ul> |
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+ | outline steps | <ul><li>'What are the steps to develop a training program?'</li><li>'Outline the steps to launch a new product.'</li><li>'Outline the steps to implement a new software system.'</li></ul> |
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+ | provide general information | <ul><li>'Can you give me an overview of the new software?'</li><li>"Give me general information about the industry's trends."</li><li>'What are the key points about the product launch?'</li></ul> |
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+ | recommend | <ul><li>'What are the top destinations for a vacation?'</li><li>'What podcasts would you suggest for entrepreneurs?'</li><li>'Recommend a good book on data science.'</li></ul> |
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+ | summarize advantages | <ul><li>'Summarize the advantages of using renewable energy.'</li><li>'Summarize the advantages of social media marketing.'</li><li>'What are the benefits of using project management software?'</li></ul> |
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+ | summarize financial report | <ul><li>'Summarize the main findings of the quarterly financial report.'</li><li>'Provide a summary of the financial projections.'</li><li>'What are the key metrics in the financial summary?'</li></ul> |
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+
110
+ ## Evaluation
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+
112
+ ### Metrics
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+ | Label | Accuracy |
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+ |:--------|:---------|
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+ | **all** | 0.9977 |
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+
117
+ ## Uses
118
+
119
+ ### Direct Use for Inference
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+
121
+ First install the SetFit library:
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+
123
+ ```bash
124
+ pip install setfit
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+ ```
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+
127
+ Then you can load this model and run inference.
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+
129
+ ```python
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+ from setfit import SetFitModel
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+
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+ # Download from the 🤗 Hub
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+ model = SetFitModel.from_pretrained("nmlemus/setfit-paraphrase-mpnet-base-v2-surepath-chatgtp-dataset")
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+ # Run inference
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+ preds = model("I need a resume for a finance analyst.")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
141
+ *List how someone could finetune this model on their own dataset.*
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+ -->
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+
144
+ <!--
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+ ### Out-of-Scope Use
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+
147
+ *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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+
150
+ <!--
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+ ## Bias, Risks and Limitations
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+
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+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
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+
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+ <!--
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+ ### Recommendations
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+
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+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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+ -->
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+
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+ ## Training Details
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+
164
+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:-------|:----|
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+ | Word count | 3 | 7.8795 | 13 |
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+
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+ | Label | Training Sample Count |
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+ |:-------------------------------------------------|:----------------------|
171
+ | analyze | 10 |
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+ | analyze advantages | 10 |
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+ | analyze best practices | 10 |
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+ | analyze business proposal | 10 |
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+ | analyze data | 10 |
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+ | analyze data backup and recovery | 10 |
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+ | analyze data visualization | 10 |
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+ | analyze feedback | 10 |
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+ | analyze information | 10 |
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+ | analyze information technology security policy | 10 |
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+ | analyze job descriptions | 10 |
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+ | analyze marketing campaign | 10 |
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+ | analyze packaging design | 10 |
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+ | analyze process | 10 |
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+ | analyze product description | 10 |
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+ | analyze product rebranding | 10 |
