---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Why should companies invest in UX design?
- text: Evaluate the efficiency of the current workflow.
- text: I need a resume for a finance analyst.
- text: Generate ideas for improving employee satisfaction.
- text: Generate a campaign for increasing our Instagram followers.
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9977272727272727
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 44 classes
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:-------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| analyze | - 'Analyze the results from the A/B testing.'
- 'Evaluate the effectiveness of the new strategy.'
- 'What are the key insights from the customer survey?'
|
| analyze advantages | - 'Analyze the advantages of social media marketing for startups.'
- 'Analyze the advantages of electric vehicles over gas-powered cars.'
- 'What are the benefits of a plant-based diet for health?'
|
| analyze best practices | - 'What are the industry standards for data security?'
- 'Evaluate best practices for customer service.'
- 'Analyze best practices for social media marketing.'
|
| analyze business proposal | - 'Analyze the competitive analysis in the business plan.'
- 'Evaluate the team structure mentioned in the proposal.'
- 'What are the key points in the executive summary?'
|
| analyze data | - 'What does the data tell us about user engagement?'
- 'Analyze the sales data for the last quarter.'
- 'Analyze the data to determine customer preferences.'
|
| analyze data backup and recovery | - 'Evaluate the effectiveness of the backup strategy.'
- 'Analyze the current data backup procedures.'
- 'What are the risks associated with our data recovery plan?'
|
| analyze data visualization | - 'What does this bar chart tell us about customer demographics?'
- 'Interpret the data in this line chart.'
- 'Analyze the distribution shown in this histogram.'
|
| analyze feedback | - 'Analyze customer feedback from the recent survey.'
- 'Analyze the feedback received from the beta testers.'
- 'Evaluate the feedback from the focus group.'
|
| analyze information | - 'What are the main points from the research findings?'
- 'Evaluate the information from the competitor analysis.'
- 'What conclusions can be drawn from the survey results?'
|
| analyze information technology security policy | - 'Evaluate the risks mentioned in the security policy.'
- "Analyze the company's IT security policy."
- 'What are the strengths of our IT security measures?'
|
| analyze job descriptions | - 'Analyze the job description for the new position.'
- 'What are the key responsibilities listed in the job description?'
- 'Evaluate the job description for inclusivity.'
|
| analyze marketing campaign | - 'Evaluate the customer conversion rates from the Google Ads campaign.'
- 'What were the engagement rates for the spring sale campaign?'
- 'Assess the performance of the influencer marketing strategy.'
|
| analyze packaging design | - 'What are the strengths and weaknesses of the packaging?'
- 'Evaluate the impact of packaging on brand perception.'
- 'Analyze the cost-effectiveness of the packaging design.'
|
| analyze process | - 'What are the key steps in our product development process?'
- 'Evaluate the process for software deployment.'
- 'Analyze the process for onboarding new employees.'
|
| analyze product description | - 'What are the strengths of this product description?'
- 'Evaluate the clarity of the product description.'
- 'Analyze the persuasiveness of the product features.'
|
| analyze product rebranding | - 'What were the challenges faced during rebranding?'
- 'What are the key changes in the new branding?'
- 'Evaluate the customer response to the rebranding effort.'
|
| analyze product recall | - 'Analyze the customer feedback after the recall.'
- 'What were the financial implications of the recall?'
- 'Evaluate the effectiveness of the recall process.'
|
| analyze social media campaign | - 'Evaluate the reach and impressions of the LinkedIn posts.'
- 'Analyze the effectiveness of the Twitter campaign.'
- 'What improvements can be made to our social media campaigns?'
|
| analyze time management | - 'Analyze how I can better prioritize my tasks.'
- 'Analyze my current time management techniques.'
- 'What are the weaknesses in my time management?'
|
| analyze trends | - 'Analyze the social media trends influencing businesses.'
- 'What are the current trends in digital marketing?'
- 'Analyze the latest trends in the tech industry.'
|
| analyze website concept | - 'Analyze the content strategy of the new website.'
- 'What are the key elements of a successful website concept?'
- 'Analyze the mobile responsiveness of the website design.'
|
| bake | - 'Bake a loaf of banana bread.'
- 'How do I bake a cheesecake?'
- 'Bake a batch of brownies.'
|
| define | - "Define the term 'machine learning'."
- "What does 'SEO' stand for?"
- "Define 'data analytics'."
|
| explain | - "Explain the importance of cybersecurity in today's world."
- 'Explain how machine learning works.'
- 'Can you clarify what SEO involves?'
|
| explain the importance of user experience design | - 'Explain how UX design improves accessibility.'
- 'Why is user experience design important for websites?'
- 'Why should companies invest in UX design?'
|
| generate business proposal | - 'What is the format for a business proposal?'
- 'Generate a business proposal for a new product line.'
- 'Create a proposal for a partnership with another company.'
|
| generate crisis communication plan | - 'Create a communication plan for a financial crisis.'
- 'Create a plan for communicating with stakeholders in a crisis.'
- 'Generate a plan for internal communication during a crisis.'
|
| generate ideas | - 'Generate ideas for improving employee satisfaction.'
- 'Come up with ideas for our company’s anniversary event.'
- 'What are some unique selling points for our service?'
|
| generate learning plan | - 'Create a learning plan for understanding machine learning concepts.'
- 'Create a plan for learning digital marketing skills.'
- 'What should be included in a learning plan for data science?'
|
| generate product description | - 'Generate a product description for the new smartphone.'
- 'Create a detailed description of the latest software.'
- 'What should be included in a product description?'
|
| generate product roadmap | - 'Create a roadmap for the new software development.'
- 'What should be included in a product roadmap?'
- 'Generate a product roadmap for customer feedback integration.'
|
| generate project proposal | - 'What are the key elements of a project proposal?'
- 'What should be included in a project proposal?'
- 'Generate a proposal for a research project.'
|
| generate recommendations | - 'Provide recommendations for streamlining operations.'
- 'Generate recommendations for improving customer service.'
- 'What are your recommendations for the new marketing strategy?'
|
| generate resume | - 'I need a resume for a teaching position.'
- 'Generate a resume for a software engineer.'
- 'Generate a resume for a data scientist.'
|
| generate social media campaign | - "Create a campaign to highlight our company's sustainability efforts."
- 'Generate a campaign for increasing our Instagram followers.'
- 'I need a campaign plan for promoting our summer sale.'
|
| generate template | - 'Can you make a template for job descriptions?'
- 'Create a template for a project proposal.'
- 'Generate a meeting agenda template.'
|
| generate training program outline | - 'Generate a training program outline for new employees.'
- 'Generate an outline for diversity and inclusion training.'
- 'Create an outline for a leadership training program.'
|
| learn a language | - 'How do I become fluent in Portuguese?'
- 'How can I practice English pronunciation?'
- 'What is the best way to learn Chinese characters?'
|
| manage time | - 'How do I balance work and personal life?'
- 'Tips for managing time during exams.'
- 'How do I create a daily schedule?'
|
| outline steps | - 'What are the steps to develop a training program?'
- 'Outline the steps to launch a new product.'
- 'Outline the steps to implement a new software system.'
|
| provide general information | - 'Can you give me an overview of the new software?'
- "Give me general information about the industry's trends."
- 'What are the key points about the product launch?'
|
| recommend | - 'What are the top destinations for a vacation?'
- 'What podcasts would you suggest for entrepreneurs?'
- 'Recommend a good book on data science.'
|
| summarize advantages | - 'Summarize the advantages of using renewable energy.'
- 'Summarize the advantages of social media marketing.'
- 'What are the benefits of using project management software?'
|
| summarize financial report | - 'Summarize the main findings of the quarterly financial report.'
- 'Provide a summary of the financial projections.'
