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model_g20_multilabel_30sample

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
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 ---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Government-led initiatives have introduced tailored insurance products that
mitigate the financial risks faced by smallholder farmers exposed to climate-induced
hazards such as droughts and floods.
- text: "National Food and Nutrition Strategic Plan 2011-2015\n\n53\n\n\n\n5.10.7\
\ Resource allocation and generation \n\nThe resources required for monitoring\
\ and evaluation of nutrition intervention should \nnormally be built into the\
\ cost of the intervention programmes."
- text: 'COVID-19: The Development Program for Drinking Water Supply and Sanitation
Systems of the Kyrgyz Republic until 2026 was approved.
The Program is aimed at increasing the provision of drinking water of standard
quality, improving the health and quality of life of the population of the republic,
reducing the harmful effects on the environment through the construction, reconstruction,
and modernization of drinking water supply and sanitation systems.'
- text: "Objectives of this project are \nto develop socio-economic infrastructure\
\ in the rural sector, expand road \ntransportation network, conduct rural employment\
\ activities, and build \n\n\n\n 227\n\nlocal level’s institutional capacity."
- text: "Housing and Community Amenities \n \n\n133."
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
---
# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier 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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 128 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **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)
## 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("faodl/model_g20_multilabel_30sample")
# Run inference
preds = model("Housing and Community Amenities
133.")
```
<!--
### 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 | 1 | 41.0925 | 506 |
### Training Hyperparameters
- batch_size: (8, 8)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0001 | 1 | 0.2661 | - |
| 0.0068 | 50 | 0.1923 | - |
| 0.0136 | 100 | 0.1856 | - |
| 0.0204 | 150 | 0.1927 | - |
| 0.0272 | 200 | 0.1708 | - |
| 0.0340 | 250 | 0.1706 | - |
| 0.0408 | 300 | 0.156 | - |
| 0.0476 | 350 | 0.1597 | - |
| 0.0544 | 400 | 0.149 | - |
| 0.0612 | 450 | 0.1488 | - |
| 0.0680 | 500 | 0.1375 | - |
| 0.0748 | 550 | 0.1234 | - |
| 0.0816 | 600 | 0.1339 | - |
| 0.0884 | 650 | 0.126 | - |
| 0.0952 | 700 | 0.1347 | - |
