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metadata
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 model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression 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 with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

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:

pip install setfit

Then you can load this model and run inference.

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 -
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  • 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

@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}
}