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 ---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: What is an SRE? Use only Korean in your response and provide a title wrapped
in double angular brackets, such as <<SRE>>. Use the keywords 'indicator', 'objective'
and 'management'.
- text: 'Who is known as The Invincibles in English football?
In football, "The Invincibles" is a nickname used to refer to the Preston North
End team of the 1888–89 season, managed by William Sudell, and the Arsenal team
of the 2003–04 season managed by Arsène Wenger. Preston North End earned the
nickname after completing an entire season undefeated in league and cup competition
(27 games), while Arsenal were undefeated in the league (38 games) in a run that
stretched to a record 49 games. The actual nickname of the Preston team was the
"Old Invincibles" but both versions have been in use.'
- text: 'Security guard is in the vicinity of at mall. Mountain peak is in the vicinity
of at apartment. Napkin holder is in the vicinity of at mall. Store is in the
vicinity of at mall. Legal pad is not in the vicinity of desk. Napkin holder is
not in the vicinity of at apartment. Room is in the vicinity of store. Legal pad
is not in the vicinity of tsunami. Legal pad has property yellow. Desk is in the
vicinity of room. Food is in the vicinity of store. Legal pad is not in the vicinity
of haystack.
Store is in the vicinity of at mall. What do you think about that statement?'
- text: 'How much PVC produced each year?
Polyvinyl chloride (alternatively: poly(vinyl chloride), colloquial: polyvinyl,
or simply vinyl; abbreviated: PVC) is the world''s third-most widely produced
synthetic polymer of plastic (after polyethylene and polypropylene). About 40
million tons of PVC are produced each year.'
- text: 'What is Bubble tea?
Bubble tea (also known as pearl milk tea, bubble milk tea, tapioca milk tea, boba
tea, or boba; Chinese: 珍珠奶茶; pinyin: zhēnzhū nǎichá, 波霸奶茶; bōbà nǎichá) is a tea-based
drink that originated in Taiwan in the early 1980s. Taiwanese immigrants brought
it to the United States in the 1990s, initially in California through regions
like Los Angeles County, but the drink has also spread to other countries where
there is a large East Asian diaspora population.
Bubble tea most commonly consists of tea accompanied by chewy tapioca balls ("boba"
or "pearls"), but it can be made with other toppings as well, such as grass jelly,
aloe vera, red bean, or popping boba. It has many varieties and flavors, but the
two most popular varieties are pearl black milk tea and pearl green milk tea ("pearl"
signifies the tapioca balls at the bottom).'
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: NovaSearch/stella_en_400M_v5
---
# SetFit with NovaSearch/stella_en_400M_v5
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [NovaSearch/stella_en_400M_v5](https://huggingface.co/NovaSearch/stella_en_400M_v5) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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:** [NovaSearch/stella_en_400M_v5](https://huggingface.co/NovaSearch/stella_en_400M_v5)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 7 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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 |