| 0.1020 | 750 | 0.1323 | - |
| 0.1088 | 800 | 0.1159 | - |
| 0.1156 | 850 | 0.1236 | - |
| 0.1224 | 900 | 0.1218 | - |
| 0.1293 | 950 | 0.1323 | - |
| 0.1361 | 1000 | 0.1258 | - |
| 0.1429 | 1050 | 0.1206 | - |
| 0.1497 | 1100 | 0.1127 | - |
| 0.1565 | 1150 | 0.1211 | - |
| 0.1633 | 1200 | 0.1234 | - |
| 0.1701 | 1250 | 0.1178 | - |
| 0.1769 | 1300 | 0.1009 | - |
| 0.1837 | 1350 | 0.11 | - |
| 0.1905 | 1400 | 0.1103 | - |
| 0.1973 | 1450 | 0.1015 | - |
| 0.2041 | 1500 | 0.0926 | - |
| 0.2109 | 1550 | 0.099 | - |
| 0.2177 | 1600 | 0.1079 | - |
| 0.2245 | 1650 | 0.0979 | - |
| 0.2313 | 1700 | 0.1001 | - |
| 0.2381 | 1750 | 0.1039 | - |
| 0.2449 | 1800 | 0.0838 | - |
| 0.2517 | 1850 | 0.0941 | - |
| 0.2585 | 1900 | 0.0929 | - |
| 0.2653 | 1950 | 0.0851 | - |
| 0.2721 | 2000 | 0.0956 | - |
| 0.2789 | 2050 | 0.075 | - |
| 0.2857 | 2100 | 0.1067 | - |
| 0.2925 | 2150 | 0.0891 | - |
| 0.2993 | 2200 | 0.0939 | - |
| 0.3061 | 2250 | 0.0908 | - |
| 0.3129 | 2300 | 0.0847 | - |
| 0.3197 | 2350 | 0.0812 | - |
| 0.3265 | 2400 | 0.0918 | - |
| 0.3333 | 2450 | 0.0935 | - |
| 0.3401 | 2500 | 0.0792 | - |
| 0.3469 | 2550 | 0.0669 | - |
| 0.3537 | 2600 | 0.0883 | - |
| 0.3605 | 2650 | 0.0829 | - |
| 0.3673 | 2700 | 0.0656 | - |
| 0.3741 | 2750 | 0.0752 | - |
| 0.3810 | 2800 | 0.0825 | - |
| 0.3878 | 2850 | 0.0813 | - |
| 0.3946 | 2900 | 0.0852 | - |
| 0.4014 | 2950 | 0.0903 | - |
| 0.4082 | 3000 | 0.0902 | - |
| 0.4150 | 3050 | 0.0739 | - |
| 0.4218 | 3100 | 0.0786 | - |
| 0.4286 | 3150 | 0.083 | - |
| 0.4354 | 3200 | 0.0648 | - |
| 0.4422 | 3250 | 0.0704 | - |
| 0.4490 | 3300 | 0.0798 | - |
| 0.4558 | 3350 | 0.0651 | - |
| 0.4626 | 3400 | 0.0705 | - |
| 0.4694 | 3450 | 0.0653 | - |
| 0.4762 | 3500 | 0.0767 | - |
| 0.4830 | 3550 | 0.0747 | - |
| 0.4898 | 3600 | 0.0738 | - |
| 0.4966 | 3650 | 0.055 | - |
| 0.5034 | 3700 | 0.0741 | - |
| 0.5102 | 3750 | 0.0688 | - |
| 0.5170 | 3800 | 0.0699 | - |
| 0.5238 | 3850 | 0.0787 | - |
| 0.5306 | 3900 | 0.0673 | - |
| 0.5374 | 3950 | 0.0629 | - |
| 0.5442 | 4000 | 0.0639 | - |
| 0.5510 | 4050 | 0.0809 | - |
| 0.5578 | 4100 | 0.0694 | - |
| 0.5646 | 4150 | 0.0696 | - |
| 0.5714 | 4200 | 0.0577 | - |
| 0.5782 | 4250 | 0.0707 | - |
| 0.5850 | 4300 | 0.0542 | - |
| 0.5918 | 4350 | 0.0541 | - |
| 0.5986 | 4400 | 0.0462 | - |
| 0.6054 | 4450 | 0.0675 | - |
| 0.6122 | 4500 | 0.0561 | - |
| 0.6190 | 4550 | 0.056 | - |
| 0.6259 | 4600 | 0.0556 | - |
| 0.6327 | 4650 | 0.0552 | - |
| 0.6395 | 4700 | 0.0566 | - |
| 0.6463 | 4750 | 0.0578 | - |
| 0.6531 | 4800 | 0.0488 | - |
| 0.6599 | 4850 | 0.0419 | - |
| 0.6667 | 4900 | 0.0485 | - |
| 0.6735 | 4950 | 0.0477 | - |