- 'What are the key metrics in the financial summary?'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9977 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("nmlemus/setfit-paraphrase-mpnet-base-v2-surepath-chatgtp-dataset")
# Run inference
preds = model("I need a resume for a finance analyst.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 3 | 7.8795 | 13 |
| Label | Training Sample Count |
|:-------------------------------------------------|:----------------------|
| analyze | 10 |
| analyze advantages | 10 |
| analyze best practices | 10 |
| analyze business proposal | 10 |
| analyze data | 10 |
| analyze data backup and recovery | 10 |
| analyze data visualization | 10 |
| analyze feedback | 10 |
| analyze information | 10 |
| analyze information technology security policy | 10 |
| analyze job descriptions | 10 |
| analyze marketing campaign | 10 |
| analyze packaging design | 10 |
| analyze process | 10 |
| analyze product description | 10 |
| analyze product rebranding | 10 |
| analyze product recall | 10 |
| analyze social media campaign | 10 |
| analyze time management | 10 |
| analyze trends | 10 |
| analyze website concept | 10 |
| bake | 10 |
| define | 10 |
| explain | 10 |
| explain the importance of user experience design | 10 |
| generate business proposal | 10 |
| generate crisis communication plan | 10 |
| generate ideas | 10 |
| generate learning plan | 10 |
| generate product description | 10 |
| generate product roadmap | 10 |
| generate project proposal | 10 |
| generate recommendations | 10 |
| generate resume | 10 |
| generate social media campaign | 10 |
| generate template | 10 |
| generate training program outline | 10 |
| learn a language | 10 |
| manage time | 10 |
| outline steps | 10 |
| provide general information | 10 |
| recommend | 10 |
| summarize advantages | 10 |
| summarize financial report | 10 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:---------:|:-------------:|:---------------:|
| 0.0001 | 1 | 0.1037 | - |
| 0.0042 | 50 | 0.1544 | - |
| 0.0085 | 100 | 0.1555 | - |
| 0.0127 | 150 | 0.0948 | - |
| 0.0169 | 200 | 0.1176 | - |
| 0.0211 | 250 | 0.1108 | - |
| 0.0254 | 300 | 0.1169 | - |
| 0.0296 | 350 | 0.1291 | - |
| 0.0338 | 400 | 0.1068 | - |
| 0.0381 | 450 | 0.1369 | - |
| 0.0423 | 500 | 0.0823 | - |
| 0.0465 | 550 | 0.0732 | - |
| 0.0507 | 600 | 0.1006 | - |
| 0.0550 | 650 | 0.0638 | - |
| 0.0592 | 700 | 0.0818 | - |
| 0.0634 | 750 | 0.0542 | - |
| 0.0677 | 800 | 0.039 | - |
| 0.0719 | 850 | 0.0497 | - |
| 0.0761 | 900 | 0.016 | - |
| 0.0803 | 950 | 0.021 | - |
| 0.0846 | 1000 | 0.0136 | - |
| 0.0888 | 1050 | 0.0353 | - |
| 0.0930 | 1100 | 0.0164 | - |
| 0.0973 | 1150 | 0.0123 | - |
| 0.1015 | 1200 | 0.0218 | - |
| 0.1057 | 1250 | 0.0845 | - |
| 0.1099 | 1300 | 0.0082 | - |
| 0.1142 | 1350 | 0.0385 | - |
| 0.1184 | 1400 | 0.0087 | - |
| 0.1226 | 1450 | 0.0133 | - |
| 0.1268 | 1500 | 0.0045 | - |
| 0.1311 | 1550 | 0.0054 | - |
| 0.1353 | 1600 | 0.0078 | - |
| 0.1395 | 1650 | 0.0068 | - |
| 0.1438 | 1700 | 0.0586 | - |
| 0.1480 | 1750 | 0.0173 | - |
| 0.1522 | 1800 | 0.0585 | - |
| 0.1564 | 1850 | 0.0052 | - |
| 0.1607 | 1900 | 0.0046 | - |
| 0.1649 | 1950 | 0.0021 | - |
| 0.1691 | 2000 | 0.0092 | - |
| 0.1734 | 2050 | 0.0027 | - |
| 0.1776 | 2100 | 0.0041 | - |
| 0.1818 | 2150 | 0.0053 | - |
| 0.1860 | 2200 | 0.0585 | - |
| 0.1903 | 2250 | 0.0034 | - |
| 0.1945 | 2300 | 0.0601 | - |
| 0.1987 | 2350 | 0.0061 | - |
| 0.2030 | 2400 | 0.0022 | - |
| 0.2072 | 2450 | 0.0037 | - |
| 0.2114 | 2500 | 0.0019 | - |
| 0.2156 | 2550 | 0.0012 | - |
| 0.2199 | 2600 | 0.0031 | - |
| 0.2241 | 2650 | 0.0028 | - |
| 0.2283 | 2700 | 0.0011 | - |
| 0.2326 | 2750 | 0.0019 | - |
| 0.2368 | 2800 | 0.0638 | - |
| 0.2410 | 2850 | 0.0018 | - |
| 0.2452 | 2900 | 0.0017 | - |
| 0.2495 | 2950 | 0.0021 | - |
| 0.2537 | 3000 | 0.0016 | - |
| 0.2579 | 3050 | 0.0013 | - |
| 0.2622 | 3100 | 0.0017 | - |
| 0.2664 | 3150 | 0.0101 | - |
| 0.2706 | 3200 | 0.0029 | - |
| 0.2748 | 3250 | 0.0013 | - |
| 0.2791 | 3300 | 0.002 | - |
| 0.2833 | 3350 | 0.0079 | - |
| 0.2875 | 3400 | 0.0013 | - |
| 0.2918 | 3450 | 0.001 | - |
| 0.2960 | 3500 | 0.0015 | - |
| 0.3002 | 3550 | 0.0013 | - |
| 0.3044 | 3600 | 0.0017 | - |
| 0.3087 | 3650 | 0.0012 | - |
| 0.3129 | 3700 | 0.0007 | - |
| 0.3171 | 3750 | 0.0019 | - |
| 0.3214 | 3800 | 0.0008 | - |
| 0.3256 | 3850 | 0.0008 | - |
| 0.3298 | 3900 | 0.0007 | - |
| 0.3340 | 3950 | 0.0007 | - |
| 0.3383 | 4000 | 0.001 | - |
| 0.3425 | 4050 | 0.0005 | - |
| 0.3467 | 4100 | 0.0008 | - |
| 0.3510 | 4150 | 0.0007 | - |