|:--------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Extraction | <ul><li>"Please convert the following to a list of unique names of persons mentioned in the article.\n\nHere's the article:\nBut a hotly contested primary is likely to drag the eventual nominee to the right, even on issues that could otherwise favor his party. Mr. DeSantis, widely seen as Mr. Trump’s most serious challenger, signed a ban on abortion in his state after six weeks, a threshold before many women know they are pregnant.\n\nAnd at some point, Republicans’ drive against transgender people and their fixation on social issues may appear to be bullying — or simply far afield from real issues in the lives of swing voters, said Ms. Caprara, the chief of staff for the Illinois governor.\n\n“There’s this toxic soup between abortion, guns, gay rights, library books, African American history,” she said. “It just comes across to people as, ‘Who are these people?’”\n\nThe biggest issue, however, may be the storm cloud on the horizon that may or may not burst — the economy. In 2020, Mr. Biden became one of the few presidential candidates in modern history to have triumphed over the candidate who was more trusted on the economy in polls."</li><li>"What did the writer say they were probably going to upgrade to?\n\nI've owned almost every nexus and pixel device from google. I still have my nexus 6p lying around somewhere.\nI'm actually pretty happy with my pixel 6p. The battery life is good, the performance is excellent, no issues with the fp sensor, and contrary to other opinions, I quite like android 13.\nI've only had the phone overheat once and that was when it was super hot and humid outside and I was using walking directions with the gps on.\nThe modem isn't the best, but i've never been left without a signal and most of the time I'm close to wifi so its not a huge issue for me.\nI'll probably upgrade to the pixel 7 pro if it has some meaningful improvements to the 6p."</li><li>"What is the name of the boys' dog? \n\nMike Brady (Robert Reed), a widowed architect with three sons—Greg (Barry Williams), Peter (Christopher Knight), and Bobby (Mike Lookinland)—marries Carol Martin (Florence Henderson), who herself has three daughters: Marcia (Maureen McCormick), Jan (Eve Plumb), and Cindy (Susan Olsen). Carol and her daughters take the Brady surname. Included in the blended family are Mike's live-in housekeeper, Alice Nelson (Ann B. Davis), and the boys' dog, Tiger. (In the pilot episode, the girls also have a pet: a cat named Fluffy. Fluffy never appears in any other episodes.) The setting is a large two-story house designed by Mike, located in a Los Angeles suburb.[4] The show never addressed what happened to Carol's first husband.[5]"</li></ul> |
| Math | <ul><li>'Given that the four-digit number \\( N \\) is a perfect square, and each digit of \\( N \\) is less than 7. If each digit is increased by 3, the resulting four-digit number is still a perfect square. Find \\( N \\).'</li><li>'Given $f(x) = ax^5 + bx^3 + cx + 8$, and $f(-2) = 10$, then $f(2) = \\boxed{ }$\n\nA: $-2$\n\nB: $-6$\n\nC: $6$\n\nD: $8$'</li><li>'Find the seventh term of the geometric sequence with first term $3$ and second term $\\frac{2}{5}$.'</li></ul> |
| Brainstorming | <ul><li>'Is this quote inspiring? Give me the words and phrases that make you think it is or is not inspiring. \n\nQuote: “The best way to not feel hopeless is to get up and do something. Don’t wait for good things to happen to you. If you go out and make some good things happen, you will fill the world with hope, you will fill yourself with hope.”'</li><li>'How can I manage my time effectively while balancing school and work?'</li><li>'Why did the Hood Canal Bridge sink in Washington in 1979?'</li></ul> |