| 0.6803 | 5000 | 0.0566 | - |
| 0.6871 | 5050 | 0.0571 | - |
| 0.6939 | 5100 | 0.0531 | - |
| 0.7007 | 5150 | 0.0563 | - |
| 0.7075 | 5200 | 0.0452 | - |
| 0.7143 | 5250 | 0.0459 | - |
| 0.7211 | 5300 | 0.039 | - |
| 0.7279 | 5350 | 0.0382 | - |
| 0.7347 | 5400 | 0.0679 | - |
| 0.7415 | 5450 | 0.0465 | - |
| 0.7483 | 5500 | 0.0493 | - |
| 0.7551 | 5550 | 0.0489 | - |
| 0.7619 | 5600 | 0.0443 | - |
| 0.7687 | 5650 | 0.0591 | - |
| 0.7755 | 5700 | 0.0441 | - |
| 0.7823 | 5750 | 0.0501 | - |
| 0.7891 | 5800 | 0.0497 | - |
| 0.7959 | 5850 | 0.0543 | - |
| 0.8027 | 5900 | 0.05 | - |
| 0.8095 | 5950 | 0.0449 | - |
| 0.8163 | 6000 | 0.0432 | - |
| 0.8231 | 6050 | 0.0491 | - |
| 0.8299 | 6100 | 0.0507 | - |
| 0.8367 | 6150 | 0.0405 | - |
| 0.8435 | 6200 | 0.0426 | - |
| 0.8503 | 6250 | 0.0528 | - |
| 0.8571 | 6300 | 0.0428 | - |
| 0.8639 | 6350 | 0.0534 | - |
| 0.8707 | 6400 | 0.0512 | - |
| 0.8776 | 6450 | 0.049 | - |
| 0.8844 | 6500 | 0.0386 | - |
| 0.8912 | 6550 | 0.0468 | - |
| 0.8980 | 6600 | 0.0505 | - |
| 0.9048 | 6650 | 0.0538 | - |
| 0.9116 | 6700 | 0.0484 | - |
| 0.9184 | 6750 | 0.044 | - |
| 0.9252 | 6800 | 0.0431 | - |
| 0.9320 | 6850 | 0.0456 | - |
| 0.9388 | 6900 | 0.0342 | - |
| 0.9456 | 6950 | 0.0445 | - |
| 0.9524 | 7000 | 0.0499 | - |
| 0.9592 | 7050 | 0.0589 | - |
| 0.9660 | 7100 | 0.0409 | - |
| 0.9728 | 7150 | 0.04 | - |
| 0.9796 | 7200 | 0.0443 | - |
| 0.9864 | 7250 | 0.0373 | - |
| 0.9932 | 7300 | 0.0306 | - |
| 1.0 | 7350 | 0.0303 | - |
| 1.0068 | 7400 | 0.0317 | - |
| 1.0136 | 7450 | 0.0364 | - |
| 1.0204 | 7500 | 0.0349 | - |
| 1.0272 | 7550 | 0.0388 | - |
| 1.0340 | 7600 | 0.0466 | - |
| 1.0408 | 7650 | 0.0334 | - |
| 1.0476 | 7700 | 0.0512 | - |
| 1.0544 | 7750 | 0.0413 | - |
| 1.0612 | 7800 | 0.0399 | - |
| 1.0680 | 7850 | 0.0412 | - |
| 1.0748 | 7900 | 0.0341 | - |
| 1.0816 | 7950 | 0.0395 | - |
| 1.0884 | 8000 | 0.045 | - |
| 1.0952 | 8050 | 0.0385 | - |
| 1.1020 | 8100 | 0.038 | - |
| 1.1088 | 8150 | 0.0376 | - |
| 1.1156 | 8200 | 0.0434 | - |
| 1.1224 | 8250 | 0.0323 | - |
| 1.1293 | 8300 | 0.0364 | - |
| 1.1361 | 8350 | 0.033 | - |
| 1.1429 | 8400 | 0.025 | - |
| 1.1497 | 8450 | 0.0461 | - |
| 1.1565 | 8500 | 0.033 | - |
| 1.1633 | 8550 | 0.0317 | - |
| 1.1701 | 8600 | 0.047 | - |
| 1.1769 | 8650 | 0.0344 | - |
| 1.1837 | 8700 | 0.0388 | - |
| 1.1905 | 8750 | 0.0359 | - |
| 1.1973 | 8800 | 0.0429 | - |
| 1.2041 | 8850 | 0.0355 | - |
| 1.2109 | 8900 | 0.0421 | - |
| 1.2177 | 8950 | 0.0351 | - |
| 1.2245 | 9000 | 0.0359 | - |
| 1.2313 | 9050 | 0.035 | - |
| 1.2381 | 9100 | 0.0331 | - |
| 1.2449 | 9150 | 0.0337 | - |
| 1.2517 | 9200 | 0.0376 | - |