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+ | analyze product recall | 10 |
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+ | analyze social media campaign | 10 |
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+ | analyze time management | 10 |
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+ | analyze trends | 10 |
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+ | analyze website concept | 10 |
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+ | bake | 10 |
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+ | define | 10 |
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+ | explain | 10 |
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+ | explain the importance of user experience design | 10 |
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+ | generate business proposal | 10 |
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+ | generate crisis communication plan | 10 |
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+ | generate ideas | 10 |
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+ | generate learning plan | 10 |
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+ | generate product description | 10 |
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+ | generate product roadmap | 10 |
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+ | generate project proposal | 10 |
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+ | generate recommendations | 10 |
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+ | generate resume | 10 |
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+ | generate social media campaign | 10 |
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+ | generate template | 10 |
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+ | generate training program outline | 10 |
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+ | learn a language | 10 |
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+ | manage time | 10 |
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+ | outline steps | 10 |
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+ | provide general information | 10 |
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+ | recommend | 10 |
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+ | summarize advantages | 10 |
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+ | summarize financial report | 10 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (16, 16)
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+ - num_epochs: (4, 4)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - body_learning_rate: (2e-05, 1e-05)
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+ - head_learning_rate: 0.01
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+ - loss: CosineSimilarityLoss
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+ - distance_metric: cosine_distance
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+ - margin: 0.25
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+ - end_to_end: False
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+ - use_amp: False
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+ - warmup_proportion: 0.1
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+ - seed: 42
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+ - eval_max_steps: -1
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+ - load_best_model_at_end: True
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:-------:|:---------:|:-------------:|:---------------:|
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+ | 0.0001 | 1 | 0.1037 | - |
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+ | 0.0042 | 50 | 0.1544 | - |
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+ | 0.0085 | 100 | 0.1555 | - |
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+ | 0.0127 | 150 | 0.0948 | - |
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+ | 0.0169 | 200 | 0.1176 | - |
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+ | 0.0211 | 250 | 0.1108 | - |
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+ | 0.0254 | 300 | 0.1169 | - |
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+ | 0.0296 | 350 | 0.1291 | - |
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+ | 0.0338 | 400 | 0.1068 | - |
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+ | 0.0381 | 450 | 0.1369 | - |
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+ | 0.0423 | 500 | 0.0823 | - |
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+ | 0.0465 | 550 | 0.0732 | - |
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+ | 0.0507 | 600 | 0.1006 | - |
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+ | 0.0550 | 650 | 0.0638 | - |
250
+ | 0.0592 | 700 | 0.0818 | - |
251
+ | 0.0634 | 750 | 0.0542 | - |
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+ | 0.0677 | 800 | 0.039 | - |
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+ | 0.0719 | 850 | 0.0497 | - |
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+ | 0.0761 | 900 | 0.016 | - |
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+ | 0.0803 | 950 | 0.021 | - |
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+ | 0.0846 | 1000 | 0.0136 | - |
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+ | 0.0888 | 1050 | 0.0353 | - |
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+ | 0.0930 | 1100 | 0.0164 | - |
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+ | 0.0973 | 1150 | 0.0123 | - |
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+ | 0.1015 | 1200 | 0.0218 | - |
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+ | 0.1057 | 1250 | 0.0845 | - |
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+ | 0.1099 | 1300 | 0.0082 | - |
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+ | 0.1142 | 1350 | 0.0385 | - |
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+ | 0.1184 | 1400 | 0.0087 | - |
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+ | 0.1226 | 1450 | 0.0133 | - |