| 0.3552 | 4200 | 0.0014 | - |
| 0.3594 | 4250 | 0.0005 | - |
| 0.3636 | 4300 | 0.0008 | - |
| 0.3679 | 4350 | 0.0006 | - |
| 0.3721 | 4400 | 0.0011 | - |
| 0.3763 | 4450 | 0.0006 | - |
| 0.3805 | 4500 | 0.0007 | - |
| 0.3848 | 4550 | 0.0006 | - |
| 0.3890 | 4600 | 0.0003 | - |
| 0.3932 | 4650 | 0.0022 | - |
| 0.3975 | 4700 | 0.0007 | - |
| 0.4017 | 4750 | 0.0031 | - |
| 0.4059 | 4800 | 0.0013 | - |
| 0.4101 | 4850 | 0.0015 | - |
| 0.4144 | 4900 | 0.0017 | - |
| 0.4186 | 4950 | 0.0007 | - |
| 0.4228 | 5000 | 0.0006 | - |
| 0.4271 | 5050 | 0.0006 | - |
| 0.4313 | 5100 | 0.0013 | - |
| 0.4355 | 5150 | 0.0003 | - |
| 0.4397 | 5200 | 0.12 | - |
| 0.4440 | 5250 | 0.0005 | - |
| 0.4482 | 5300 | 0.0006 | - |
| 0.4524 | 5350 | 0.0016 | - |
| 0.4567 | 5400 | 0.0008 | - |
| 0.4609 | 5450 | 0.0118 | - |
| 0.4651 | 5500 | 0.0003 | - |
| 0.4693 | 5550 | 0.0542 | - |
| 0.4736 | 5600 | 0.0011 | - |
| 0.4778 | 5650 | 0.0004 | - |
| 0.4820 | 5700 | 0.001 | - |
| 0.4863 | 5750 | 0.0008 | - |
| 0.4905 | 5800 | 0.0008 | - |
| 0.4947 | 5850 | 0.0004 | - |
| 0.4989 | 5900 | 0.0008 | - |
| 0.5032 | 5950 | 0.0009 | - |
| 0.5074 | 6000 | 0.0005 | - |
| 0.5116 | 6050 | 0.0006 | - |
| 0.5159 | 6100 | 0.0012 | - |
| 0.5201 | 6150 | 0.0004 | - |
| 0.5243 | 6200 | 0.0005 | - |
| 0.5285 | 6250 | 0.0007 | - |
| 0.5328 | 6300 | 0.0009 | - |
| 0.5370 | 6350 | 0.0006 | - |
| 0.5412 | 6400 | 0.0007 | - |
| 0.5455 | 6450 | 0.0007 | - |
| 0.5497 | 6500 | 0.0003 | - |
| 0.5539 | 6550 | 0.0568 | - |
| 0.5581 | 6600 | 0.0006 | - |
| 0.5624 | 6650 | 0.0002 | - |
| 0.5666 | 6700 | 0.0006 | - |
| 0.5708 | 6750 | 0.0003 | - |
| 0.5751 | 6800 | 0.0003 | - |
| 0.5793 | 6850 | 0.0004 | - |
| 0.5835 | 6900 | 0.0006 | - |
| 0.5877 | 6950 | 0.0004 | - |
| 0.5920 | 7000 | 0.0004 | - |
| 0.5962 | 7050 | 0.0002 | - |
| 0.6004 | 7100 | 0.0002 | - |
| 0.6047 | 7150 | 0.001 | - |
| 0.6089 | 7200 | 0.0002 | - |
| 0.6131 | 7250 | 0.0004 | - |
| 0.6173 | 7300 | 0.0009 | - |
| 0.6216 | 7350 | 0.0003 | - |
| 0.6258 | 7400 | 0.0003 | - |
| 0.6300 | 7450 | 0.0018 | - |
| 0.6342 | 7500 | 0.0004 | - |
| 0.6385 | 7550 | 0.0035 | - |
| 0.6427 | 7600 | 0.0012 | - |
| 0.6469 | 7650 | 0.0005 | - |
| 0.6512 | 7700 | 0.0003 | - |
| 0.6554 | 7750 | 0.0003 | - |
| 0.6596 | 7800 | 0.0004 | - |
| 0.6638 | 7850 | 0.0004 | - |
| 0.6681 | 7900 | 0.0004 | - |
| 0.6723 | 7950 | 0.0003 | - |
| 0.6765 | 8000 | 0.0002 | - |
| 0.6808 | 8050 | 0.0002 | - |
| 0.6850 | 8100 | 0.0008 | - |
| 0.6892 | 8150 | 0.0003 | - |
| 0.6934 | 8200 | 0.0002 | - |
| 0.6977 | 8250 | 0.0003 | - |
| 0.7019 | 8300 | 0.0002 | - |
| 0.7061 | 8350 | 0.0024 | - |
| 0.7104 | 8400 | 0.0022 | - |
| 0.7146 | 8450 | 0.0004 | - |
| 0.7188 | 8500 | 0.0092 | - |
| 0.7230 | 8550 | 0.0002 | - |
| 0.7273 | 8600 | 0.0001 | - |
| 0.7315 | 8650 | 0.0002 | - |
| 0.7357 | 8700 | 0.0003 | - |
| 0.7400 | 8750 | 0.0005 | - |
| 0.7442 | 8800 | 0.0002 | - |
| 0.7484 | 8850 | 0.0005 | - |
| 0.7526 | 8900 | 0.0002 | - |
| 0.7569 | 8950 | 0.0002 | - |
| 0.7611 | 9000 | 0.0002 | - |
| 0.7653 | 9050 | 0.0002 | - |
| 0.7696 | 9100 | 0.0001 | - |
| 0.7738 | 9150 | 0.0002 | - |
| 0.7780 | 9200 | 0.0004 | - |
| 0.7822 | 9250 | 0.0003 | - |
| 0.7865 | 9300 | 0.0003 | - |
| 0.7907 | 9350 | 0.0002 | - |
| 0.7949 | 9400 | 0.0005 | - |
| 0.7992 | 9450 | 0.0002 | - |
| 0.8034 | 9500 | 0.0002 | - |
| 0.8076 | 9550 | 0.0017 | - |
| 0.8118 | 9600 | 0.0004 | - |
| 0.8161 | 9650 | 0.0003 | - |
| 0.8203 | 9700 | 0.0002 | - |
| 0.8245 | 9750 | 0.0002 | - |
| 0.8288 | 9800 | 0.0001 | - |
| 0.8330 | 9850 | 0.0001 | - |
| 0.8372 | 9900 | 0.0001 | - |
| 0.8414 | 9950 | 0.0005 | - |
| 0.8457 | 10000 | 0.0001 | - |
| 0.8499 | 10050 | 0.0001 | - |
| 0.8541 | 10100 | 0.0002 | - |
| 0.8584 | 10150 | 0.0002 | - |
| 0.8626 | 10200 | 0.0003 | - |
| 0.8668 | 10250 | 0.0003 | - |
| 0.8710 | 10300 | 0.0002 | - |
| 0.8753 | 10350 | 0.0002 | - |
| 0.8795 | 10400 | 0.001 | - |
| 0.8837 | 10450 | 0.0008 | - |
| 0.8879 | 10500 | 0.0005 | - |
| 0.8922 | 10550 | 0.0017 | - |
| 0.8964 | 10600 | 0.0606 | - |
| 0.9006 | 10650 | 0.0002 | - |
| 0.9049 | 10700 | 0.0003 | - |
| 0.9091 | 10750 | 0.0005 | - |
| 0.9133 | 10800 | 0.0008 | - |
| 0.9175 | 10850 | 0.0003 | - |
| 0.9218 | 10900 | 0.002 | - |
| 0.9260 | 10950 | 0.0003 | - |
| 0.9302 | 11000 | 0.0003 | - |
| 0.9345 | 11050 | 0.0003 | - |
| 0.9387 | 11100 | 0.0243 | - |
| 0.9429 | 11150 | 0.0016 | - |
| 0.9471 | 11200 | 0.021 | - |
| 0.9514 | 11250 | 0.0003 | - |
| 0.9556 | 11300 | 0.0006 | - |
| 0.9598 | 11350 | 0.0166 | - |
| 0.9641 | 11400 | 0.0014 | - |
| 0.9683 | 11450 | 0.0004 | - |