| 1.2585 | 9250 | 0.0366 | - |
| 1.2653 | 9300 | 0.0369 | - |
| 1.2721 | 9350 | 0.0353 | - |
| 1.2789 | 9400 | 0.0439 | - |
| 1.2857 | 9450 | 0.0439 | - |
| 1.2925 | 9500 | 0.0288 | - |
| 1.2993 | 9550 | 0.0404 | - |
| 1.3061 | 9600 | 0.0355 | - |
| 1.3129 | 9650 | 0.0375 | - |
| 1.3197 | 9700 | 0.0452 | - |
| 1.3265 | 9750 | 0.0408 | - |
| 1.3333 | 9800 | 0.0369 | - |
| 1.3401 | 9850 | 0.0337 | - |
| 1.3469 | 9900 | 0.0294 | - |
| 1.3537 | 9950 | 0.0341 | - |
| 1.3605 | 10000 | 0.0356 | - |
| 1.3673 | 10050 | 0.0394 | - |
| 1.3741 | 10100 | 0.0387 | - |
| 1.3810 | 10150 | 0.0276 | - |
| 1.3878 | 10200 | 0.0345 | - |
| 1.3946 | 10250 | 0.037 | - |
| 1.4014 | 10300 | 0.0272 | - |
| 1.4082 | 10350 | 0.0341 | - |
| 1.4150 | 10400 | 0.033 | - |
| 1.4218 | 10450 | 0.0517 | - |
| 1.4286 | 10500 | 0.0297 | - |
| 1.4354 | 10550 | 0.0388 | - |
| 1.4422 | 10600 | 0.0312 | - |
| 1.4490 | 10650 | 0.0283 | - |
| 1.4558 | 10700 | 0.0287 | - |
| 1.4626 | 10750 | 0.0319 | - |
| 1.4694 | 10800 | 0.0343 | - |
| 1.4762 | 10850 | 0.033 | - |
| 1.4830 | 10900 | 0.0444 | - |
| 1.4898 | 10950 | 0.0239 | - |
| 1.4966 | 11000 | 0.0294 | - |
| 1.5034 | 11050 | 0.0313 | - |
| 1.5102 | 11100 | 0.0344 | - |
| 1.5170 | 11150 | 0.0304 | - |
| 1.5238 | 11200 | 0.0339 | - |
| 1.5306 | 11250 | 0.0342 | - |
| 1.5374 | 11300 | 0.0291 | - |
| 1.5442 | 11350 | 0.0301 | - |
| 1.5510 | 11400 | 0.0309 | - |
| 1.5578 | 11450 | 0.0346 | - |
| 1.5646 | 11500 | 0.0406 | - |
| 1.5714 | 11550 | 0.034 | - |
| 1.5782 | 11600 | 0.0273 | - |
| 1.5850 | 11650 | 0.0316 | - |
| 1.5918 | 11700 | 0.0404 | - |
| 1.5986 | 11750 | 0.0295 | - |
| 1.6054 | 11800 | 0.0385 | - |
| 1.6122 | 11850 | 0.0373 | - |
| 1.6190 | 11900 | 0.0384 | - |
| 1.6259 | 11950 | 0.0307 | - |
| 1.6327 | 12000 | 0.0222 | - |
| 1.6395 | 12050 | 0.0257 | - |
| 1.6463 | 12100 | 0.0313 | - |
| 1.6531 | 12150 | 0.0293 | - |
| 1.6599 | 12200 | 0.0312 | - |
| 1.6667 | 12250 | 0.0299 | - |
| 1.6735 | 12300 | 0.0284 | - |
| 1.6803 | 12350 | 0.042 | - |
| 1.6871 | 12400 | 0.031 | - |
| 1.6939 | 12450 | 0.0295 | - |
| 1.7007 | 12500 | 0.0339 | - |
| 1.7075 | 12550 | 0.0385 | - |
| 1.7143 | 12600 | 0.0355 | - |
| 1.7211 | 12650 | 0.0291 | - |
| 1.7279 | 12700 | 0.0366 | - |
| 1.7347 | 12750 | 0.0337 | - |
| 1.7415 | 12800 | 0.0268 | - |
| 1.7483 | 12850 | 0.0373 | - |
| 1.7551 | 12900 | 0.0404 | - |
| 1.7619 | 12950 | 0.025 | - |
| 1.7687 | 13000 | 0.0282 | - |
| 1.7755 | 13050 | 0.0282 | - |
| 1.7823 | 13100 | 0.0341 | - |
| 1.7891 | 13150 | 0.0338 | - |
| 1.7959 | 13200 | 0.0342 | - |
| 1.8027 | 13250 | 0.035 | - |
| 1.8095 | 13300 | 0.0399 | - |