| Factual QA | <ul><li>'Who was the first host of Top Chef?'</li><li>'In the 2016 World Rugby Nations Cup, what was the final score of the match between Namibia and Emerging Italy?'</li><li>"What is the full name of Vic Clapham's great-grandson who completed the Comrades Marathon from 2012 to 2015?"</li></ul> |
| Generation | <ul><li>'Imagine you are a highly experienced spaceship engineer, and someone is asking your opinion on the most efficient design for a newly proposed interstellar spacecraft. Provide a brief overview of essential features for such a vessel.'</li><li>'Create a dialogue between two people who have completely opposite political views, but must find a way to work together.'</li><li>'Your response should contain at least 5 sentences. Include keywords [love, happiness, joy] in the response. In your response, the word "joy" should appear at least 3 times.\n\nHow can I express my feelings of joy and happiness to someone I love?'</li></ul> |
| Coding | <ul><li>'Your friend Claire has dragged you along to a speedcubing event that is happening in Eindhoven. These events are all about solving the Rubik’s cube and similar twisty puzzles as quickly as possible. The attendants of the event can enter into various competitions based on the type and size of the puzzle, and there are even special competitions where the puzzles need to be solved one-handed or blindfolded. \n\nClaire is competing in the most popular competition: speedsolving the $3\\times 3\\times 3$ Rubik’s cube, pictured on the right. Each contestant needs to solve the cube five times, each time with a different random scramble. After all solves are completed, the best and the worst times are discarded and the final score is the average of the remaining three times. The contestant with the smallest final score wins.\n\nClaire has done well in the competition so far and is among the contenders for the overall victory. All the other contestants have already finished their five solves, but Claire has one solve remaining. By looking at the final scores of the other contestants, she has deduced her own target final score. As long as her final score is less than or equal to this target score, she will be declared the overall winner. Is it possible for her to win the competition, and if so, what is the worst time she can have on her last solve in order to do so?\n\n-----Input-----\nThe input consists of:\n - One line with four real numbers $t_1$, $t_2$, $t_3$ and $t_4$, the times Claire got on her first four solves.\n - One line with a real number $t$, Claire’s target final score, the worst final score she can have in order to be declared the overall winner.\n\nEach number is between $1$ and $20$, inclusive, and is given with exactly two decimal places.\n\n-----Output-----\nIf it is not possible for Claire to win the event, output “impossible”. If she will win regardless of the time she gets on her last solve, output “infinite”. Otherwise, output the worst time she can have on her last solve in order to be declared the overall winner. Output the number to exactly two decimal places.\n\n-----Examples-----\nSample Input 1:\n6.38 7.20 6.95 8.11\n7.53\nSample Output 1:\ninfinite\n\nSample Input 2:\n6.38 7.20 6.95 8.11\n6.99\nSample Output 2:\n6.82'</li><li>"Problem :\n\nBajirao is on a date with his girlfriend Avni. It is a romantic night and they are\nplaying a game of words. \n\nThe rule of this game is that if Bajirao says a word such that no adjacent letters occurring in the word are same then he gets a kiss from her otherwise he gets a slap.\n\nInput :\n\nThe first line consists of T the number of test cases. The next T lines are such that each line consists of a single word spoken by Bajirao.