| 0.9725 | 11500 | 0.0006 | - |
| 0.9767 | 11550 | 0.0001 | - |
| 0.9810 | 11600 | 0.0002 | - |
| 0.9852 | 11650 | 0.0021 | - |
| 0.9894 | 11700 | 0.0004 | - |
| 0.9937 | 11750 | 0.0002 | - |
| 0.9979 | 11800 | 0.0003 | - |
| 1.0 | 11825 | - | 0.0019 |
| 1.0021 | 11850 | 0.0002 | - |
| 1.0063 | 11900 | 0.0002 | - |
| 1.0106 | 11950 | 0.0002 | - |
| 1.0148 | 12000 | 0.0002 | - |
| 1.0190 | 12050 | 0.0002 | - |
| 1.0233 | 12100 | 0.0002 | - |
| 1.0275 | 12150 | 0.0002 | - |
| 1.0317 | 12200 | 0.0002 | - |
| 1.0359 | 12250 | 0.0005 | - |
| 1.0402 | 12300 | 0.0002 | - |
| 1.0444 | 12350 | 0.0002 | - |
| 1.0486 | 12400 | 0.0004 | - |
| 1.0529 | 12450 | 0.0002 | - |
| 1.0571 | 12500 | 0.0002 | - |
| 1.0613 | 12550 | 0.0001 | - |
| 1.0655 | 12600 | 0.0001 | - |
| 1.0698 | 12650 | 0.0001 | - |
| 1.0740 | 12700 | 0.0001 | - |
| 1.0782 | 12750 | 0.0001 | - |
| 1.0825 | 12800 | 0.0002 | - |
| 1.0867 | 12850 | 0.0001 | - |
| 1.0909 | 12900 | 0.0002 | - |
| 1.0951 | 12950 | 0.0002 | - |
| 1.0994 | 13000 | 0.0002 | - |
| 1.1036 | 13050 | 0.0002 | - |
| 1.1078 | 13100 | 0.0001 | - |
| 1.1121 | 13150 | 0.0002 | - |
| 1.1163 | 13200 | 0.0236 | - |
| 1.1205 | 13250 | 0.0002 | - |
| 1.1247 | 13300 | 0.0001 | - |
| 1.1290 | 13350 | 0.0023 | - |
| 1.1332 | 13400 | 0.0003 | - |
| 1.1374 | 13450 | 0.0001 | - |
| 1.1416 | 13500 | 0.0003 | - |
| 1.1459 | 13550 | 0.0003 | - |
| 1.1501 | 13600 | 0.0004 | - |
| 1.1543 | 13650 | 0.0002 | - |
| 1.1586 | 13700 | 0.0002 | - |
| 1.1628 | 13750 | 0.0001 | - |
| 1.1670 | 13800 | 0.0001 | - |
| 1.1712 | 13850 | 0.0001 | - |
| 1.1755 | 13900 | 0.0001 | - |
| 1.1797 | 13950 | 0.0001 | - |
| 1.1839 | 14000 | 0.0001 | - |
| 1.1882 | 14050 | 0.0002 | - |
| 1.1924 | 14100 | 0.0002 | - |
| 1.1966 | 14150 | 0.0001 | - |
| 1.2008 | 14200 | 0.0002 | - |
| 1.2051 | 14250 | 0.0003 | - |
| 1.2093 | 14300 | 0.0001 | - |
| 1.2135 | 14350 | 0.0001 | - |
| 1.2178 | 14400 | 0.0002 | - |
| 1.2220 | 14450 | 0.001 | - |
| 1.2262 | 14500 | 0.0001 | - |
| 1.2304 | 14550 | 0.0001 | - |
| 1.2347 | 14600 | 0.0001 | - |
| 1.2389 | 14650 | 0.0002 | - |
| 1.2431 | 14700 | 0.0001 | - |
| 1.2474 | 14750 | 0.0002 | - |
| 1.2516 | 14800 | 0.0001 | - |
| 1.2558 | 14850 | 0.0001 | - |
| 1.2600 | 14900 | 0.0001 | - |
| 1.2643 | 14950 | 0.0002 | - |
| 1.2685 | 15000 | 0.0001 | - |
| 1.2727 | 15050 | 0.0061 | - |
| 1.2770 | 15100 | 0.0001 | - |
| 1.2812 | 15150 | 0.0004 | - |
| 1.2854 | 15200 | 0.0002 | - |
| 1.2896 | 15250 | 0.0002 | - |
| 1.2939 | 15300 | 0.0001 | - |
| 1.2981 | 15350 | 0.0001 | - |
| 1.3023 | 15400 | 0.0001 | - |
| 1.3066 | 15450 | 0.0002 | - |
| 1.3108 | 15500 | 0.0001 | - |
| 1.3150 | 15550 | 0.0001 | - |
| 1.3192 | 15600 | 0.002 | - |
| 1.3235 | 15650 | 0.0004 | - |
| 1.3277 | 15700 | 0.0001 | - |
| 1.3319 | 15750 | 0.0001 | - |
| 1.3362 | 15800 | 0.0002 | - |
| 1.3404 | 15850 | 0.0001 | - |
| 1.3446 | 15900 | 0.0001 | - |
| 1.3488 | 15950 | 0.0001 | - |
| 1.3531 | 16000 | 0.0002 | - |
| 1.3573 | 16050 | 0.0001 | - |
| 1.3615 | 16100 | 0.0003 | - |
| 1.3658 | 16150 | 0.0001 | - |
| 1.3700 | 16200 | 0.0001 | - |
| 1.3742 | 16250 | 0.0001 | - |
| 1.3784 | 16300 | 0.0001 | - |
| 1.3827 | 16350 | 0.0001 | - |
| 1.3869 | 16400 | 0.0001 | - |
| 1.3911 | 16450 | 0.0004 | - |
| 1.3953 | 16500 | 0.0002 | - |
| 1.3996 | 16550 | 0.0001 | - |
| 1.4038 | 16600 | 0.0001 | - |
| 1.4080 | 16650 | 0.0001 | - |
| 1.4123 | 16700 | 0.0001 | - |
| 1.4165 | 16750 | 0.0001 | - |
| 1.4207 | 16800 | 0.0001 | - |
| 1.4249 | 16850 | 0.0001 | - |
| 1.4292 | 16900 | 0.0001 | - |
| 1.4334 | 16950 | 0.0024 | - |
| 1.4376 | 17000 | 0.0001 | - |
| 1.4419 | 17050 | 0.0002 | - |
| 1.4461 | 17100 | 0.0001 | - |
| 1.4503 | 17150 | 0.0001 | - |
| 1.4545 | 17200 | 0.0001 | - |
| 1.4588 | 17250 | 0.0001 | - |
| 1.4630 | 17300 | 0.0606 | - |
| 1.4672 | 17350 | 0.0004 | - |
| 1.4715 | 17400 | 0.0001 | - |
| 1.4757 | 17450 | 0.0007 | - |
| 1.4799 | 17500 | 0.0001 | - |
| 1.4841 | 17550 | 0.0001 | - |
| 1.4884 | 17600 | 0.0001 | - |
| 1.4926 | 17650 | 0.0002 | - |
| 1.4968 | 17700 | 0.0015 | - |
| 1.5011 | 17750 | 0.0001 | - |
| 1.5053 | 17800 | 0.0001 | - |
| 1.5095 | 17850 | 0.0002 | - |
| 1.5137 | 17900 | 0.0002 | - |
| 1.5180 | 17950 | 0.0001 | - |
| 1.5222 | 18000 | 0.0001 | - |
| 1.5264 | 18050 | 0.0001 | - |
| 1.5307 | 18100 | 0.0001 | - |
| 1.5349 | 18150 | 0.0002 | - |
| 1.5391 | 18200 | 0.0001 | - |
| 1.5433 | 18250 | 0.0001 | - |
| 1.5476 | 18300 | 0.0001 | - |
| 1.5518 | 18350 | 0.0001 | - |
| 1.5560 | 18400 | 0.0002 | - |
| 1.5603 | 18450 | 0.0001 | - |
| 1.5645 | 18500 | 0.0001 | - |
| 1.5687 | 18550 | 0.0001 | - |