| 1.8163 | 13350 | 0.035 | - |
| 1.8231 | 13400 | 0.0367 | - |
| 1.8299 | 13450 | 0.0294 | - |
| 1.8367 | 13500 | 0.0382 | - |
| 1.8435 | 13550 | 0.0261 | - |
| 1.8503 | 13600 | 0.0301 | - |
| 1.8571 | 13650 | 0.0258 | - |
| 1.8639 | 13700 | 0.0301 | - |
| 1.8707 | 13750 | 0.0306 | - |
| 1.8776 | 13800 | 0.0242 | - |
| 1.8844 | 13850 | 0.0258 | - |
| 1.8912 | 13900 | 0.0296 | - |
| 1.8980 | 13950 | 0.0338 | - |
| 1.9048 | 14000 | 0.0315 | - |
| 1.9116 | 14050 | 0.0282 | - |
| 1.9184 | 14100 | 0.0325 | - |
| 1.9252 | 14150 | 0.0286 | - |
| 1.9320 | 14200 | 0.0355 | - |
| 1.9388 | 14250 | 0.0317 | - |
| 1.9456 | 14300 | 0.0314 | - |
| 1.9524 | 14350 | 0.031 | - |
| 1.9592 | 14400 | 0.03 | - |
| 1.9660 | 14450 | 0.0262 | - |
| 1.9728 | 14500 | 0.0275 | - |
| 1.9796 | 14550 | 0.0356 | - |
| 1.9864 | 14600 | 0.0369 | - |
| 1.9932 | 14650 | 0.0364 | - |
| 2.0 | 14700 | 0.0344 | - |
| 2.0068 | 14750 | 0.0248 | - |
| 2.0136 | 14800 | 0.0273 | - |
| 2.0204 | 14850 | 0.0282 | - |
| 2.0272 | 14900 | 0.023 | - |
| 2.0340 | 14950 | 0.0278 | - |
| 2.0408 | 15000 | 0.0355 | - |
| 2.0476 | 15050 | 0.0258 | - |
| 2.0544 | 15100 | 0.0258 | - |
| 2.0612 | 15150 | 0.0322 | - |
| 2.0680 | 15200 | 0.0266 | - |
| 2.0748 | 15250 | 0.0279 | - |
| 2.0816 | 15300 | 0.0282 | - |
| 2.0884 | 15350 | 0.0289 | - |
| 2.0952 | 15400 | 0.024 | - |
| 2.1020 | 15450 | 0.0268 | - |
| 2.1088 | 15500 | 0.0348 | - |
| 2.1156 | 15550 | 0.0281 | - |
| 2.1224 | 15600 | 0.0282 | - |
| 2.1293 | 15650 | 0.0218 | - |
| 2.1361 | 15700 | 0.0201 | - |
| 2.1429 | 15750 | 0.0207 | - |
| 2.1497 | 15800 | 0.0308 | - |
| 2.1565 | 15850 | 0.0261 | - |
| 2.1633 | 15900 | 0.0292 | - |
| 2.1701 | 15950 | 0.0308 | - |
| 2.1769 | 16000 | 0.0298 | - |
| 2.1837 | 16050 | 0.0308 | - |
| 2.1905 | 16100 | 0.0359 | - |
| 2.1973 | 16150 | 0.0265 | - |
| 2.2041 | 16200 | 0.0351 | - |
| 2.2109 | 16250 | 0.0223 | - |
| 2.2177 | 16300 | 0.0322 | - |
| 2.2245 | 16350 | 0.0261 | - |
| 2.2313 | 16400 | 0.0206 | - |
| 2.2381 | 16450 | 0.0384 | - |
| 2.2449 | 16500 | 0.0381 | - |
| 2.2517 | 16550 | 0.0238 | - |
| 2.2585 | 16600 | 0.0261 | - |
| 2.2653 | 16650 | 0.0323 | - |
| 2.2721 | 16700 | 0.0296 | - |
| 2.2789 | 16750 | 0.0256 | - |
| 2.2857 | 16800 | 0.0287 | - |
| 2.2925 | 16850 | 0.0272 | - |
| 2.2993 | 16900 | 0.0285 | - |
| 2.3061 | 16950 | 0.0245 | - |
| 2.3129 | 17000 | 0.0299 | - |
| 2.3197 | 17050 | 0.0193 | - |
| 2.3265 | 17100 | 0.0234 | - |
| 2.3333 | 17150 | 0.0308 | - |
| 2.3401 | 17200 | 0.0239 | - |
| 2.3469 | 17250 | 0.0309 | - |
| 2.3537 | 17300 | 0.0331 | - |
| 2.3605 | 17350 | 0.0316 | - |
| 2.3673 | 17400 | 0.0292 | - |