\n\nOutput\n\nFor every test case, on a new line print 'KISS' if Bajirao gets a kiss and 'SLAP' if Bajirao gets a slap.\n\nConstraints :\n\n1 ≤ T ≤ 100\n\n2 ≤ Length of Word spoken by Bajirao ≤ 100\n\nThe input word will comprise only of lower case English alphabets (a-z).\n\nProblem Setter : Shreyans\n\nProblem Tester : Sandeep\n\nProblem Statement : Ravi\n\n(By IIT Kgp HackerEarth Programming Club)\n\nSAMPLE INPUT\n2\nremember\noccurring\n\nSAMPLE OUTPUT\nKISS\nSLAP"</li><li>'Using the Arduino IDE, create an .ino sketch that creates an MQTT broker for the MKR WIFI 1010'</li></ul> |
| Reasoning | <ul><li>'Silver dollar pancakes are a variety that is smaller than traditional pancakes. Silver dollars are a type of American coin.\n\nAre some types of pancakes named after coins?'</li><li>'Most successful companies are related to good management. Enterprise management generally includes two aspects, namely management and management, of which management is more important. To do a good job of management, you need a variety of Managing talent also requires leaders to make the most of their role.\n\nIt follows from this:.\n\n1. A well-managed company will succeed.\n2. With good management talent, good management is guaranteed.\n3. Poorly managed companies will eventually fail in market competition.\n4. Leaders should pay attention to the role of subordinates.'</li><li>'The survey shows that the biggest difficulty in youth entrepreneurship is funding.64.2% of people believe that lack of sufficient funds is the main difficulty. Many people are unwilling to borrow or raise funds despite lack of funds, which reflects that many entrepreneurs are starting a business. There is a conservative mentality in the process. Another prominent difficulty is the excessive competition of peers, accounting for 26.9%. During the survey, it was found that the field of youth entrepreneurship is more concentrated, such as the college student group is more inclined to e-commerce, computer technology support, etc. Young farmers are more willing to engage in the planting and breeding industries that they are more familiar with. This kind of homogeneous entrepreneurship will inevitably lead to excessive competition while forming a scale effect.\n\nThe following statement is consistent with the original?\n\n1. Insufficient funding is a major factor in the failure of youth entrepreneurship.\n2. Inadequate financial services support for young entrepreneurs.\n3. Homogeneous entrepreneurship reflects the conservative mindset of entrepreneurs.\n4. The field of youth entrepreneurship is concentrated in certain fixed industries.'</li></ul> |
## 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("setfit_model_id")
# Run inference
preds = model("What is an SRE? Use only Korean in your response and provide a title wrapped in double angular brackets, such as <<SRE>>. Use the keywords 'indicator', 'objective' and 'management'.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:-----|
| Word count | 3 | 110.8976 | 8430 |
| Label | Training Sample Count |
|:--------------|:----------------------|
| Brainstorming | 250 |
| Coding | 253 |
| Extraction | 250 |
| Factual QA | 255 |
| Generation | 250 |
| Math | 250 |
| Reasoning | 250 |
### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (1, 15)
- max_steps: 500
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.0001
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: True
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- run_name: stella_en_400M_v5
- evaluation_strategy: no
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-----:|:----:|:-------------:|:---------------:|