| 1.5729 | 18600 | 0.0001 | - |
| 1.5772 | 18650 | 0.0001 | - |
| 1.5814 | 18700 | 0.0002 | - |
| 1.5856 | 18750 | 0.0001 | - |
| 1.5899 | 18800 | 0.0001 | - |
| 1.5941 | 18850 | 0.0001 | - |
| 1.5983 | 18900 | 0.0009 | - |
| 1.6025 | 18950 | 0.0001 | - |
| 1.6068 | 19000 | 0.0002 | - |
| 1.6110 | 19050 | 0.0013 | - |
| 1.6152 | 19100 | 0.0001 | - |
| 1.6195 | 19150 | 0.0005 | - |
| 1.6237 | 19200 | 0.0001 | - |
| 1.6279 | 19250 | 0.0016 | - |
| 1.6321 | 19300 | 0.0001 | - |
| 1.6364 | 19350 | 0.0001 | - |
| 1.6406 | 19400 | 0.0015 | - |
| 1.6448 | 19450 | 0.0001 | - |
| 1.6490 | 19500 | 0.0001 | - |
| 1.6533 | 19550 | 0.0001 | - |
| 1.6575 | 19600 | 0.0001 | - |
| 1.6617 | 19650 | 0.0001 | - |
| 1.6660 | 19700 | 0.0001 | - |
| 1.6702 | 19750 | 0.0001 | - |
| 1.6744 | 19800 | 0.0001 | - |
| 1.6786 | 19850 | 0.0001 | - |
| 1.6829 | 19900 | 0.0001 | - |
| 1.6871 | 19950 | 0.0001 | - |
| 1.6913 | 20000 | 0.0001 | - |
| 1.6956 | 20050 | 0.0001 | - |
| 1.6998 | 20100 | 0.0001 | - |
| 1.7040 | 20150 | 0.0001 | - |
| 1.7082 | 20200 | 0.0001 | - |
| 1.7125 | 20250 | 0.0001 | - |
| 1.7167 | 20300 | 0.0001 | - |
| 1.7209 | 20350 | 0.0001 | - |
| 1.7252 | 20400 | 0.0001 | - |
| 1.7294 | 20450 | 0.0001 | - |
| 1.7336 | 20500 | 0.002 | - |
| 1.7378 | 20550 | 0.0001 | - |
| 1.7421 | 20600 | 0.0001 | - |
| 1.7463 | 20650 | 0.0001 | - |
| 1.7505 | 20700 | 0.0001 | - |
| 1.7548 | 20750 | 0.0001 | - |
| 1.7590 | 20800 | 0.0001 | - |
| 1.7632 | 20850 | 0.0001 | - |
| 1.7674 | 20900 | 0.0001 | - |
| 1.7717 | 20950 | 0.0002 | - |
| 1.7759 | 21000 | 0.0001 | - |
| 1.7801 | 21050 | 0.0004 | - |
| 1.7844 | 21100 | 0.0002 | - |
| 1.7886 | 21150 | 0.0599 | - |
| 1.7928 | 21200 | 0.0001 | - |
| 1.7970 | 21250 | 0.0001 | - |
| 1.8013 | 21300 | 0.0001 | - |
| 1.8055 | 21350 | 0.0001 | - |
| 1.8097 | 21400 | 0.0001 | - |
| 1.8140 | 21450 | 0.0001 | - |
| 1.8182 | 21500 | 0.0001 | - |
| 1.8224 | 21550 | 0.0001 | - |
| 1.8266 | 21600 | 0.0001 | - |
| 1.8309 | 21650 | 0.0013 | - |
| 1.8351 | 21700 | 0.0002 | - |
| 1.8393 | 21750 | 0.0001 | - |
| 1.8436 | 21800 | 0.0001 | - |
| 1.8478 | 21850 | 0.0001 | - |
| 1.8520 | 21900 | 0.0001 | - |
| 1.8562 | 21950 | 0.0001 | - |
| 1.8605 | 22000 | 0.0001 | - |
| 1.8647 | 22050 | 0.0001 | - |
| 1.8689 | 22100 | 0.0001 | - |
| 1.8732 | 22150 | 0.0 | - |
| 1.8774 | 22200 | 0.0001 | - |
| 1.8816 | 22250 | 0.0001 | - |
| 1.8858 | 22300 | 0.0001 | - |
| 1.8901 | 22350 | 0.0001 | - |
| 1.8943 | 22400 | 0.0001 | - |
| 1.8985 | 22450 | 0.0001 | - |
| 1.9027 | 22500 | 0.0001 | - |
| 1.9070 | 22550 | 0.0001 | - |
| 1.9112 | 22600 | 0.0001 | - |
| 1.9154 | 22650 | 0.0001 | - |
| 1.9197 | 22700 | 0.0001 | - |
| 1.9239 | 22750 | 0.0001 | - |
| 1.9281 | 22800 | 0.0001 | - |
| 1.9323 | 22850 | 0.0001 | - |
| 1.9366 | 22900 | 0.0001 | - |
| 1.9408 | 22950 | 0.0 | - |
| 1.9450 | 23000 | 0.0016 | - |
| 1.9493 | 23050 | 0.0001 | - |
| 1.9535 | 23100 | 0.0002 | - |
| 1.9577 | 23150 | 0.0001 | - |
| 1.9619 | 23200 | 0.0001 | - |
| 1.9662 | 23250 | 0.0001 | - |
| 1.9704 | 23300 | 0.0001 | - |
| 1.9746 | 23350 | 0.0001 | - |
| 1.9789 | 23400 | 0.0001 | - |
| 1.9831 | 23450 | 0.0001 | - |
| 1.9873 | 23500 | 0.0016 | - |
| 1.9915 | 23550 | 0.0001 | - |
| 1.9958 | 23600 | 0.0001 | - |
| 2.0 | 23650 | 0.0001 | 0.0008 |
| 2.0042 | 23700 | 0.0001 | - |
| 2.0085 | 23750 | 0.0017 | - |
| 2.0127 | 23800 | 0.0001 | - |
| 2.0169 | 23850 | 0.0 | - |
| 2.0211 | 23900 | 0.0001 | - |
| 2.0254 | 23950 | 0.0001 | - |
| 2.0296 | 24000 | 0.0001 | - |
| 2.0338 | 24050 | 0.0001 | - |
| 2.0381 | 24100 | 0.0001 | - |
| 2.0423 | 24150 | 0.0001 | - |
| 2.0465 | 24200 | 0.0001 | - |
| 2.0507 | 24250 | 0.0001 | - |
| 2.0550 | 24300 | 0.0001 | - |
| 2.0592 | 24350 | 0.0001 | - |
| 2.0634 | 24400 | 0.0001 | - |
| 2.0677 | 24450 | 0.0 | - |
| 2.0719 | 24500 | 0.0001 | - |
| 2.0761 | 24550 | 0.0001 | - |
| 2.0803 | 24600 | 0.0001 | - |
| 2.0846 | 24650 | 0.0001 | - |
| 2.0888 | 24700 | 0.0002 | - |
| 2.0930 | 24750 | 0.0002 | - |
| 2.0973 | 24800 | 0.0001 | - |
| 2.1015 | 24850 | 0.0006 | - |
| 2.1057 | 24900 | 0.0579 | - |
| 2.1099 | 24950 | 0.0001 | - |
| 2.1142 | 25000 | 0.0004 | - |
| 2.1184 | 25050 | 0.0011 | - |
| 2.1226 | 25100 | 0.0001 | - |
| 2.1268 | 25150 | 0.0002 | - |
| 2.1311 | 25200 | 0.0003 | - |
| 2.1353 | 25250 | 0.0001 | - |
| 2.1395 | 25300 | 0.0014 | - |
| 2.1438 | 25350 | 0.0001 | - |
| 2.1480 | 25400 | 0.0002 | - |
| 2.1522 | 25450 | 0.0012 | - |
| 2.1564 | 25500 | 0.0001 | - |
| 2.1607 | 25550 | 0.0001 | - |
| 2.1649 | 25600 | 0.0002 | - |
| 2.1691 | 25650 | 0.0001 | - |
| 2.1734 | 25700 | 0.0001 | - |