| 2.3741 | 17450 | 0.0337 | - |
| 2.3810 | 17500 | 0.0338 | - |
| 2.3878 | 17550 | 0.0288 | - |
| 2.3946 | 17600 | 0.031 | - |
| 2.4014 | 17650 | 0.0251 | - |
| 2.4082 | 17700 | 0.0288 | - |
| 2.4150 | 17750 | 0.0249 | - |
| 2.4218 | 17800 | 0.0281 | - |
| 2.4286 | 17850 | 0.0284 | - |
| 2.4354 | 17900 | 0.0268 | - |
| 2.4422 | 17950 | 0.0303 | - |
| 2.4490 | 18000 | 0.0233 | - |
| 2.4558 | 18050 | 0.0297 | - |
| 2.4626 | 18100 | 0.0265 | - |
| 2.4694 | 18150 | 0.0306 | - |
| 2.4762 | 18200 | 0.0286 | - |
| 2.4830 | 18250 | 0.0278 | - |
| 2.4898 | 18300 | 0.0254 | - |
| 2.4966 | 18350 | 0.0278 | - |
| 2.5034 | 18400 | 0.0257 | - |
| 2.5102 | 18450 | 0.0272 | - |
| 2.5170 | 18500 | 0.0297 | - |
| 2.5238 | 18550 | 0.0262 | - |
| 2.5306 | 18600 | 0.0309 | - |
| 2.5374 | 18650 | 0.0259 | - |
| 2.5442 | 18700 | 0.0212 | - |
| 2.5510 | 18750 | 0.026 | - |
| 2.5578 | 18800 | 0.0252 | - |
| 2.5646 | 18850 | 0.0228 | - |
| 2.5714 | 18900 | 0.0304 | - |
| 2.5782 | 18950 | 0.0278 | - |
| 2.5850 | 19000 | 0.0263 | - |
| 2.5918 | 19050 | 0.0305 | - |
| 2.5986 | 19100 | 0.0315 | - |
| 2.6054 | 19150 | 0.0288 | - |
| 2.6122 | 19200 | 0.0221 | - |
| 2.6190 | 19250 | 0.022 | - |
| 2.6259 | 19300 | 0.0299 | - |
| 2.6327 | 19350 | 0.0302 | - |
| 2.6395 | 19400 | 0.0282 | - |
| 2.6463 | 19450 | 0.0308 | - |
| 2.6531 | 19500 | 0.0306 | - |
| 2.6599 | 19550 | 0.0327 | - |
| 2.6667 | 19600 | 0.0284 | - |
| 2.6735 | 19650 | 0.0185 | - |
| 2.6803 | 19700 | 0.0248 | - |
| 2.6871 | 19750 | 0.0212 | - |
| 2.6939 | 19800 | 0.0254 | - |
| 2.7007 | 19850 | 0.0276 | - |
| 2.7075 | 19900 | 0.027 | - |
| 2.7143 | 19950 | 0.0261 | - |
| 2.7211 | 20000 | 0.0307 | - |
| 2.7279 | 20050 | 0.0225 | - |
| 2.7347 | 20100 | 0.0189 | - |
| 2.7415 | 20150 | 0.0325 | - |
| 2.7483 | 20200 | 0.0304 | - |
| 2.7551 | 20250 | 0.0351 | - |
| 2.7619 | 20300 | 0.0274 | - |
| 2.7687 | 20350 | 0.0318 | - |
| 2.7755 | 20400 | 0.0266 | - |
| 2.7823 | 20450 | 0.0211 | - |
| 2.7891 | 20500 | 0.0388 | - |
| 2.7959 | 20550 | 0.0245 | - |
| 2.8027 | 20600 | 0.0307 | - |
| 2.8095 | 20650 | 0.0346 | - |
| 2.8163 | 20700 | 0.0251 | - |
| 2.8231 | 20750 | 0.0289 | - |
| 2.8299 | 20800 | 0.0338 | - |
| 2.8367 | 20850 | 0.0228 | - |
| 2.8435 | 20900 | 0.0248 | - |
| 2.8503 | 20950 | 0.0176 | - |
| 2.8571 | 21000 | 0.0277 | - |
| 2.8639 | 21050 | 0.0312 | - |
| 2.8707 | 21100 | 0.0271 | - |
| 2.8776 | 21150 | 0.0251 | - |
| 2.8844 | 21200 | 0.0253 | - |
| 2.8912 | 21250 | 0.0304 | - |
| 2.8980 | 21300 | 0.0321 | - |
| 2.9048 | 21350 | 0.0223 | - |
| 2.9116 | 21400 | 0.0269 | - |
| 2.9184 | 21450 | 0.0326 | - |
| 2.9252 | 21500 | 0.0226 | - |