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+ | 0.1268 | 1500 | 0.0045 | - |
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+ | 0.1311 | 1550 | 0.0054 | - |
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+ | 0.1353 | 1600 | 0.0078 | - |
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+ | 0.1395 | 1650 | 0.0068 | - |
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+ | 0.1438 | 1700 | 0.0586 | - |
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+ | 0.1480 | 1750 | 0.0173 | - |
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+ | 0.1522 | 1800 | 0.0585 | - |
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+ | 0.1564 | 1850 | 0.0052 | - |
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+ | 0.1607 | 1900 | 0.0046 | - |
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+ | 0.1649 | 1950 | 0.0021 | - |
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+ | 0.1691 | 2000 | 0.0092 | - |
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+ | 0.1734 | 2050 | 0.0027 | - |
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+ | 0.1776 | 2100 | 0.0041 | - |
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+ | 0.1818 | 2150 | 0.0053 | - |
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+ | 0.1860 | 2200 | 0.0585 | - |
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+ | 0.1903 | 2250 | 0.0034 | - |
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+ | 0.1945 | 2300 | 0.0601 | - |
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+ | 0.1987 | 2350 | 0.0061 | - |
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+ | 0.2030 | 2400 | 0.0022 | - |
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+ | 0.2072 | 2450 | 0.0037 | - |
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+ | 0.2114 | 2500 | 0.0019 | - |
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+ | 0.2156 | 2550 | 0.0012 | - |
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+ | 0.2199 | 2600 | 0.0031 | - |
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+ | 0.2241 | 2650 | 0.0028 | - |
290
+ | 0.2283 | 2700 | 0.0011 | - |
291
+ | 0.2326 | 2750 | 0.0019 | - |
292
+ | 0.2368 | 2800 | 0.0638 | - |
293
+ | 0.2410 | 2850 | 0.0018 | - |
294
+ | 0.2452 | 2900 | 0.0017 | - |
295
+ | 0.2495 | 2950 | 0.0021 | - |
296
+ | 0.2537 | 3000 | 0.0016 | - |
297
+ | 0.2579 | 3050 | 0.0013 | - |
298
+ | 0.2622 | 3100 | 0.0017 | - |
299
+ | 0.2664 | 3150 | 0.0101 | - |
300
+ | 0.2706 | 3200 | 0.0029 | - |
301
+ | 0.2748 | 3250 | 0.0013 | - |
302
+ | 0.2791 | 3300 | 0.002 | - |
303
+ | 0.2833 | 3350 | 0.0079 | - |
304
+ | 0.2875 | 3400 | 0.0013 | - |
305
+ | 0.2918 | 3450 | 0.001 | - |
306
+ | 0.2960 | 3500 | 0.0015 | - |
307
+ | 0.3002 | 3550 | 0.0013 | - |
308
+ | 0.3044 | 3600 | 0.0017 | - |
309
+ | 0.3087 | 3650 | 0.0012 | - |
310
+ | 0.3129 | 3700 | 0.0007 | - |
311
+ | 0.3171 | 3750 | 0.0019 | - |
312
+ | 0.3214 | 3800 | 0.0008 | - |
313
+ | 0.3256 | 3850 | 0.0008 | - |
314
+ | 0.3298 | 3900 | 0.0007 | - |
315
+ | 0.3340 | 3950 | 0.0007 | - |
316
+ | 0.3383 | 4000 | 0.001 | - |
317
+ | 0.3425 | 4050 | 0.0005 | - |
318
+ | 0.3467 | 4100 | 0.0008 | - |
319
+ | 0.3510 | 4150 | 0.0007 | - |
320
+ | 0.3552 | 4200 | 0.0014 | - |
321
+ | 0.3594 | 4250 | 0.0005 | - |
322
+ | 0.3636 | 4300 | 0.0008 | - |
323
+ | 0.3679 | 4350 | 0.0006 | - |
324
+ | 0.3721 | 4400 | 0.0011 | - |
325
+ | 0.3763 | 4450 | 0.0006 | - |
326
+ | 0.3805 | 4500 | 0.0007 | - |
327
+ | 0.3848 | 4550 | 0.0006 | - |
328
+ | 0.3890 | 4600 | 0.0003 | - |
329
+ | 0.3932 | 4650 | 0.0022 | - |
330
+ | 0.3975 | 4700 | 0.0007 | - |
331
+ | 0.4017 | 4750 | 0.0031 | - |
332
+ | 0.4059 | 4800 | 0.0013 | - |
333
+ | 0.4101 | 4850 | 0.0015 | - |
334
+ | 0.4144 | 4900 | 0.0017 | - |
335
+ | 0.4186 | 4950 | 0.0007 | - |
336
+ | 0.4228 | 5000 | 0.0006 | - |
337
+ | 0.4271 | 5050 | 0.0006 | - |
338
+ | 0.4313 | 5100 | 0.0013 | - |
339
+ | 0.4355 | 5150 | 0.0003 | - |
340
+ | 0.4397 | 5200 | 0.12 | - |
341
+ | 0.4440 | 5250 | 0.0005 | - |
342
+ | 0.4482 | 5300 | 0.0006 | - |
343
+ | 0.4524 | 5350 | 0.0016 | - |
344
+ | 0.4567 | 5400 | 0.0008 | - |
345
+ | 0.4609 | 5450 | 0.0118 | - |
346
+ | 0.4651 | 5500 | 0.0003 | - |
347
+ | 0.4693 | 5550 | 0.0542 | - |
348
+ | 0.4736 | 5600 | 0.0011 | - |
349
+ | 0.4778 | 5650 | 0.0004 | - |
350
+ | 0.4820 | 5700 | 0.001 | - |
351
+ | 0.4863 | 5750 | 0.0008 | - |
352
+ | 0.4905 | 5800 | 0.0008 | - |
353
+ | 0.4947 | 5850 | 0.0004 | - |
354
+ | 0.4989 | 5900 | 0.0008 | - |
355
+ | 0.5032 | 5950 | 0.0009 | - |
356
+ | 0.5074 | 6000 | 0.0005 | - |
357
+ | 0.5116 | 6050 | 0.0006 | - |
358
+ | 0.5159 | 6100 | 0.0012 | - |
359
+ | 0.5201 | 6150 | 0.0004 | - |
360
+ | 0.5243 | 6200 | 0.0005 | - |
361
+ | 0.5285 | 6250 | 0.0007 | - |
362
+ | 0.5328 | 6300 | 0.0009 | - |
363
+ | 0.5370 | 6350 | 0.0006 | - |
364
+ | 0.5412 | 6400 | 0.0007 | - |
365
+ | 0.5455 | 6450 | 0.0007 | - |
366
+ | 0.5497 | 6500 | 0.0003 | - |
367
+ | 0.5539 | 6550 | 0.0568 | - |
368
+ | 0.5581 | 6600 | 0.0006 | - |
369
+ | 0.5624 | 6650 | 0.0002 | - |
370
+ | 0.5666 | 6700 | 0.0006 | - |
371
+ | 0.5708 | 6750 | 0.0003 | - |
372
+ | 0.5751 | 6800 | 0.0003 | - |
373
+ | 0.5793 | 6850 | 0.0004 | - |
374
+ | 0.5835 | 6900 | 0.0006 | - |
375
+ | 0.5877 | 6950 | 0.0004 | - |
376
+ | 0.5920 | 7000 | 0.0004 | - |
377
+ | 0.5962 | 7050 | 0.0002 | - |
378
+ | 0.6004 | 7100 | 0.0002 | - |
379
+ | 0.6047 | 7150 | 0.001 | - |
380
+ | 0.6089 | 7200 | 0.0002 | - |
381
+ | 0.6131 | 7250 | 0.0004 | - |
382
+ | 0.6173 | 7300 | 0.0009 | - |
383
+ | 0.6216 | 7350 | 0.0003 | - |
384
+ | 0.6258 | 7400 | 0.0003 | - |
385
+ | 0.6300 | 7450 | 0.0018 | - |
386
+ | 0.6342 | 7500 | 0.0004 | - |
387
+ | 0.6385 | 7550 | 0.0035 | - |
388
+ | 0.6427 | 7600 | 0.0012 | - |
389
+ | 0.6469 | 7650 | 0.0005 | - |
390
+ | 0.6512 | 7700 | 0.0003 | - |
391
+ | 0.6554 | 7750 | 0.0003 | - |
392
+ | 0.6596 | 7800 | 0.0004 | - |
393
+ | 0.6638 | 7850 | 0.0004 | - |
394
+ | 0.6681 | 7900 | 0.0004 | - |
395
+ | 0.6723 | 7950 | 0.0003 | - |