| 2.1776 | 25750 | 0.0001 | - |
| 2.1818 | 25800 | 0.0001 | - |
| 2.1860 | 25850 | 0.0544 | - |
| 2.1903 | 25900 | 0.0001 | - |
| 2.1945 | 25950 | 0.0001 | - |
| 2.1987 | 26000 | 0.0001 | - |
| 2.2030 | 26050 | 0.0001 | - |
| 2.2072 | 26100 | 0.0001 | - |
| 2.2114 | 26150 | 0.0001 | - |
| 2.2156 | 26200 | 0.0002 | - |
| 2.2199 | 26250 | 0.0 | - |
| 2.2241 | 26300 | 0.0001 | - |
| 2.2283 | 26350 | 0.0002 | - |
| 2.2326 | 26400 | 0.0001 | - |
| 2.2368 | 26450 | 0.0001 | - |
| 2.2410 | 26500 | 0.0602 | - |
| 2.2452 | 26550 | 0.0022 | - |
| 2.2495 | 26600 | 0.0001 | - |
| 2.2537 | 26650 | 0.0003 | - |
| 2.2579 | 26700 | 0.0002 | - |
| 2.2622 | 26750 | 0.0001 | - |
| 2.2664 | 26800 | 0.0001 | - |
| 2.2706 | 26850 | 0.0001 | - |
| 2.2748 | 26900 | 0.0001 | - |
| 2.2791 | 26950 | 0.0001 | - |
| 2.2833 | 27000 | 0.0001 | - |
| 2.2875 | 27050 | 0.0001 | - |
| 2.2918 | 27100 | 0.0001 | - |
| 2.2960 | 27150 | 0.0001 | - |
| 2.3002 | 27200 | 0.0001 | - |
| 2.3044 | 27250 | 0.0001 | - |
| 2.3087 | 27300 | 0.0001 | - |
| 2.3129 | 27350 | 0.0003 | - |
| 2.3171 | 27400 | 0.0001 | - |
| 2.3214 | 27450 | 0.0001 | - |
| 2.3256 | 27500 | 0.0001 | - |
| 2.3298 | 27550 | 0.0001 | - |
| 2.3340 | 27600 | 0.0001 | - |
| 2.3383 | 27650 | 0.0001 | - |
| 2.3425 | 27700 | 0.0015 | - |
| 2.3467 | 27750 | 0.001 | - |
| 2.3510 | 27800 | 0.0002 | - |
| 2.3552 | 27850 | 0.0001 | - |
| 2.3594 | 27900 | 0.0001 | - |
| 2.3636 | 27950 | 0.0001 | - |
| 2.3679 | 28000 | 0.0002 | - |
| 2.3721 | 28050 | 0.0001 | - |
| 2.3763 | 28100 | 0.0001 | - |
| 2.3805 | 28150 | 0.001 | - |
| 2.3848 | 28200 | 0.0001 | - |
| 2.3890 | 28250 | 0.0001 | - |
| 2.3932 | 28300 | 0.0001 | - |
| 2.3975 | 28350 | 0.0001 | - |
| 2.4017 | 28400 | 0.0002 | - |
| 2.4059 | 28450 | 0.0001 | - |
| 2.4101 | 28500 | 0.0001 | - |
| 2.4144 | 28550 | 0.0001 | - |
| 2.4186 | 28600 | 0.0001 | - |
| 2.4228 | 28650 | 0.0001 | - |
| 2.4271 | 28700 | 0.0001 | - |
| 2.4313 | 28750 | 0.0001 | - |
| 2.4355 | 28800 | 0.0001 | - |
| 2.4397 | 28850 | 0.0001 | - |
| 2.4440 | 28900 | 0.0001 | - |
| 2.4482 | 28950 | 0.0001 | - |
| 2.4524 | 29000 | 0.0001 | - |
| 2.4567 | 29050 | 0.0021 | - |
| 2.4609 | 29100 | 0.0001 | - |
| 2.4651 | 29150 | 0.0001 | - |
| 2.4693 | 29200 | 0.0001 | - |
| 2.4736 | 29250 | 0.0 | - |
| 2.4778 | 29300 | 0.0002 | - |
| 2.4820 | 29350 | 0.0002 | - |
| 2.4863 | 29400 | 0.0001 | - |
| 2.4905 | 29450 | 0.0001 | - |
| 2.4947 | 29500 | 0.0002 | - |
| 2.4989 | 29550 | 0.0013 | - |
| 2.5032 | 29600 | 0.0001 | - |
| 2.5074 | 29650 | 0.0001 | - |
| 2.5116 | 29700 | 0.0001 | - |
| 2.5159 | 29750 | 0.0001 | - |
| 2.5201 | 29800 | 0.0015 | - |
| 2.5243 | 29850 | 0.0001 | - |
| 2.5285 | 29900 | 0.0001 | - |
| 2.5328 | 29950 | 0.0001 | - |
| 2.5370 | 30000 | 0.0002 | - |
| 2.5412 | 30050 | 0.0001 | - |
| 2.5455 | 30100 | 0.0001 | - |
| 2.5497 | 30150 | 0.0001 | - |
| 2.5539 | 30200 | 0.0001 | - |
| 2.5581 | 30250 | 0.0001 | - |
| 2.5624 | 30300 | 0.0002 | - |
| 2.5666 | 30350 | 0.0001 | - |
| 2.5708 | 30400 | 0.0001 | - |
| 2.5751 | 30450 | 0.0001 | - |
| 2.5793 | 30500 | 0.0001 | - |
| 2.5835 | 30550 | 0.0001 | - |
| 2.5877 | 30600 | 0.0001 | - |
| 2.5920 | 30650 | 0.0001 | - |
| 2.5962 | 30700 | 0.0 | - |
| 2.6004 | 30750 | 0.0001 | - |
| 2.6047 | 30800 | 0.0001 | - |
| 2.6089 | 30850 | 0.0001 | - |
| 2.6131 | 30900 | 0.0001 | - |
| 2.6173 | 30950 | 0.0001 | - |
| 2.6216 | 31000 | 0.0001 | - |
| 2.6258 | 31050 | 0.0001 | - |
| 2.6300 | 31100 | 0.0001 | - |
| 2.6342 | 31150 | 0.0001 | - |
| 2.6385 | 31200 | 0.0001 | - |
| 2.6427 | 31250 | 0.0001 | - |
| 2.6469 | 31300 | 0.0001 | - |
| 2.6512 | 31350 | 0.0024 | - |
| 2.6554 | 31400 | 0.0001 | - |
| 2.6596 | 31450 | 0.0001 | - |
| 2.6638 | 31500 | 0.0025 | - |
| 2.6681 | 31550 | 0.0001 | - |
| 2.6723 | 31600 | 0.0001 | - |
| 2.6765 | 31650 | 0.0002 | - |
| 2.6808 | 31700 | 0.0001 | - |
| 2.6850 | 31750 | 0.0 | - |
| 2.6892 | 31800 | 0.0001 | - |
| 2.6934 | 31850 | 0.0001 | - |
| 2.6977 | 31900 | 0.0001 | - |
| 2.7019 | 31950 | 0.0001 | - |
| 2.7061 | 32000 | 0.0001 | - |
| 2.7104 | 32050 | 0.0001 | - |
| 2.7146 | 32100 | 0.0001 | - |
| 2.7188 | 32150 | 0.0001 | - |
| 2.7230 | 32200 | 0.0001 | - |
| 2.7273 | 32250 | 0.0001 | - |
| 2.7315 | 32300 | 0.0 | - |
| 2.7357 | 32350 | 0.0001 | - |
| 2.7400 | 32400 | 0.0001 | - |
| 2.7442 | 32450 | 0.0001 | - |
| 2.7484 | 32500 | 0.0001 | - |
| 2.7526 | 32550 | 0.0001 | - |
| 2.7569 | 32600 | 0.0016 | - |
| 2.7611 | 32650 | 0.0001 | - |
| 2.7653 | 32700 | 0.0001 | - |
| 2.7696 | 32750 | 0.0001 | - |
| 2.7738 | 32800 | 0.0001 | - |
| 2.7780 | 32850 | 0.0001 | - |