| 0.002 | 1 | 0.2869 | - |
| 0.004 | 2 | 0.1469 | - |
| 0.006 | 3 | 0.2431 | - |
| 0.008 | 4 | 0.3568 | - |
| 0.01 | 5 | 0.2769 | - |
| 0.012 | 6 | 0.2425 | - |
| 0.014 | 7 | 0.2001 | - |
| 0.016 | 8 | 0.2825 | - |
| 0.018 | 9 | 0.2433 | - |
| 0.02 | 10 | 0.3096 | - |
| 0.022 | 11 | 0.2856 | - |
| 0.024 | 12 | 0.265 | - |
| 0.026 | 13 | 0.2476 | - |
| 0.028 | 14 | 0.1764 | - |
| 0.03 | 15 | 0.1491 | - |
| 0.032 | 16 | 0.3051 | - |
| 0.034 | 17 | 0.2445 | - |
| 0.036 | 18 | 0.249 | - |
| 0.038 | 19 | 0.1981 | - |
| 0.04 | 20 | 0.1892 | - |
| 0.042 | 21 | 0.1933 | - |
| 0.044 | 22 | 0.2331 | - |
| 0.046 | 23 | 0.2145 | - |
| 0.048 | 24 | 0.1708 | - |
| 0.05 | 25 | 0.2272 | - |
| 0.052 | 26 | 0.1714 | - |
| 0.054 | 27 | 0.2138 | - |
| 0.056 | 28 | 0.2178 | - |
| 0.058 | 29 | 0.1346 | - |
| 0.06 | 30 | 0.1939 | - |
| 0.062 | 31 | 0.1632 | - |
| 0.064 | 32 | 0.1934 | - |
| 0.066 | 33 | 0.1897 | - |
| 0.068 | 34 | 0.1558 | - |
| 0.07 | 35 | 0.1568 | - |
| 0.072 | 36 | 0.1116 | - |
| 0.074 | 37 | 0.1609 | - |
| 0.076 | 38 | 0.1294 | - |
| 0.078 | 39 | 0.1511 | - |
| 0.08 | 40 | 0.1654 | - |
| 0.082 | 41 | 0.1542 | - |
| 0.084 | 42 | 0.0887 | - |
| 0.086 | 43 | 0.0811 | - |
| 0.088 | 44 | 0.0991 | - |
| 0.09 | 45 | 0.0845 | - |
| 0.092 | 46 | 0.0875 | - |
| 0.094 | 47 | 0.0338 | - |
| 0.096 | 48 | 0.0945 | - |
| 0.098 | 49 | 0.0477 | - |
| 0.1 | 50 | 0.0696 | - |
| 0.102 | 51 | 0.136 | - |
| 0.104 | 52 | 0.099 | - |
| 0.106 | 53 | 0.0371 | - |
| 0.108 | 54 | 0.0513 | - |
| 0.11 | 55 | 0.0484 | - |
| 0.112 | 56 | 0.0194 | - |
| 0.114 | 57 | 0.0601 | - |
| 0.116 | 58 | 0.1149 | - |
| 0.118 | 59 | 0.0836 | - |
| 0.12 | 60 | 0.0865 | - |
| 0.122 | 61 | 0.0659 | - |
| 0.124 | 62 | 0.0849 | - |
| 0.126 | 63 | 0.0963 | - |
| 0.128 | 64 | 0.07 | - |
| 0.13 | 65 | 0.0233 | - |
| 0.132 | 66 | 0.1248 | - |
| 0.134 | 67 | 0.0561 | - |
| 0.136 | 68 | 0.0851 | - |
| 0.138 | 69 | 0.0638 | - |
| 0.14 | 70 | 0.0498 | - |
| 0.142 | 71 | 0.0311 | - |
| 0.144 | 72 | 0.1374 | - |
| 0.146 | 73 | 0.0502 | - |
| 0.148 | 74 | 0.0605 | - |
| 0.15 | 75 | 0.0137 | - |
| 0.152 | 76 | 0.065 | - |
| 0.154 | 77 | 0.0846 | - |
| 0.156 | 78 | 0.0347 | - |
| 0.158 | 79 | 0.0517 | - |
| 0.16 | 80 | 0.1447 | - |
| 0.162 | 81 | 0.0609 | - |
| 0.164 | 82 | 0.1423 | - |
| 0.166 | 83 | 0.0917 | - |
| 0.168 | 84 | 0.226 | - |
| 0.17 | 85 | 0.0595 | - |
| 0.172 | 86 | 0.0588 | - |
| 0.174 | 87 | 0.0228 | - |
| 0.176 | 88 | 0.0925 | - |
| 0.178 | 89 | 0.0595 | - |
| 0.18 | 90 | 0.044 | - |
| 0.182 | 91 | 0.0244 | - |
| 0.184 | 92 | 0.0939 | - |
| 0.186 | 93 | 0.0794 | - |
| 0.188 | 94 | 0.0501 | - |
| 0.19 | 95 | 0.1363 | - |
| 0.192 | 96 | 0.0502 | - |
| 0.194 | 97 | 0.0498 | - |
| 0.196 | 98 | 0.0562 | - |
| 0.198 | 99 | 0.0657 | - |
| 0.2 | 100 | 0.0397 | - |
| 0.202 | 101 | 0.0305 | - |
| 0.204 | 102 | 0.0559 | - |
| 0.206 | 103 | 0.0871 | - |
| 0.208 | 104 | 0.063 | - |
| 0.21 | 105 | 0.0143 | - |
| 0.212 | 106 | 0.0706 | - |
| 0.214 | 107 | 0.0627 | - |
| 0.216 | 108 | 0.1047 | - |
| 0.218 | 109 | 0.0487 | - |
| 0.22 | 110 | 0.0086 | - |
| 0.222 | 111 | 0.0562 | - |
| 0.224 | 112 | 0.0101 | - |
| 0.226 | 113 | 0.0235 | - |
| 0.228 | 114 | 0.0511 | - |
| 0.23 | 115 | 0.0295 | - |
| 0.232 | 116 | 0.0549 | - |
| 0.234 | 117 | 0.0554 | - |
| 0.236 | 118 | 0.0301 | - |
| 0.238 | 119 | 0.0152 | - |
| 0.24 | 120 | 0.0234 | - |
| 0.242 | 121 | 0.01 | - |
| 0.244 | 122 | 0.0372 | - |
| 0.246 | 123 | 0.0085 | - |
| 0.248 | 124 | 0.0205 | - |
| 0.25 | 125 | 0.0117 | - |
| 0.252 | 126 | 0.0039 | - |
| 0.254 | 127 | 0.0178 | - |
| 0.256 | 128 | 0.0276 | - |
| 0.258 | 129 | 0.0592 | - |
| 0.26 | 130 | 0.0143 | - |