396
+ | 0.6765 | 8000 | 0.0002 | - |
397
+ | 0.6808 | 8050 | 0.0002 | - |
398
+ | 0.6850 | 8100 | 0.0008 | - |
399
+ | 0.6892 | 8150 | 0.0003 | - |
400
+ | 0.6934 | 8200 | 0.0002 | - |
401
+ | 0.6977 | 8250 | 0.0003 | - |
402
+ | 0.7019 | 8300 | 0.0002 | - |
403
+ | 0.7061 | 8350 | 0.0024 | - |
404
+ | 0.7104 | 8400 | 0.0022 | - |
405
+ | 0.7146 | 8450 | 0.0004 | - |
406
+ | 0.7188 | 8500 | 0.0092 | - |
407
+ | 0.7230 | 8550 | 0.0002 | - |
408
+ | 0.7273 | 8600 | 0.0001 | - |
409
+ | 0.7315 | 8650 | 0.0002 | - |
410
+ | 0.7357 | 8700 | 0.0003 | - |
411
+ | 0.7400 | 8750 | 0.0005 | - |
412
+ | 0.7442 | 8800 | 0.0002 | - |
413
+ | 0.7484 | 8850 | 0.0005 | - |
414
+ | 0.7526 | 8900 | 0.0002 | - |
415
+ | 0.7569 | 8950 | 0.0002 | - |
416
+ | 0.7611 | 9000 | 0.0002 | - |
417
+ | 0.7653 | 9050 | 0.0002 | - |
418
+ | 0.7696 | 9100 | 0.0001 | - |
419
+ | 0.7738 | 9150 | 0.0002 | - |
420
+ | 0.7780 | 9200 | 0.0004 | - |
421
+ | 0.7822 | 9250 | 0.0003 | - |
422
+ | 0.7865 | 9300 | 0.0003 | - |
423
+ | 0.7907 | 9350 | 0.0002 | - |
424
+ | 0.7949 | 9400 | 0.0005 | - |
425
+ | 0.7992 | 9450 | 0.0002 | - |
426
+ | 0.8034 | 9500 | 0.0002 | - |
427
+ | 0.8076 | 9550 | 0.0017 | - |
428
+ | 0.8118 | 9600 | 0.0004 | - |
429
+ | 0.8161 | 9650 | 0.0003 | - |
430
+ | 0.8203 | 9700 | 0.0002 | - |
431
+ | 0.8245 | 9750 | 0.0002 | - |
432
+ | 0.8288 | 9800 | 0.0001 | - |
433
+ | 0.8330 | 9850 | 0.0001 | - |
434
+ | 0.8372 | 9900 | 0.0001 | - |
435
+ | 0.8414 | 9950 | 0.0005 | - |
436
+ | 0.8457 | 10000 | 0.0001 | - |
437
+ | 0.8499 | 10050 | 0.0001 | - |
438
+ | 0.8541 | 10100 | 0.0002 | - |
439
+ | 0.8584 | 10150 | 0.0002 | - |
440
+ | 0.8626 | 10200 | 0.0003 | - |
441
+ | 0.8668 | 10250 | 0.0003 | - |
442
+ | 0.8710 | 10300 | 0.0002 | - |
443
+ | 0.8753 | 10350 | 0.0002 | - |
444
+ | 0.8795 | 10400 | 0.001 | - |
445
+ | 0.8837 | 10450 | 0.0008 | - |
446
+ | 0.8879 | 10500 | 0.0005 | - |
447
+ | 0.8922 | 10550 | 0.0017 | - |
448
+ | 0.8964 | 10600 | 0.0606 | - |
449
+ | 0.9006 | 10650 | 0.0002 | - |
450
+ | 0.9049 | 10700 | 0.0003 | - |
451
+ | 0.9091 | 10750 | 0.0005 | - |
452
+ | 0.9133 | 10800 | 0.0008 | - |
453
+ | 0.9175 | 10850 | 0.0003 | - |
454
+ | 0.9218 | 10900 | 0.002 | - |
455
+ | 0.9260 | 10950 | 0.0003 | - |
456
+ | 0.9302 | 11000 | 0.0003 | - |
457
+ | 0.9345 | 11050 | 0.0003 | - |
458
+ | 0.9387 | 11100 | 0.0243 | - |
459
+ | 0.9429 | 11150 | 0.0016 | - |
460
+ | 0.9471 | 11200 | 0.021 | - |
461
+ | 0.9514 | 11250 | 0.0003 | - |
462
+ | 0.9556 | 11300 | 0.0006 | - |
463
+ | 0.9598 | 11350 | 0.0166 | - |
464
+ | 0.9641 | 11400 | 0.0014 | - |
465
+ | 0.9683 | 11450 | 0.0004 | - |
466
+ | 0.9725 | 11500 | 0.0006 | - |
467
+ | 0.9767 | 11550 | 0.0001 | - |
468
+ | 0.9810 | 11600 | 0.0002 | - |
469
+ | 0.9852 | 11650 | 0.0021 | - |
470
+ | 0.9894 | 11700 | 0.0004 | - |
471
+ | 0.9937 | 11750 | 0.0002 | - |
472
+ | 0.9979 | 11800 | 0.0003 | - |
473
+ | 1.0 | 11825 | - | 0.0019 |
474
+ | 1.0021 | 11850 | 0.0002 | - |
475
+ | 1.0063 | 11900 | 0.0002 | - |
476
+ | 1.0106 | 11950 | 0.0002 | - |
477
+ | 1.0148 | 12000 | 0.0002 | - |
478
+ | 1.0190 | 12050 | 0.0002 | - |
479
+ | 1.0233 | 12100 | 0.0002 | - |
480
+ | 1.0275 | 12150 | 0.0002 | - |
481
+ | 1.0317 | 12200 | 0.0002 | - |
482
+ | 1.0359 | 12250 | 0.0005 | - |
483
+ | 1.0402 | 12300 | 0.0002 | - |
484
+ | 1.0444 | 12350 | 0.0002 | - |
485
+ | 1.0486 | 12400 | 0.0004 | - |
486
+ | 1.0529 | 12450 | 0.0002 | - |
487
+ | 1.0571 | 12500 | 0.0002 | - |
488
+ | 1.0613 | 12550 | 0.0001 | - |
489
+ | 1.0655 | 12600 | 0.0001 | - |
490
+ | 1.0698 | 12650 | 0.0001 | - |
491
+ | 1.0740 | 12700 | 0.0001 | - |
492
+ | 1.0782 | 12750 | 0.0001 | - |
493
+ | 1.0825 | 12800 | 0.0002 | - |
494
+ | 1.0867 | 12850 | 0.0001 | - |
495
+ | 1.0909 | 12900 | 0.0002 | - |
496
+ | 1.0951 | 12950 | 0.0002 | - |
497
+ | 1.0994 | 13000 | 0.0002 | - |
498
+ | 1.1036 | 13050 | 0.0002 | - |
499
+ | 1.1078 | 13100 | 0.0001 | - |
500
+ | 1.1121 | 13150 | 0.0002 | - |
501
+ | 1.1163 | 13200 | 0.0236 | - |
502
+ | 1.1205 | 13250 | 0.0002 | - |
503
+ | 1.1247 | 13300 | 0.0001 | - |
504
+ | 1.1290 | 13350 | 0.0023 | - |
505
+ | 1.1332 | 13400 | 0.0003 | - |
506
+ | 1.1374 | 13450 | 0.0001 | - |
507
+ | 1.1416 | 13500 | 0.0003 | - |
508
+ | 1.1459 | 13550 | 0.0003 | - |
509
+ | 1.1501 | 13600 | 0.0004 | - |
510
+ | 1.1543 | 13650 | 0.0002 | - |
511
+ | 1.1586 | 13700 | 0.0002 | - |
512
+ | 1.1628 | 13750 | 0.0001 | - |
513
+ | 1.1670 | 13800 | 0.0001 | - |
514
+ | 1.1712 | 13850 | 0.0001 | - |
515
+ | 1.1755 | 13900 | 0.0001 | - |
516
+ | 1.1797 | 13950 | 0.0001 | - |
517
+ | 1.1839 | 14000 | 0.0001 | - |
518
+ | 1.1882 | 14050 | 0.0002 | - |
519
+ | 1.1924 | 14100 | 0.0002 | - |
520
+ | 1.1966 | 14150 | 0.0001 | - |
521
+ | 1.2008 | 14200 | 0.0002 | - |
522
+ | 1.2051 | 14250 | 0.0003 | - |
523
+ | 1.2093 | 14300 | 0.0001 | - |
524
+ | 1.2135 | 14350 | 0.0001 | - |
525
+ | 1.2178 | 14400 | 0.0002 | - |
526
+ | 1.2220 | 14450 | 0.001 | - |
527
+ | 1.2262 | 14500 | 0.0001 | - |
528
+ | 1.2304 | 14550 | 0.0001 | - |
529
+ | 1.2347 | 14600 | 0.0001 | - |
530
+ | 1.2389 | 14650 | 0.0002 | - |
531
+ | 1.2431 | 14700 | 0.0001 | - |
532
+ | 1.2474 | 14750 | 0.0002 | - |
533
+ | 1.2516 | 14800 | 0.0001 | - |
534
+ | 1.2558 | 14850 | 0.0001 | - |
535
+ | 1.2600 | 14900 | 0.0001 | - |
536
+ | 1.2643 | 14950 | 0.0002 | - |
537
+ | 1.2685 | 15000 | 0.0001 | - |
538
+ | 1.2727 | 15050 | 0.0061 | - |
539
+ | 1.2770 | 15100 | 0.0001 | - |
540
+ | 1.2812 | 15150 | 0.0004 | - |
541
+ | 1.2854 | 15200 | 0.0002 | - |
542
+ | 1.2896 | 15250 | 0.0002 | - |
543
+ | 1.2939 | 15300 | 0.0001 | - |
544
+ | 1.2981 | 15350 | 0.0001 | - |
545
+ | 1.3023 | 15400 | 0.0001 | - |
546
+ | 1.3066 | 15450 | 0.0002 | - |
547
+ | 1.3108 | 15500 | 0.0001 | - |
548
+ | 1.3150 | 15550 | 0.0001 | - |
549
+ | 1.3192 | 15600 | 0.002 | - |
550
+ | 1.3235 | 15650 | 0.0004 | - |
551
+ | 1.3277 | 15700 | 0.0001 | - |
552
+ | 1.3319 | 15750 | 0.0001 | - |
553
+ | 1.3362 | 15800 | 0.0002 | - |
554