| 2.9320 | 21550 | 0.0347 | - |
| 2.9388 | 21600 | 0.0223 | - |
| 2.9456 | 21650 | 0.0256 | - |
| 2.9524 | 21700 | 0.0256 | - |
| 2.9592 | 21750 | 0.0322 | - |
| 2.9660 | 21800 | 0.0281 | - |
| 2.9728 | 21850 | 0.0318 | - |
| 2.9796 | 21900 | 0.0279 | - |
| 2.9864 | 21950 | 0.0303 | - |
| 2.9932 | 22000 | 0.0349 | - |
| 3.0 | 22050 | 0.0254 | - |
| 3.0068 | 22100 | 0.0185 | - |
| 3.0136 | 22150 | 0.0241 | - |
| 3.0204 | 22200 | 0.0285 | - |
| 3.0272 | 22250 | 0.0257 | - |
| 3.0340 | 22300 | 0.0247 | - |
| 3.0408 | 22350 | 0.023 | - |
| 3.0476 | 22400 | 0.0335 | - |
| 3.0544 | 22450 | 0.0302 | - |
| 3.0612 | 22500 | 0.0249 | - |
| 3.0680 | 22550 | 0.029 | - |
| 3.0748 | 22600 | 0.0312 | - |
| 3.0816 | 22650 | 0.0303 | - |
| 3.0884 | 22700 | 0.0225 | - |
| 3.0952 | 22750 | 0.0271 | - |
| 3.1020 | 22800 | 0.0275 | - |
| 3.1088 | 22850 | 0.0264 | - |
| 3.1156 | 22900 | 0.0202 | - |
| 3.1224 | 22950 | 0.0247 | - |
| 3.1293 | 23000 | 0.0292 | - |
| 3.1361 | 23050 | 0.0235 | - |
| 3.1429 | 23100 | 0.019 | - |
| 3.1497 | 23150 | 0.0247 | - |
| 3.1565 | 23200 | 0.0219 | - |
| 3.1633 | 23250 | 0.0217 | - |
| 3.1701 | 23300 | 0.0236 | - |
| 3.1769 | 23350 | 0.0223 | - |
| 3.1837 | 23400 | 0.0237 | - |
| 3.1905 | 23450 | 0.0307 | - |
| 3.1973 | 23500 | 0.0275 | - |
| 3.2041 | 23550 | 0.0192 | - |
| 3.2109 | 23600 | 0.0198 | - |
| 3.2177 | 23650 | 0.0322 | - |
| 3.2245 | 23700 | 0.0195 | - |
| 3.2313 | 23750 | 0.019 | - |
| 3.2381 | 23800 | 0.0266 | - |
| 3.2449 | 23850 | 0.0287 | - |
| 3.2517 | 23900 | 0.0205 | - |
| 3.2585 | 23950 | 0.025 | - |
| 3.2653 | 24000 | 0.0282 | - |
| 3.2721 | 24050 | 0.0261 | - |
| 3.2789 | 24100 | 0.0275 | - |
| 3.2857 | 24150 | 0.0273 | - |
| 3.2925 | 24200 | 0.0195 | - |
| 3.2993 | 24250 | 0.0265 | - |
| 3.3061 | 24300 | 0.0276 | - |
| 3.3129 | 24350 | 0.0277 | - |
| 3.3197 | 24400 | 0.0224 | - |
| 3.3265 | 24450 | 0.0231 | - |
| 3.3333 | 24500 | 0.0275 | - |
| 3.3401 | 24550 | 0.0333 | - |
| 3.3469 | 24600 | 0.0181 | - |
| 3.3537 | 24650 | 0.0266 | - |
| 3.3605 | 24700 | 0.0268 | - |
| 3.3673 | 24750 | 0.0177 | - |
| 3.3741 | 24800 | 0.0185 | - |
| 3.3810 | 24850 | 0.023 | - |
| 3.3878 | 24900 | 0.0281 | - |
| 3.3946 | 24950 | 0.0202 | - |
| 3.4014 | 25000 | 0.0206 | - |
| 3.4082 | 25050 | 0.0224 | - |
| 3.4150 | 25100 | 0.0275 | - |
| 3.4218 | 25150 | 0.0272 | - |
| 3.4286 | 25200 | 0.0221 | - |
| 3.4354 | 25250 | 0.0259 | - |
| 3.4422 | 25300 | 0.0244 | - |
| 3.4490 | 25350 | 0.034 | - |
| 3.4558 | 25400 | 0.0258 | - |
| 3.4626 | 25450 | 0.0271 | - |
| 3.4694 | 25500 | 0.0291 | - |
| 3.4762 | 25550 | 0.0204 | - |
| 3.4830 | 25600 | 0.0248 | - |