| 0.262 | 131 | 0.0667 | - |
| 0.264 | 132 | 0.0059 | - |
| 0.266 | 133 | 0.0767 | - |
| 0.268 | 134 | 0.0088 | - |
| 0.27 | 135 | 0.0034 | - |
| 0.272 | 136 | 0.0031 | - |
| 0.274 | 137 | 0.0151 | - |
| 0.276 | 138 | 0.0072 | - |
| 0.278 | 139 | 0.0033 | - |
| 0.28 | 140 | 0.0188 | - |
| 0.282 | 141 | 0.0069 | - |
| 0.284 | 142 | 0.1552 | - |
| 0.286 | 143 | 0.0618 | - |
| 0.288 | 144 | 0.0043 | - |
| 0.29 | 145 | 0.0209 | - |
| 0.292 | 146 | 0.0094 | - |
| 0.294 | 147 | 0.0191 | - |
| 0.296 | 148 | 0.0119 | - |
| 0.298 | 149 | 0.0012 | - |
| 0.3 | 150 | 0.0014 | - |
| 0.302 | 151 | 0.0121 | - |
| 0.304 | 152 | 0.0018 | - |
| 0.306 | 153 | 0.0792 | - |
| 0.308 | 154 | 0.0027 | - |
| 0.31 | 155 | 0.0035 | - |
| 0.312 | 156 | 0.0009 | - |
| 0.314 | 157 | 0.0014 | - |
| 0.316 | 158 | 0.0068 | - |
| 0.318 | 159 | 0.0025 | - |
| 0.32 | 160 | 0.003 | - |
| 0.322 | 161 | 0.0116 | - |
| 0.324 | 162 | 0.0009 | - |
| 0.326 | 163 | 0.0404 | - |
| 0.328 | 164 | 0.0022 | - |
| 0.33 | 165 | 0.0011 | - |
| 0.332 | 166 | 0.0122 | - |
| 0.334 | 167 | 0.0006 | - |
| 0.336 | 168 | 0.0138 | - |
| 0.338 | 169 | 0.0101 | - |
| 0.34 | 170 | 0.0019 | - |
| 0.342 | 171 | 0.0033 | - |
| 0.344 | 172 | 0.0035 | - |
| 0.346 | 173 | 0.007 | - |
| 0.348 | 174 | 0.0008 | - |
| 0.35 | 175 | 0.002 | - |
| 0.352 | 176 | 0.0006 | - |
| 0.354 | 177 | 0.001 | - |
| 0.356 | 178 | 0.0011 | - |
| 0.358 | 179 | 0.0057 | - |
| 0.36 | 180 | 0.0003 | - |
| 0.362 | 181 | 0.001 | - |
| 0.364 | 182 | 0.0007 | - |
| 0.366 | 183 | 0.0016 | - |
| 0.368 | 184 | 0.0018 | - |
| 0.37 | 185 | 0.001 | - |
| 0.372 | 186 | 0.0009 | - |
| 0.374 | 187 | 0.0057 | - |
| 0.376 | 188 | 0.0008 | - |
| 0.378 | 189 | 0.0182 | - |
| 0.38 | 190 | 0.0005 | - |
| 0.382 | 191 | 0.053 | - |
| 0.384 | 192 | 0.0012 | - |
| 0.386 | 193 | 0.0158 | - |
| 0.388 | 194 | 0.0043 | - |
| 0.39 | 195 | 0.0074 | - |
| 0.392 | 196 | 0.0013 | - |
| 0.394 | 197 | 0.0016 | - |
| 0.396 | 198 | 0.0021 | - |
| 0.398 | 199 | 0.0007 | - |
| 0.4 | 200 | 0.002 | - |
| 0.402 | 201 | 0.0004 | - |
| 0.404 | 202 | 0.0008 | - |
| 0.406 | 203 | 0.0002 | - |
| 0.408 | 204 | 0.0026 | - |
| 0.41 | 205 | 0.0012 | - |
| 0.412 | 206 | 0.0004 | - |
| 0.414 | 207 | 0.0017 | - |
| 0.416 | 208 | 0.0038 | - |
| 0.418 | 209 | 0.0008 | - |
| 0.42 | 210 | 0.0008 | - |
| 0.422 | 211 | 0.0007 | - |
| 0.424 | 212 | 0.0577 | - |
| 0.426 | 213 | 0.0013 | - |
| 0.428 | 214 | 0.0005 | - |
| 0.43 | 215 | 0.0015 | - |
| 0.432 | 216 | 0.0006 | - |
| 0.434 | 217 | 0.0005 | - |
| 0.436 | 218 | 0.0017 | - |
| 0.438 | 219 | 0.001 | - |
| 0.44 | 220 | 0.0002 | - |
| 0.442 | 221 | 0.0005 | - |
| 0.444 | 222 | 0.003 | - |
| 0.446 | 223 | 0.0007 | - |
| 0.448 | 224 | 0.0002 | - |
| 0.45 | 225 | 0.001 | - |
| 0.452 | 226 | 0.0006 | - |
| 0.454 | 227 | 0.001 | - |
| 0.456 | 228 | 0.0506 | - |
| 0.458 | 229 | 0.0005 | - |
| 0.46 | 230 | 0.0009 | - |
| 0.462 | 231 | 0.0015 | - |
| 0.464 | 232 | 0.0003 | - |
| 0.466 | 233 | 0.0004 | - |
| 0.468 | 234 | 0.001 | - |
| 0.47 | 235 | 0.0004 | - |
| 0.472 | 236 | 0.0007 | - |
| 0.474 | 237 | 0.0014 | - |
| 0.476 | 238 | 0.0003 | - |
| 0.478 | 239 | 0.0004 | - |
| 0.48 | 240 | 0.0007 | - |
| 0.482 | 241 | 0.0002 | - |
| 0.484 | 242 | 0.0006 | - |
| 0.486 | 243 | 0.0003 | - |
| 0.488 | 244 | 0.0004 | - |
| 0.49 | 245 | 0.0587 | - |
| 0.492 | 246 | 0.0003 | - |
| 0.494 | 247 | 0.0007 | - |
| 0.496 | 248 | 0.0013 | - |
| 0.498 | 249 | 0.0507 | - |
| 0.5 | 250 | 0.0002 | - |
| 0.502 | 251 | 0.0004 | - |
| 0.504 | 252 | 0.0003 | - |
| 0.506 | 253 | 0.0004 | - |
| 0.508 | 254 | 0.0002 | - |
| 0.51 | 255 | 0.0003 | - |
| 0.512 | 256 | 0.0096 | - |