+ | 1.3404 | 15850 | 0.0001 | - |
555
+ | 1.3446 | 15900 | 0.0001 | - |
556
+ | 1.3488 | 15950 | 0.0001 | - |
557
+ | 1.3531 | 16000 | 0.0002 | - |
558
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559
+ | 1.3615 | 16100 | 0.0003 | - |
560
+ | 1.3658 | 16150 | 0.0001 | - |
561
+ | 1.3700 | 16200 | 0.0001 | - |
562
+ | 1.3742 | 16250 | 0.0001 | - |
563
+ | 1.3784 | 16300 | 0.0001 | - |
564
+ | 1.3827 | 16350 | 0.0001 | - |
565
+ | 1.3869 | 16400 | 0.0001 | - |
566
+ | 1.3911 | 16450 | 0.0004 | - |
567
+ | 1.3953 | 16500 | 0.0002 | - |
568
+ | 1.3996 | 16550 | 0.0001 | - |
569
+ | 1.4038 | 16600 | 0.0001 | - |
570
+ | 1.4080 | 16650 | 0.0001 | - |
571
+ | 1.4123 | 16700 | 0.0001 | - |
572
+ | 1.4165 | 16750 | 0.0001 | - |
573
+ | 1.4207 | 16800 | 0.0001 | - |
574
+ | 1.4249 | 16850 | 0.0001 | - |
575
+ | 1.4292 | 16900 | 0.0001 | - |
576
+ | 1.4334 | 16950 | 0.0024 | - |
577
+ | 1.4376 | 17000 | 0.0001 | - |
578
+ | 1.4419 | 17050 | 0.0002 | - |
579
+ | 1.4461 | 17100 | 0.0001 | - |
580
+ | 1.4503 | 17150 | 0.0001 | - |
581
+ | 1.4545 | 17200 | 0.0001 | - |
582
+ | 1.4588 | 17250 | 0.0001 | - |
583
+ | 1.4630 | 17300 | 0.0606 | - |
584
+ | 1.4672 | 17350 | 0.0004 | - |
585
+ | 1.4715 | 17400 | 0.0001 | - |
586
+ | 1.4757 | 17450 | 0.0007 | - |
587
+ | 1.4799 | 17500 | 0.0001 | - |
588
+ | 1.4841 | 17550 | 0.0001 | - |
589
+ | 1.4884 | 17600 | 0.0001 | - |
590
+ | 1.4926 | 17650 | 0.0002 | - |
591
+ | 1.4968 | 17700 | 0.0015 | - |
592
+ | 1.5011 | 17750 | 0.0001 | - |
593
+ | 1.5053 | 17800 | 0.0001 | - |
594
+ | 1.5095 | 17850 | 0.0002 | - |
595
+ | 1.5137 | 17900 | 0.0002 | - |
596
+ | 1.5180 | 17950 | 0.0001 | - |
597
+ | 1.5222 | 18000 | 0.0001 | - |
598
+ | 1.5264 | 18050 | 0.0001 | - |
599
+ | 1.5307 | 18100 | 0.0001 | - |
600
+ | 1.5349 | 18150 | 0.0002 | - |
601
+ | 1.5391 | 18200 | 0.0001 | - |
602
+ | 1.5433 | 18250 | 0.0001 | - |
603
+ | 1.5476 | 18300 | 0.0001 | - |
604
+ | 1.5518 | 18350 | 0.0001 | - |
605
+ | 1.5560 | 18400 | 0.0002 | - |
606
+ | 1.5603 | 18450 | 0.0001 | - |
607
+ | 1.5645 | 18500 | 0.0001 | - |
608
+ | 1.5687 | 18550 | 0.0001 | - |
609
+ | 1.5729 | 18600 | 0.0001 | - |
610
+ | 1.5772 | 18650 | 0.0001 | - |
611
+ | 1.5814 | 18700 | 0.0002 | - |
612
+ | 1.5856 | 18750 | 0.0001 | - |
613
+ | 1.5899 | 18800 | 0.0001 | - |
614
+ | 1.5941 | 18850 | 0.0001 | - |
615
+ | 1.5983 | 18900 | 0.0009 | - |
616
+ | 1.6025 | 18950 | 0.0001 | - |
617
+ | 1.6068 | 19000 | 0.0002 | - |
618
+ | 1.6110 | 19050 | 0.0013 | - |
619
+ | 1.6152 | 19100 | 0.0001 | - |
620
+ | 1.6195 | 19150 | 0.0005 | - |
621
+ | 1.6237 | 19200 | 0.0001 | - |
622
+ | 1.6279 | 19250 | 0.0016 | - |
623
+ | 1.6321 | 19300 | 0.0001 | - |
624
+ | 1.6364 | 19350 | 0.0001 | - |
625
+ | 1.6406 | 19400 | 0.0015 | - |
626
+ | 1.6448 | 19450 | 0.0001 | - |
627
+ | 1.6490 | 19500 | 0.0001 | - |
628
+ | 1.6533 | 19550 | 0.0001 | - |
629
+ | 1.6575 | 19600 | 0.0001 | - |
630
+ | 1.6617 | 19650 | 0.0001 | - |
631
+ | 1.6660 | 19700 | 0.0001 | - |
632
+ | 1.6702 | 19750 | 0.0001 | - |
633
+ | 1.6744 | 19800 | 0.0001 | - |
634
+ | 1.6786 | 19850 | 0.0001 | - |
635
+ | 1.6829 | 19900 | 0.0001 | - |
636
+ | 1.6871 | 19950 | 0.0001 | - |
637
+ | 1.6913 | 20000 | 0.0001 | - |
638
+ | 1.6956 | 20050 | 0.0001 | - |
639
+ | 1.6998 | 20100 | 0.0001 | - |
640
+ | 1.7040 | 20150 | 0.0001 | - |
641
+ | 1.7082 | 20200 | 0.0001 | - |
642
+ | 1.7125 | 20250 | 0.0001 | - |
643
+ | 1.7167 | 20300 | 0.0001 | - |
644
+ | 1.7209 | 20350 | 0.0001 | - |
645
+ | 1.7252 | 20400 | 0.0001 | - |
646
+ | 1.7294 | 20450 | 0.0001 | - |
647
+ | 1.7336 | 20500 | 0.002 | - |
648
+ | 1.7378 | 20550 | 0.0001 | - |
649
+ | 1.7421 | 20600 | 0.0001 | - |
650
+ | 1.7463 | 20650 | 0.0001 | - |
651
+ | 1.7505 | 20700 | 0.0001 | - |
652
+ | 1.7548 | 20750 | 0.0001 | - |
653
+ | 1.7590 | 20800 | 0.0001 | - |
654
+ | 1.7632 | 20850 | 0.0001 | - |
655
+ | 1.7674 | 20900 | 0.0001 | - |
656
+ | 1.7717 | 20950 | 0.0002 | - |
657
+ | 1.7759 | 21000 | 0.0001 | - |
658
+ | 1.7801 | 21050 | 0.0004 | - |
659
+ | 1.7844 | 21100 | 0.0002 | - |
660
+ | 1.7886 | 21150 | 0.0599 | - |
661
+ | 1.7928 | 21200 | 0.0001 | - |
662
+ | 1.7970 | 21250 | 0.0001 | - |
663
+ | 1.8013 | 21300 | 0.0001 | - |
664
+ | 1.8055 | 21350 | 0.0001 | - |
665
+ | 1.8097 | 21400 | 0.0001 | - |
666
+ | 1.8140 | 21450 | 0.0001 | - |
667
+ | 1.8182 | 21500 | 0.0001 | - |
668
+ | 1.8224 | 21550 | 0.0001 | - |
669
+ | 1.8266 | 21600 | 0.0001 | - |
670
+ | 1.8309 | 21650 | 0.0013 | - |
671
+ | 1.8351 | 21700 | 0.0002 | - |
672
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673
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674
+ | 1.8478 | 21850 | 0.0001 | - |
675
+ | 1.8520 | 21900 | 0.0001 | - |
676
+ | 1.8562 | 21950 | 0.0001 | - |
677
+ | 1.8605 | 22000 | 0.0001 | - |
678
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679
+ | 1.8689 | 22100 | 0.0001 | - |
680
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681
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682
+ | 1.8816 | 22250 | 0.0001 | - |
683
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684
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685
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686
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687
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688
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689
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690
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691
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692
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693
+ | 1.9281 | 22800 | 0.0001 | - |
694
+ | 1.9323 | 22850 | 0.0001 | - |
695
+ | 1.9366 | 22900 | 0.0001 | - |
696
+ | 1.9408 | 22950 | 0.0 | - |
697
+ | 1.9450 | 23000 | 0.0016 | - |
698
+ | 1.9493 | 23050 | 0.0001 | - |
699
+ | 1.9535 | 23100 | 0.0002 | - |
700
+ | 1.9577 | 23150 | 0.0001 | - |
701
+ | 1.9619 | 23200 | 0.0001 | - |
702
+ | 1.9662 | 23250 | 0.0001 | - |
703
+ | 1.9704 | 23300 | 0.0001 | - |