| 3.4898 | 25650 | 0.0225 | - |
| 3.4966 | 25700 | 0.0347 | - |
| 3.5034 | 25750 | 0.0243 | - |
| 3.5102 | 25800 | 0.031 | - |
| 3.5170 | 25850 | 0.024 | - |
| 3.5238 | 25900 | 0.0199 | - |
| 3.5306 | 25950 | 0.0278 | - |
| 3.5374 | 26000 | 0.0318 | - |
| 3.5442 | 26050 | 0.0267 | - |
| 3.5510 | 26100 | 0.027 | - |
| 3.5578 | 26150 | 0.0191 | - |
| 3.5646 | 26200 | 0.0233 | - |
| 3.5714 | 26250 | 0.0239 | - |
| 3.5782 | 26300 | 0.0203 | - |
| 3.5850 | 26350 | 0.0243 | - |
| 3.5918 | 26400 | 0.0246 | - |
| 3.5986 | 26450 | 0.0233 | - |
| 3.6054 | 26500 | 0.0364 | - |
| 3.6122 | 26550 | 0.0273 | - |
| 3.6190 | 26600 | 0.0269 | - |
| 3.6259 | 26650 | 0.0206 | - |
| 3.6327 | 26700 | 0.0316 | - |
| 3.6395 | 26750 | 0.023 | - |
| 3.6463 | 26800 | 0.0257 | - |
| 3.6531 | 26850 | 0.0263 | - |
| 3.6599 | 26900 | 0.0218 | - |
| 3.6667 | 26950 | 0.0257 | - |
| 3.6735 | 27000 | 0.0228 | - |
| 3.6803 | 27050 | 0.0256 | - |
| 3.6871 | 27100 | 0.0239 | - |
| 3.6939 | 27150 | 0.0225 | - |
| 3.7007 | 27200 | 0.0294 | - |
| 3.7075 | 27250 | 0.0187 | - |
| 3.7143 | 27300 | 0.02 | - |
| 3.7211 | 27350 | 0.0261 | - |
| 3.7279 | 27400 | 0.0201 | - |
| 3.7347 | 27450 | 0.0253 | - |
| 3.7415 | 27500 | 0.0265 | - |
| 3.7483 | 27550 | 0.0303 | - |
| 3.7551 | 27600 | 0.0239 | - |
| 3.7619 | 27650 | 0.0246 | - |
| 3.7687 | 27700 | 0.0249 | - |
| 3.7755 | 27750 | 0.023 | - |
| 3.7823 | 27800 | 0.0237 | - |
| 3.7891 | 27850 | 0.0197 | - |
| 3.7959 | 27900 | 0.0268 | - |
| 3.8027 | 27950 | 0.0246 | - |
| 3.8095 | 28000 | 0.029 | - |
| 3.8163 | 28050 | 0.0248 | - |
| 3.8231 | 28100 | 0.0275 | - |
| 3.8299 | 28150 | 0.0241 | - |
| 3.8367 | 28200 | 0.027 | - |
| 3.8435 | 28250 | 0.0252 | - |
| 3.8503 | 28300 | 0.0245 | - |
| 3.8571 | 28350 | 0.0241 | - |
| 3.8639 | 28400 | 0.0264 | - |
| 3.8707 | 28450 | 0.0233 | - |
| 3.8776 | 28500 | 0.0319 | - |
| 3.8844 | 28550 | 0.0236 | - |
| 3.8912 | 28600 | 0.0277 | - |
| 3.8980 | 28650 | 0.0178 | - |
| 3.9048 | 28700 | 0.0209 | - |
| 3.9116 | 28750 | 0.0263 | - |
| 3.9184 | 28800 | 0.0236 | - |
| 3.9252 | 28850 | 0.0216 | - |
| 3.9320 | 28900 | 0.0209 | - |
| 3.9388 | 28950 | 0.0283 | - |
| 3.9456 | 29000 | 0.0307 | - |
| 3.9524 | 29050 | 0.0276 | - |
| 3.9592 | 29100 | 0.0277 | - |
| 3.9660 | 29150 | 0.031 | - |
| 3.9728 | 29200 | 0.0304 | - |
| 3.9796 | 29250 | 0.0332 | - |
| 3.9864 | 29300 | 0.0277 | - |
| 3.9932 | 29350 | 0.0233 | - |
| 4.0 | 29400 | 0.0237 | - |
### Framework Versions
- Python: 3.11.13
- SetFit: 1.1.2
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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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