| 0.514 | 257 | 0.0002 | - |
| 0.516 | 258 | 0.0003 | - |
| 0.518 | 259 | 0.0003 | - |
| 0.52 | 260 | 0.0013 | - |
| 0.522 | 261 | 0.0004 | - |
| 0.524 | 262 | 0.0004 | - |
| 0.526 | 263 | 0.0007 | - |
| 0.528 | 264 | 0.0006 | - |
| 0.53 | 265 | 0.0003 | - |
| 0.532 | 266 | 0.0023 | - |
| 0.534 | 267 | 0.0008 | - |
| 0.536 | 268 | 0.0002 | - |
| 0.538 | 269 | 0.0018 | - |
| 0.54 | 270 | 0.0002 | - |
| 0.542 | 271 | 0.0007 | - |
| 0.544 | 272 | 0.0001 | - |
| 0.546 | 273 | 0.0004 | - |
| 0.548 | 274 | 0.0618 | - |
| 0.55 | 275 | 0.0192 | - |
| 0.552 | 276 | 0.0009 | - |
| 0.554 | 277 | 0.0142 | - |
| 0.556 | 278 | 0.0014 | - |
| 0.558 | 279 | 0.0006 | - |
| 0.56 | 280 | 0.0565 | - |
| 0.562 | 281 | 0.0006 | - |
| 0.564 | 282 | 0.0233 | - |
| 0.566 | 283 | 0.0004 | - |
| 0.568 | 284 | 0.0116 | - |
| 0.57 | 285 | 0.0002 | - |
| 0.572 | 286 | 0.0032 | - |
| 0.574 | 287 | 0.0001 | - |
| 0.576 | 288 | 0.0003 | - |
| 0.578 | 289 | 0.0004 | - |
| 0.58 | 290 | 0.0003 | - |
| 0.582 | 291 | 0.0003 | - |
| 0.584 | 292 | 0.0003 | - |
| 0.586 | 293 | 0.0012 | - |
| 0.588 | 294 | 0.0021 | - |
| 0.59 | 295 | 0.0002 | - |
| 0.592 | 296 | 0.0003 | - |
| 0.594 | 297 | 0.0022 | - |
| 0.596 | 298 | 0.0005 | - |
| 0.598 | 299 | 0.0005 | - |
| 0.6 | 300 | 0.0024 | - |
| 0.602 | 301 | 0.0008 | - |
| 0.604 | 302 | 0.0003 | - |
| 0.606 | 303 | 0.0022 | - |
| 0.608 | 304 | 0.0069 | - |
| 0.61 | 305 | 0.0009 | - |
| 0.612 | 306 | 0.0144 | - |
| 0.614 | 307 | 0.0004 | - |
| 0.616 | 308 | 0.0006 | - |
| 0.618 | 309 | 0.0006 | - |
| 0.62 | 310 | 0.0261 | - |
| 0.622 | 311 | 0.0002 | - |
| 0.624 | 312 | 0.0003 | - |
| 0.626 | 313 | 0.0003 | - |
| 0.628 | 314 | 0.0007 | - |
| 0.63 | 315 | 0.0603 | - |
| 0.632 | 316 | 0.0002 | - |
| 0.634 | 317 | 0.0003 | - |
| 0.636 | 318 | 0.0007 | - |
| 0.638 | 319 | 0.0006 | - |
| 0.64 | 320 | 0.0002 | - |
| 0.642 | 321 | 0.0016 | - |
| 0.644 | 322 | 0.0003 | - |
| 0.646 | 323 | 0.0003 | - |
| 0.648 | 324 | 0.0002 | - |
| 0.65 | 325 | 0.0006 | - |
| 0.652 | 326 | 0.0006 | - |
| 0.654 | 327 | 0.0006 | - |
| 0.656 | 328 | 0.0002 | - |
| 0.658 | 329 | 0.0004 | - |
| 0.66 | 330 | 0.0002 | - |
| 0.662 | 331 | 0.0002 | - |
| 0.664 | 332 | 0.0001 | - |
| 0.666 | 333 | 0.0466 | - |
| 0.668 | 334 | 0.0002 | - |
| 0.67 | 335 | 0.0003 | - |
| 0.672 | 336 | 0.0005 | - |
| 0.674 | 337 | 0.0013 | - |
| 0.676 | 338 | 0.0002 | - |
| 0.678 | 339 | 0.0004 | - |
| 0.68 | 340 | 0.0573 | - |
| 0.682 | 341 | 0.0001 | - |
| 0.684 | 342 | 0.0002 | - |
| 0.686 | 343 | 0.0002 | - |
| 0.688 | 344 | 0.0009 | - |
| 0.69 | 345 | 0.024 | - |
| 0.692 | 346 | 0.0003 | - |
| 0.694 | 347 | 0.0011 | - |
| 0.696 | 348 | 0.0002 | - |
| 0.698 | 349 | 0.0191 | - |
| 0.7 | 350 | 0.0001 | - |
| 0.702 | 351 | 0.0002 | - |
| 0.704 | 352 | 0.0009 | - |
| 0.706 | 353 | 0.0004 | - |
| 0.708 | 354 | 0.0001 | - |
| 0.71 | 355 | 0.0 | - |
| 0.712 | 356 | 0.0002 | - |
| 0.714 | 357 | 0.0002 | - |
| 0.716 | 358 | 0.0009 | - |
| 0.718 | 359 | 0.0005 | - |
| 0.72 | 360 | 0.0013 | - |
| 0.722 | 361 | 0.0046 | - |
| 0.724 | 362 | 0.0001 | - |
| 0.726 | 363 | 0.0005 | - |
| 0.728 | 364 | 0.0002 | - |
| 0.73 | 365 | 0.0017 | - |
| 0.732 | 366 | 0.0332 | - |
| 0.734 | 367 | 0.0004 | - |
| 0.736 | 368 | 0.0203 | - |
| 0.738 | 369 | 0.0003 | - |
| 0.74 | 370 | 0.0001 | - |
| 0.742 | 371 | 0.0003 | - |
| 0.744 | 372 | 0.0004 | - |
| 0.746 | 373 | 0.0133 | - |
| 0.748 | 374 | 0.0009 | - |
| 0.75 | 375 | 0.0017 | - |
| 0.752 | 376 | 0.0016 | - |
| 0.754 | 377 | 0.0022 | - |
| 0.756 | 378 | 0.0015 | - |
| 0.758 | 379 | 0.0004 | - |
| 0.76 | 380 | 0.0002 | - |
| 0.762 | 381 | 0.0001 | - |
| 0.764 | 382 | 0.0004 | - |