704
+ | 1.9746 | 23350 | 0.0001 | - |
705
+ | 1.9789 | 23400 | 0.0001 | - |
706
+ | 1.9831 | 23450 | 0.0001 | - |
707
+ | 1.9873 | 23500 | 0.0016 | - |
708
+ | 1.9915 | 23550 | 0.0001 | - |
709
+ | 1.9958 | 23600 | 0.0001 | - |
710
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711
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712
+ | 2.0085 | 23750 | 0.0017 | - |
713
+ | 2.0127 | 23800 | 0.0001 | - |
714
+ | 2.0169 | 23850 | 0.0 | - |
715
+ | 2.0211 | 23900 | 0.0001 | - |
716
+ | 2.0254 | 23950 | 0.0001 | - |
717
+ | 2.0296 | 24000 | 0.0001 | - |
718
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719
+ | 2.0381 | 24100 | 0.0001 | - |
720
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721
+ | 2.0465 | 24200 | 0.0001 | - |
722
+ | 2.0507 | 24250 | 0.0001 | - |
723
+ | 2.0550 | 24300 | 0.0001 | - |
724
+ | 2.0592 | 24350 | 0.0001 | - |
725
+ | 2.0634 | 24400 | 0.0001 | - |
726
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727
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728
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729
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730
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731
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732
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733
+ | 2.0973 | 24800 | 0.0001 | - |
734
+ | 2.1015 | 24850 | 0.0006 | - |
735
+ | 2.1057 | 24900 | 0.0579 | - |
736
+ | 2.1099 | 24950 | 0.0001 | - |
737
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738
+ | 2.1184 | 25050 | 0.0011 | - |
739
+ | 2.1226 | 25100 | 0.0001 | - |
740
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741
+ | 2.1311 | 25200 | 0.0003 | - |
742
+ | 2.1353 | 25250 | 0.0001 | - |
743
+ | 2.1395 | 25300 | 0.0014 | - |
744
+ | 2.1438 | 25350 | 0.0001 | - |
745
+ | 2.1480 | 25400 | 0.0002 | - |
746
+ | 2.1522 | 25450 | 0.0012 | - |
747
+ | 2.1564 | 25500 | 0.0001 | - |
748
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749
+ | 2.1649 | 25600 | 0.0002 | - |
750
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751
+ | 2.1734 | 25700 | 0.0001 | - |
752
+ | 2.1776 | 25750 | 0.0001 | - |
753
+ | 2.1818 | 25800 | 0.0001 | - |
754
+ | 2.1860 | 25850 | 0.0544 | - |
755
+ | 2.1903 | 25900 | 0.0001 | - |
756
+ | 2.1945 | 25950 | 0.0001 | - |
757
+ | 2.1987 | 26000 | 0.0001 | - |
758
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759
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760
+ | 2.2114 | 26150 | 0.0001 | - |
761
+ | 2.2156 | 26200 | 0.0002 | - |
762
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763
+ | 2.2241 | 26300 | 0.0001 | - |
764
+ | 2.2283 | 26350 | 0.0002 | - |
765
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766
+ | 2.2368 | 26450 | 0.0001 | - |
767
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768
+ | 2.2452 | 26550 | 0.0022 | - |
769
+ | 2.2495 | 26600 | 0.0001 | - |
770
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771
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772
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773
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774
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775
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776
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777
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778
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779
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780
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781
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782
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783
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784
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785
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786
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787
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788
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789
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790
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791
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792
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793
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794
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795
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796
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797
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798
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799
+ | 2.3763 | 28100 | 0.0001 | - |
800
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801
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802
+ | 2.3890 | 28250 | 0.0001 | - |
803
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804
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805
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806
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807
+ | 2.4101 | 28500 | 0.0001 | - |
808
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809
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810
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811
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812
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813
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814
+ | 2.4397 | 28850 | 0.0001 | - |
815
+ | 2.4440 | 28900 | 0.0001 | - |
816
+ | 2.4482 | 28950 | 0.0001 | - |
817
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818
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819
+ | 2.4609 | 29100 | 0.0001 | - |
820
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821
+ | 2.4693 | 29200 | 0.0001 | - |
822
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823
+ | 2.4778 | 29300 | 0.0002 | - |
824
+ | 2.4820 | 29350 | 0.0002 | - |
825
+ | 2.4863 | 29400 | 0.0001 | - |
826
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827
+ | 2.4947 | 29500 | 0.0002 | - |
828
+ | 2.4989 | 29550 | 0.0013 | - |