| 2.7822 | 32900 | 0.0001 | - |
| 2.7865 | 32950 | 0.0001 | - |
| 2.7907 | 33000 | 0.0001 | - |
| 2.7949 | 33050 | 0.0001 | - |
| 2.7992 | 33100 | 0.0001 | - |
| 2.8034 | 33150 | 0.0001 | - |
| 2.8076 | 33200 | 0.0001 | - |
| 2.8118 | 33250 | 0.0001 | - |
| 2.8161 | 33300 | 0.0001 | - |
| 2.8203 | 33350 | 0.0001 | - |
| 2.8245 | 33400 | 0.0001 | - |
| 2.8288 | 33450 | 0.0001 | - |
| 2.8330 | 33500 | 0.0 | - |
| 2.8372 | 33550 | 0.0 | - |
| 2.8414 | 33600 | 0.0001 | - |
| 2.8457 | 33650 | 0.0001 | - |
| 2.8499 | 33700 | 0.0001 | - |
| 2.8541 | 33750 | 0.0016 | - |
| 2.8584 | 33800 | 0.0001 | - |
| 2.8626 | 33850 | 0.0001 | - |
| 2.8668 | 33900 | 0.0001 | - |
| 2.8710 | 33950 | 0.0001 | - |
| 2.8753 | 34000 | 0.0001 | - |
| 2.8795 | 34050 | 0.0001 | - |
| 2.8837 | 34100 | 0.0001 | - |
| 2.8879 | 34150 | 0.0001 | - |
| 2.8922 | 34200 | 0.0 | - |
| 2.8964 | 34250 | 0.0001 | - |
| 2.9006 | 34300 | 0.0001 | - |
| 2.9049 | 34350 | 0.0001 | - |
| 2.9091 | 34400 | 0.0001 | - |
| 2.9133 | 34450 | 0.0001 | - |
| 2.9175 | 34500 | 0.0001 | - |
| 2.9218 | 34550 | 0.0 | - |
| 2.9260 | 34600 | 0.0001 | - |
| 2.9302 | 34650 | 0.0001 | - |
| 2.9345 | 34700 | 0.0001 | - |
| 2.9387 | 34750 | 0.0155 | - |
| 2.9429 | 34800 | 0.0001 | - |
| 2.9471 | 34850 | 0.0 | - |
| 2.9514 | 34900 | 0.0001 | - |
| 2.9556 | 34950 | 0.0001 | - |
| 2.9598 | 35000 | 0.0001 | - |
| 2.9641 | 35050 | 0.0 | - |
| 2.9683 | 35100 | 0.0018 | - |
| 2.9725 | 35150 | 0.0001 | - |
| 2.9767 | 35200 | 0.0001 | - |
| 2.9810 | 35250 | 0.0001 | - |
| 2.9852 | 35300 | 0.0001 | - |
| 2.9894 | 35350 | 0.0001 | - |
| 2.9937 | 35400 | 0.0001 | - |
| 2.9979 | 35450 | 0.0001 | - |
| 3.0 | 35475 | - | 0.0003 |
| 3.0021 | 35500 | 0.0001 | - |
| 3.0063 | 35550 | 0.0001 | - |
| 3.0106 | 35600 | 0.0022 | - |
| 3.0148 | 35650 | 0.0001 | - |
| 3.0190 | 35700 | 0.0001 | - |
| 3.0233 | 35750 | 0.0001 | - |
| 3.0275 | 35800 | 0.0 | - |
| 3.0317 | 35850 | 0.0019 | - |
| 3.0359 | 35900 | 0.0 | - |
| 3.0402 | 35950 | 0.0001 | - |
| 3.0444 | 36000 | 0.0001 | - |
| 3.0486 | 36050 | 0.0001 | - |
| 3.0529 | 36100 | 0.0 | - |
| 3.0571 | 36150 | 0.0 | - |
| 3.0613 | 36200 | 0.0001 | - |
| 3.0655 | 36250 | 0.0001 | - |
| 3.0698 | 36300 | 0.0001 | - |
| 3.0740 | 36350 | 0.0001 | - |
| 3.0782 | 36400 | 0.0001 | - |
| 3.0825 | 36450 | 0.0 | - |
| 3.0867 | 36500 | 0.0001 | - |
| 3.0909 | 36550 | 0.0001 | - |
| 3.0951 | 36600 | 0.0001 | - |
| 3.0994 | 36650 | 0.0001 | - |
| 3.1036 | 36700 | 0.0001 | - |
| 3.1078 | 36750 | 0.0 | - |
| 3.1121 | 36800 | 0.0001 | - |
| 3.1163 | 36850 | 0.0001 | - |
| 3.1205 | 36900 | 0.0 | - |
| 3.1247 | 36950 | 0.0001 | - |
| 3.1290 | 37000 | 0.0001 | - |
| 3.1332 | 37050 | 0.0001 | - |
| 3.1374 | 37100 | 0.0001 | - |
| 3.1416 | 37150 | 0.0001 | - |
| 3.1459 | 37200 | 0.0001 | - |
| 3.1501 | 37250 | 0.0001 | - |
| 3.1543 | 37300 | 0.0001 | - |
| 3.1586 | 37350 | 0.0001 | - |
| 3.1628 | 37400 | 0.0055 | - |
| 3.1670 | 37450 | 0.0 | - |
| 3.1712 | 37500 | 0.0001 | - |
| 3.1755 | 37550 | 0.0019 | - |
| 3.1797 | 37600 | 0.0001 | - |
| 3.1839 | 37650 | 0.0001 | - |
| 3.1882 | 37700 | 0.0 | - |
| 3.1924 | 37750 | 0.0 | - |
| 3.1966 | 37800 | 0.0001 | - |
| 3.2008 | 37850 | 0.0001 | - |
| 3.2051 | 37900 | 0.0 | - |
| 3.2093 | 37950 | 0.0001 | - |
| 3.2135 | 38000 | 0.0001 | - |
| 3.2178 | 38050 | 0.0001 | - |
| 3.2220 | 38100 | 0.0 | - |
| 3.2262 | 38150 | 0.0001 | - |
| 3.2304 | 38200 | 0.0 | - |
| 3.2347 | 38250 | 0.0001 | - |
| 3.2389 | 38300 | 0.0001 | - |
| 3.2431 | 38350 | 0.0 | - |
| 3.2474 | 38400 | 0.0001 | - |
| 3.2516 | 38450 | 0.0001 | - |
| 3.2558 | 38500 | 0.0 | - |
| 3.2600 | 38550 | 0.0 | - |
| 3.2643 | 38600 | 0.0 | - |
| 3.2685 | 38650 | 0.0017 | - |
| 3.2727 | 38700 | 0.0095 | - |
| 3.2770 | 38750 | 0.0001 | - |
| 3.2812 | 38800 | 0.0001 | - |
| 3.2854 | 38850 | 0.0 | - |
| 3.2896 | 38900 | 0.0001 | - |
| 3.2939 | 38950 | 0.0 | - |
| 3.2981 | 39000 | 0.0001 | - |
| 3.3023 | 39050 | 0.0 | - |
| 3.3066 | 39100 | 0.0001 | - |
| 3.3108 | 39150 | 0.0 | - |
| 3.3150 | 39200 | 0.0 | - |
| 3.3192 | 39250 | 0.0001 | - |
| 3.3235 | 39300 | 0.0001 | - |
| 3.3277 | 39350 | 0.0 | - |
| 3.3319 | 39400 | 0.0001 | - |
| 3.3362 | 39450 | 0.0001 | - |
| 3.3404 | 39500 | 0.0001 | - |
| 3.3446 | 39550 | 0.0 | - |
| 3.3488 | 39600 | 0.0001 | - |
| 3.3531 | 39650 | 0.0 | - |
| 3.3573 | 39700 | 0.0001 | - |
| 3.3615 | 39750 | 0.0001 | - |
| 3.3658 | 39800 | 0.0022 | - |
| 3.3700 | 39850 | 0.0001 | - |
| 3.3742 | 39900 | 0.0001 | - |
| 3.3784 | 39950 | 0.0 | - |
| 3.3827 | 40000 | 0.0 | - |
| 3.3869 | 40050 | 0.0 | - |
| 3.3911 | 40100 | 0.0001 | - |