| 0.766 | 383 | 0.0001 | - |
| 0.768 | 384 | 0.0012 | - |
| 0.77 | 385 | 0.0005 | - |
| 0.772 | 386 | 0.0018 | - |
| 0.774 | 387 | 0.032 | - |
| 0.776 | 388 | 0.0002 | - |
| 0.778 | 389 | 0.0001 | - |
| 0.78 | 390 | 0.0019 | - |
| 0.782 | 391 | 0.001 | - |
| 0.784 | 392 | 0.0003 | - |
| 0.786 | 393 | 0.0001 | - |
| 0.788 | 394 | 0.0005 | - |
| 0.79 | 395 | 0.0016 | - |
| 0.792 | 396 | 0.0005 | - |
| 0.794 | 397 | 0.0018 | - |
| 0.796 | 398 | 0.0007 | - |
| 0.798 | 399 | 0.0002 | - |
| 0.8 | 400 | 0.0004 | - |
| 0.802 | 401 | 0.0002 | - |
| 0.804 | 402 | 0.001 | - |
| 0.806 | 403 | 0.0001 | - |
| 0.808 | 404 | 0.0002 | - |
| 0.81 | 405 | 0.0002 | - |
| 0.812 | 406 | 0.0004 | - |
| 0.814 | 407 | 0.0003 | - |
| 0.816 | 408 | 0.0001 | - |
| 0.818 | 409 | 0.0004 | - |
| 0.82 | 410 | 0.001 | - |
| 0.822 | 411 | 0.0005 | - |
| 0.824 | 412 | 0.0001 | - |
| 0.826 | 413 | 0.0002 | - |
| 0.828 | 414 | 0.0001 | - |
| 0.83 | 415 | 0.0004 | - |
| 0.832 | 416 | 0.0002 | - |
| 0.834 | 417 | 0.0002 | - |
| 0.836 | 418 | 0.0001 | - |
| 0.838 | 419 | 0.0002 | - |
| 0.84 | 420 | 0.0011 | - |
| 0.842 | 421 | 0.0002 | - |
| 0.844 | 422 | 0.0003 | - |
| 0.846 | 423 | 0.0002 | - |
| 0.848 | 424 | 0.0004 | - |
| 0.85 | 425 | 0.0002 | - |
| 0.852 | 426 | 0.0002 | - |
| 0.854 | 427 | 0.0501 | - |
| 0.856 | 428 | 0.0001 | - |
| 0.858 | 429 | 0.0002 | - |
| 0.86 | 430 | 0.0004 | - |
| 0.862 | 431 | 0.0003 | - |
| 0.864 | 432 | 0.0001 | - |
| 0.866 | 433 | 0.0001 | - |
| 0.868 | 434 | 0.0001 | - |
| 0.87 | 435 | 0.0002 | - |
| 0.872 | 436 | 0.0008 | - |
| 0.874 | 437 | 0.0001 | - |
| 0.876 | 438 | 0.0002 | - |
| 0.878 | 439 | 0.0002 | - |
| 0.88 | 440 | 0.0004 | - |
| 0.882 | 441 | 0.0002 | - |
| 0.884 | 442 | 0.0002 | - |
| 0.886 | 443 | 0.0001 | - |
| 0.888 | 444 | 0.0006 | - |
| 0.89 | 445 | 0.0002 | - |
| 0.892 | 446 | 0.0003 | - |
| 0.894 | 447 | 0.0002 | - |
| 0.896 | 448 | 0.0011 | - |
| 0.898 | 449 | 0.0002 | - |
| 0.9 | 450 | 0.0004 | - |
| 0.902 | 451 | 0.0001 | - |
| 0.904 | 452 | 0.0009 | - |
| 0.906 | 453 | 0.0001 | - |
| 0.908 | 454 | 0.0003 | - |
| 0.91 | 455 | 0.0006 | - |
| 0.912 | 456 | 0.0028 | - |
| 0.914 | 457 | 0.0002 | - |
| 0.916 | 458 | 0.0001 | - |
| 0.918 | 459 | 0.0002 | - |
| 0.92 | 460 | 0.0002 | - |
| 0.922 | 461 | 0.0004 | - |
| 0.924 | 462 | 0.0001 | - |
| 0.926 | 463 | 0.0001 | - |
| 0.928 | 464 | 0.0001 | - |
| 0.93 | 465 | 0.002 | - |
| 0.932 | 466 | 0.0003 | - |
| 0.934 | 467 | 0.0006 | - |
| 0.936 | 468 | 0.0001 | - |
| 0.938 | 469 | 0.0002 | - |
| 0.94 | 470 | 0.0002 | - |
| 0.942 | 471 | 0.0001 | - |
| 0.944 | 472 | 0.0002 | - |
| 0.946 | 473 | 0.0003 | - |
| 0.948 | 474 | 0.0003 | - |
| 0.95 | 475 | 0.001 | - |
| 0.952 | 476 | 0.0002 | - |
| 0.954 | 477 | 0.0001 | - |
| 0.956 | 478 | 0.0003 | - |
| 0.958 | 479 | 0.0002 | - |
| 0.96 | 480 | 0.0487 | - |
| 0.962 | 481 | 0.0002 | - |
| 0.964 | 482 | 0.0004 | - |
| 0.966 | 483 | 0.0002 | - |
| 0.968 | 484 | 0.0001 | - |
| 0.97 | 485 | 0.0003 | - |
| 0.972 | 486 | 0.0002 | - |
| 0.974 | 487 | 0.0003 | - |
| 0.976 | 488 | 0.0088 | - |
| 0.978 | 489 | 0.0003 | - |
| 0.98 | 490 | 0.0011 | - |
| 0.982 | 491 | 0.0003 | - |
| 0.984 | 492 | 0.0001 | - |
| 0.986 | 493 | 0.0001 | - |
| 0.988 | 494 | 0.0003 | - |
| 0.99 | 495 | 0.0002 | - |
| 0.992 | 496 | 0.0004 | - |
| 0.994 | 497 | 0.0003 | - |
| 0.996 | 498 | 0.0001 | - |
| 0.998 | 499 | 0.0002 | - |
| 1.0 | 500 | 0.0002 | - |
### Framework Versions
- Python: 3.11.3
- SetFit: 1.2.0.dev0
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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}
}
```
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