829
+ | 2.5032 | 29600 | 0.0001 | - |
830
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831
+ | 2.5116 | 29700 | 0.0001 | - |
832
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833
+ | 2.5201 | 29800 | 0.0015 | - |
834
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835
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836
+ | 2.5328 | 29950 | 0.0001 | - |
837
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838
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839
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840
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841
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842
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843
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844
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845
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846
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847
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848
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849
+ | 2.5877 | 30600 | 0.0001 | - |
850
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851
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852
+ | 2.6004 | 30750 | 0.0001 | - |
853
+ | 2.6047 | 30800 | 0.0001 | - |
854
+ | 2.6089 | 30850 | 0.0001 | - |
855
+ | 2.6131 | 30900 | 0.0001 | - |
856
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857
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858
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859
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860
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861
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862
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863
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864
+ | 2.6512 | 31350 | 0.0024 | - |
865
+ | 2.6554 | 31400 | 0.0001 | - |
866
+ | 2.6596 | 31450 | 0.0001 | - |
867
+ | 2.6638 | 31500 | 0.0025 | - |
868
+ | 2.6681 | 31550 | 0.0001 | - |
869
+ | 2.6723 | 31600 | 0.0001 | - |
870
+ | 2.6765 | 31650 | 0.0002 | - |
871
+ | 2.6808 | 31700 | 0.0001 | - |
872
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873
+ | 2.6892 | 31800 | 0.0001 | - |
874
+ | 2.6934 | 31850 | 0.0001 | - |
875
+ | 2.6977 | 31900 | 0.0001 | - |
876
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877
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878
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879
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880
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881
+ | 2.7230 | 32200 | 0.0001 | - |
882
+ | 2.7273 | 32250 | 0.0001 | - |
883
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884
+ | 2.7357 | 32350 | 0.0001 | - |
885
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886
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887
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888
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889
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890
+ | 2.7611 | 32650 | 0.0001 | - |
891
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892
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893
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894
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895
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896
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897
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898
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899
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900
+ | 2.8034 | 33150 | 0.0001 | - |
901
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902
+ | 2.8118 | 33250 | 0.0001 | - |
903
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904
+ | 2.8203 | 33350 | 0.0001 | - |
905
+ | 2.8245 | 33400 | 0.0001 | - |
906
+ | 2.8288 | 33450 | 0.0001 | - |
907
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908
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909
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910
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911
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912
+ | 2.8541 | 33750 | 0.0016 | - |
913
+ | 2.8584 | 33800 | 0.0001 | - |
914
+ | 2.8626 | 33850 | 0.0001 | - |
915
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916
+ | 2.8710 | 33950 | 0.0001 | - |
917
+ | 2.8753 | 34000 | 0.0001 | - |
918
+ | 2.8795 | 34050 | 0.0001 | - |
919
+ | 2.8837 | 34100 | 0.0001 | - |
920
+ | 2.8879 | 34150 | 0.0001 | - |
921
+ | 2.8922 | 34200 | 0.0 | - |
922
+ | 2.8964 | 34250 | 0.0001 | - |
923
+ | 2.9006 | 34300 | 0.0001 | - |
924
+ | 2.9049 | 34350 | 0.0001 | - |
925
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926
+ | 2.9133 | 34450 | 0.0001 | - |
927
+ | 2.9175 | 34500 | 0.0001 | - |
928
+ | 2.9218 | 34550 | 0.0 | - |
929
+ | 2.9260 | 34600 | 0.0001 | - |
930
+ | 2.9302 | 34650 | 0.0001 | - |
931
+ | 2.9345 | 34700 | 0.0001 | - |
932
+ | 2.9387 | 34750 | 0.0155 | - |
933
+ | 2.9429 | 34800 | 0.0001 | - |
934
+ | 2.9471 | 34850 | 0.0 | - |
935
+ | 2.9514 | 34900 | 0.0001 | - |
936
+ | 2.9556 | 34950 | 0.0001 | - |
937
+ | 2.9598 | 35000 | 0.0001 | - |
938
+ | 2.9641 | 35050 | 0.0 | - |
939
+ | 2.9683 | 35100 | 0.0018 | - |
940
+ | 2.9725 | 35150 | 0.0001 | - |
941
+ | 2.9767 | 35200 | 0.0001 | - |
942
+ | 2.9810 | 35250 | 0.0001 | - |
943
+ | 2.9852 | 35300 | 0.0001 | - |
944
+ | 2.9894 | 35350 | 0.0001 | - |
945
+ | 2.9937 | 35400 | 0.0001 | - |
946
+ | 2.9979 | 35450 | 0.0001 | - |
947
+ | 3.0 | 35475 | - | 0.0003 |
948
+ | 3.0021 | 35500 | 0.0001 | - |
949
+ | 3.0063 | 35550 | 0.0001 | - |
950
+ | 3.0106 | 35600 | 0.0022 | - |
951
+ | 3.0148 | 35650 | 0.0001 | - |
952
+ | 3.0190 | 35700 | 0.0001 | - |
953
+ | 3.0233 | 35750 | 0.0001 | - |
954
+ | 3.0275 | 35800 | 0.0 | - |
955
+ | 3.0317 | 35850 | 0.0019 | - |
956
+ | 3.0359 | 35900 | 0.0 | - |
957
+ | 3.0402 | 35950 | 0.0001 | - |
958
+ | 3.0444 | 36000 | 0.0001 | - |