| 3.3953 | 40150 | 0.0 | - |
| 3.3996 | 40200 | 0.0 | - |
| 3.4038 | 40250 | 0.0 | - |
| 3.4080 | 40300 | 0.0001 | - |
| 3.4123 | 40350 | 0.0 | - |
| 3.4165 | 40400 | 0.0001 | - |
| 3.4207 | 40450 | 0.0 | - |
| 3.4249 | 40500 | 0.0001 | - |
| 3.4292 | 40550 | 0.0001 | - |
| 3.4334 | 40600 | 0.0001 | - |
| 3.4376 | 40650 | 0.0 | - |
| 3.4419 | 40700 | 0.0001 | - |
| 3.4461 | 40750 | 0.0 | - |
| 3.4503 | 40800 | 0.0 | - |
| 3.4545 | 40850 | 0.0 | - |
| 3.4588 | 40900 | 0.0 | - |
| 3.4630 | 40950 | 0.0001 | - |
| 3.4672 | 41000 | 0.0 | - |
| 3.4715 | 41050 | 0.0 | - |
| 3.4757 | 41100 | 0.0001 | - |
| 3.4799 | 41150 | 0.0016 | - |
| 3.4841 | 41200 | 0.0 | - |
| 3.4884 | 41250 | 0.0001 | - |
| 3.4926 | 41300 | 0.0 | - |
| 3.4968 | 41350 | 0.0001 | - |
| 3.5011 | 41400 | 0.0 | - |
| 3.5053 | 41450 | 0.0 | - |
| 3.5095 | 41500 | 0.0001 | - |
| 3.5137 | 41550 | 0.0 | - |
| 3.5180 | 41600 | 0.0 | - |
| 3.5222 | 41650 | 0.0019 | - |
| 3.5264 | 41700 | 0.0001 | - |
| 3.5307 | 41750 | 0.0001 | - |
| 3.5349 | 41800 | 0.0001 | - |
| 3.5391 | 41850 | 0.0001 | - |
| 3.5433 | 41900 | 0.0023 | - |
| 3.5476 | 41950 | 0.0001 | - |
| 3.5518 | 42000 | 0.0 | - |
| 3.5560 | 42050 | 0.0001 | - |
| 3.5603 | 42100 | 0.0001 | - |
| 3.5645 | 42150 | 0.0 | - |
| 3.5687 | 42200 | 0.0 | - |
| 3.5729 | 42250 | 0.0 | - |
| 3.5772 | 42300 | 0.0 | - |
| 3.5814 | 42350 | 0.0001 | - |
| 3.5856 | 42400 | 0.0 | - |
| 3.5899 | 42450 | 0.0 | - |
| 3.5941 | 42500 | 0.0 | - |
| 3.5983 | 42550 | 0.0 | - |
| 3.6025 | 42600 | 0.0001 | - |
| 3.6068 | 42650 | 0.0 | - |
| 3.6110 | 42700 | 0.0001 | - |
| 3.6152 | 42750 | 0.0001 | - |
| 3.6195 | 42800 | 0.0001 | - |
| 3.6237 | 42850 | 0.0001 | - |
| 3.6279 | 42900 | 0.0001 | - |
| 3.6321 | 42950 | 0.0 | - |
| 3.6364 | 43000 | 0.0 | - |
| 3.6406 | 43050 | 0.0 | - |
| 3.6448 | 43100 | 0.0001 | - |
| 3.6490 | 43150 | 0.0 | - |
| 3.6533 | 43200 | 0.0001 | - |
| 3.6575 | 43250 | 0.0001 | - |
| 3.6617 | 43300 | 0.0001 | - |
| 3.6660 | 43350 | 0.0001 | - |
| 3.6702 | 43400 | 0.0 | - |
| 3.6744 | 43450 | 0.0024 | - |
| 3.6786 | 43500 | 0.0 | - |
| 3.6829 | 43550 | 0.0001 | - |
| 3.6871 | 43600 | 0.002 | - |
| 3.6913 | 43650 | 0.0 | - |
| 3.6956 | 43700 | 0.0 | - |
| 3.6998 | 43750 | 0.0001 | - |
| 3.7040 | 43800 | 0.0001 | - |
| 3.7082 | 43850 | 0.0 | - |
| 3.7125 | 43900 | 0.0 | - |
| 3.7167 | 43950 | 0.0001 | - |
| 3.7209 | 44000 | 0.0 | - |
| 3.7252 | 44050 | 0.0001 | - |
| 3.7294 | 44100 | 0.0 | - |
| 3.7336 | 44150 | 0.0 | - |
| 3.7378 | 44200 | 0.0001 | - |
| 3.7421 | 44250 | 0.0 | - |
| 3.7463 | 44300 | 0.0 | - |
| 3.7505 | 44350 | 0.0001 | - |
| 3.7548 | 44400 | 0.0 | - |
| 3.7590 | 44450 | 0.0 | - |
| 3.7632 | 44500 | 0.0001 | - |
| 3.7674 | 44550 | 0.0 | - |
| 3.7717 | 44600 | 0.0 | - |
| 3.7759 | 44650 | 0.0 | - |
| 3.7801 | 44700 | 0.0022 | - |
| 3.7844 | 44750 | 0.0 | - |
| 3.7886 | 44800 | 0.0001 | - |
| 3.7928 | 44850 | 0.0 | - |
| 3.7970 | 44900 | 0.0001 | - |
| 3.8013 | 44950 | 0.0001 | - |
| 3.8055 | 45000 | 0.0 | - |
| 3.8097 | 45050 | 0.0 | - |
| 3.8140 | 45100 | 0.0 | - |
| 3.8182 | 45150 | 0.0 | - |
| 3.8224 | 45200 | 0.0 | - |
| 3.8266 | 45250 | 0.0 | - |
| 3.8309 | 45300 | 0.0001 | - |
| 3.8351 | 45350 | 0.0 | - |
| 3.8393 | 45400 | 0.0001 | - |
| 3.8436 | 45450 | 0.0001 | - |
| 3.8478 | 45500 | 0.0 | - |
| 3.8520 | 45550 | 0.0001 | - |
| 3.8562 | 45600 | 0.0001 | - |
| 3.8605 | 45650 | 0.0 | - |
| 3.8647 | 45700 | 0.0 | - |
| 3.8689 | 45750 | 0.0 | - |
| 3.8732 | 45800 | 0.0001 | - |
| 3.8774 | 45850 | 0.0015 | - |
| 3.8816 | 45900 | 0.0001 | - |
| 3.8858 | 45950 | 0.0 | - |
| 3.8901 | 46000 | 0.0 | - |
| 3.8943 | 46050 | 0.0001 | - |
| 3.8985 | 46100 | 0.0 | - |
| 3.9027 | 46150 | 0.0 | - |
| 3.9070 | 46200 | 0.0 | - |
| 3.9112 | 46250 | 0.0 | - |
| 3.9154 | 46300 | 0.0 | - |
| 3.9197 | 46350 | 0.0 | - |
| 3.9239 | 46400 | 0.0 | - |
| 3.9281 | 46450 | 0.0 | - |
| 3.9323 | 46500 | 0.0 | - |
| 3.9366 | 46550 | 0.0001 | - |
| 3.9408 | 46600 | 0.0001 | - |
| 3.9450 | 46650 | 0.0001 | - |
| 3.9493 | 46700 | 0.0001 | - |
| 3.9535 | 46750 | 0.0 | - |
| 3.9577 | 46800 | 0.0 | - |
| 3.9619 | 46850 | 0.0 | - |
| 3.9662 | 46900 | 0.0 | - |
| 3.9704 | 46950 | 0.0 | - |
| 3.9746 | 47000 | 0.0 | - |
| 3.9789 | 47050 | 0.0 | - |
| 3.9831 | 47100 | 0.0001 | - |
| 3.9873 | 47150 | 0.0001 | - |
| 3.9915 | 47200 | 0.0021 | - |
| 3.9958 | 47250 | 0.0 | - |
| **4.0** | **47300** | **0.0** | **0.0002** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.14
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.4.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```