959
+ | 3.0486 | 36050 | 0.0001 | - |
960
+ | 3.0529 | 36100 | 0.0 | - |
961
+ | 3.0571 | 36150 | 0.0 | - |
962
+ | 3.0613 | 36200 | 0.0001 | - |
963
+ | 3.0655 | 36250 | 0.0001 | - |
964
+ | 3.0698 | 36300 | 0.0001 | - |
965
+ | 3.0740 | 36350 | 0.0001 | - |
966
+ | 3.0782 | 36400 | 0.0001 | - |
967
+ | 3.0825 | 36450 | 0.0 | - |
968
+ | 3.0867 | 36500 | 0.0001 | - |
969
+ | 3.0909 | 36550 | 0.0001 | - |
970
+ | 3.0951 | 36600 | 0.0001 | - |
971
+ | 3.0994 | 36650 | 0.0001 | - |
972
+ | 3.1036 | 36700 | 0.0001 | - |
973
+ | 3.1078 | 36750 | 0.0 | - |
974
+ | 3.1121 | 36800 | 0.0001 | - |
975
+ | 3.1163 | 36850 | 0.0001 | - |
976
+ | 3.1205 | 36900 | 0.0 | - |
977
+ | 3.1247 | 36950 | 0.0001 | - |
978
+ | 3.1290 | 37000 | 0.0001 | - |
979
+ | 3.1332 | 37050 | 0.0001 | - |
980
+ | 3.1374 | 37100 | 0.0001 | - |
981
+ | 3.1416 | 37150 | 0.0001 | - |
982
+ | 3.1459 | 37200 | 0.0001 | - |
983
+ | 3.1501 | 37250 | 0.0001 | - |
984
+ | 3.1543 | 37300 | 0.0001 | - |
985
+ | 3.1586 | 37350 | 0.0001 | - |
986
+ | 3.1628 | 37400 | 0.0055 | - |
987
+ | 3.1670 | 37450 | 0.0 | - |
988
+ | 3.1712 | 37500 | 0.0001 | - |
989
+ | 3.1755 | 37550 | 0.0019 | - |
990
+ | 3.1797 | 37600 | 0.0001 | - |
991
+ | 3.1839 | 37650 | 0.0001 | - |
992
+ | 3.1882 | 37700 | 0.0 | - |
993
+ | 3.1924 | 37750 | 0.0 | - |
994
+ | 3.1966 | 37800 | 0.0001 | - |
995
+ | 3.2008 | 37850 | 0.0001 | - |
996
+ | 3.2051 | 37900 | 0.0 | - |
997
+ | 3.2093 | 37950 | 0.0001 | - |
998
+ | 3.2135 | 38000 | 0.0001 | - |
999
+ | 3.2178 | 38050 | 0.0001 | - |
1000
+ | 3.2220 | 38100 | 0.0 | - |
1001
+ | 3.2262 | 38150 | 0.0001 | - |
1002
+ | 3.2304 | 38200 | 0.0 | - |
1003
+ | 3.2347 | 38250 | 0.0001 | - |
1004
+ | 3.2389 | 38300 | 0.0001 | - |
1005
+ | 3.2431 | 38350 | 0.0 | - |
1006
+ | 3.2474 | 38400 | 0.0001 | - |
1007
+ | 3.2516 | 38450 | 0.0001 | - |
1008
+ | 3.2558 | 38500 | 0.0 | - |
1009
+ | 3.2600 | 38550 | 0.0 | - |
1010
+ | 3.2643 | 38600 | 0.0 | - |
1011
+ | 3.2685 | 38650 | 0.0017 | - |
1012
+ | 3.2727 | 38700 | 0.0095 | - |
1013
+ | 3.2770 | 38750 | 0.0001 | - |
1014
+ | 3.2812 | 38800 | 0.0001 | - |
1015
+ | 3.2854 | 38850 | 0.0 | - |
1016
+ | 3.2896 | 38900 | 0.0001 | - |
1017
+ | 3.2939 | 38950 | 0.0 | - |
1018
+ | 3.2981 | 39000 | 0.0001 | - |
1019
+ | 3.3023 | 39050 | 0.0 | - |
1020
+ | 3.3066 | 39100 | 0.0001 | - |
1021
+ | 3.3108 | 39150 | 0.0 | - |
1022
+ | 3.3150 | 39200 | 0.0 | - |
1023
+ | 3.3192 | 39250 | 0.0001 | - |
1024
+ | 3.3235 | 39300 | 0.0001 | - |
1025
+ | 3.3277 | 39350 | 0.0 | - |
1026
+ | 3.3319 | 39400 | 0.0001 | - |
1027
+ | 3.3362 | 39450 | 0.0001 | - |
1028
+ | 3.3404 | 39500 | 0.0001 | - |
1029
+ | 3.3446 | 39550 | 0.0 | - |
1030
+ | 3.3488 | 39600 | 0.0001 | - |
1031
+ | 3.3531 | 39650 | 0.0 | - |
1032
+ | 3.3573 | 39700 | 0.0001 | - |
1033
+ | 3.3615 | 39750 | 0.0001 | - |
1034
+ | 3.3658 | 39800 | 0.0022 | - |
1035
+ | 3.3700 | 39850 | 0.0001 | - |
1036
+ | 3.3742 | 39900 | 0.0001 | - |
1037
+ | 3.3784 | 39950 | 0.0 | - |
1038
+ | 3.3827 | 40000 | 0.0 | - |
1039
+ | 3.3869 | 40050 | 0.0 | - |
1040
+ | 3.3911 | 40100 | 0.0001 | - |
1041
+ | 3.3953 | 40150 | 0.0 | - |
1042
+ | 3.3996 | 40200 | 0.0 | - |
1043
+ | 3.4038 | 40250 | 0.0 | - |
1044
+ | 3.4080 | 40300 | 0.0001 | - |
1045
+ | 3.4123 | 40350 | 0.0 | - |
1046
+ | 3.4165 | 40400 | 0.0001 | - |
1047
+ | 3.4207 | 40450 | 0.0 | - |
1048
+ | 3.4249 | 40500 | 0.0001 | - |
1049
+ | 3.4292 | 40550 | 0.0001 | - |
1050
+ | 3.4334 | 40600 | 0.0001 | - |
1051
+ | 3.4376 | 40650 | 0.0 | - |
1052
+ | 3.4419 | 40700 | 0.0001 | - |
1053
+ | 3.4461 | 40750 | 0.0 | - |
1054
+ | 3.4503 | 40800 | 0.0 | - |
1055
+ | 3.4545 | 40850 | 0.0 | - |
1056
+ | 3.4588 | 40900 | 0.0 | - |
1057
+ | 3.4630 | 40950 | 0.0001 | - |
1058
+ | 3.4672 | 41000 | 0.0 | - |
1059
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1060
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1062
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1063
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1064
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1065
+ | 3.4968 | 41350 | 0.0001 | - |
1066
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1067
+ | 3.5053 | 41450 | 0.0 | - |
1068
+ | 3.5095 | 41500 | 0.0001 | - |
1069
+ | 3.5137 | 41550 | 0.0 | - |
1070
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1072
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1081
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1083
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+ | **4.0** | **47300** | **0.0** | **0.0002** |
1185
+
1186
+ * The bold row denotes the saved checkpoint.
1187
+ ### Framework Versions
1188
+ - Python: 3.10.14
1189
+ - SetFit: 1.0.3
1190
+ - Sentence Transformers: 3.0.1
1191
+ - Transformers: 4.39.0
1192
+ - PyTorch: 2.4.0+cu121
1193
+ - Datasets: 2.20.0
1194
+ - Tokenizers: 0.15.2
1195
+
1196
+ ## Citation
1197
+
1198
+ ### BibTeX
1199
+ ```bibtex
1200
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
1201
+ doi = {10.48550/ARXIV.2209.11055},
1202
+ url = {https://arxiv.org/abs/2209.11055},
1203
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
1204
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
1205
+ title = {Efficient Few-Shot Learning Without Prompts},
1206
+ publisher = {arXiv},
1207
+ year = {2022},
1208
+ copyright = {Creative Commons Attribution 4.0 International}
1209
+ }
1210
+ ```
1211
+
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+ <!--
1213
+ ## Glossary
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+
1215
+ *Clearly define terms in order to be accessible across audiences.*
1216
+ -->
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+
1218
+ <!--
1219
+ ## Model Card Authors
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+
1221
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
1222
+ -->
1223
+
1224
+ <!--
1225
+ ## Model Card Contact
1226
+
1227
+ *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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