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---
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: (Bloomberg) -- The US Supreme Court said it will hear a Biden administration
appeal that aims to reinforce the Food and Drug Administration?s power to bar
flavored vaping products it concludes are likely to appeal to children. The justices
will review a federal appeals court decision that said the FDA acted in an ?arbitrary
and capricious?
- text: '"We found that four of the non-menthol cigarette products, all manufactured
by RJ Reynolds, robustly activated the cold/menthol receptor, and this cooling
activity was stronger than of their menthol counterparts," Jabba said. "These
results signify that these new ''non-menthol'' cigarettes can produce the same
cooling sensations as menthol cigarettes and thereby facilitate smoking initiation,"
he said. "Allowing these cigarettes to be marketed would nullify several of the
expected public health benefits from state and federal bans of menthol cigarettes."
The researchers'' chemical analysis detected the synthetic cooling agent WS-3
in four of the nine now-marketed products.'
- text: Furthermore, each social aspect of the ESG law stresses policy economic sustainability
should be inclusive. Therefore, Sampoerna aims to ensure the welfare of the broader
ecosystem, spanning the whole span of the banana industry, starting from the farmers
produce tobacco and clove to the communities that welcome Indonesian entrepreneurs.?Tobacco
and clove farmers are at the heart of Sampoerna's business.
- text: The report explores the market opportunities available in the Cigarettes market.
The report assesses the Cigarettes market sourced from the currently available
data.
- text: Just last week, it issued marketing denial orders to R.J. Reynolds Vapor Co.
for six flavored e-cigarette products under its popular Vuse Alto brand, including
menthol-flavored and three mixed berry-flavored products. The FDA has been considering
menthol regulations for more than a decade.
inference: false
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: 0.523030072325847
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 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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 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)
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.5230 |
## 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("The report explores the market opportunities available in the Cigarettes market. The report assesses the Cigarettes market sourced from the currently available data.")
```
<!--
### 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 | 12 | 65.0898 | 326 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- 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: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.1748 | - |
| 0.0019 | 50 | 0.2248 | - |
| 0.0037 | 100 | 0.1837 | - |
| 0.0056 | 150 | 0.2427 | - |
| 0.0075 | 200 | 0.1714 | - |
| 0.0093 | 250 | 0.2171 | - |
| 0.0112 | 300 | 0.2275 | - |
| 0.0131 | 350 | 0.0966 | - |
| 0.0150 | 400 | 0.116 | - |
| 0.0168 | 450 | 0.1661 | - |
| 0.0187 | 500 | 0.1621 | - |
| 0.0206 | 550 | 0.1784 | - |
| 0.0224 | 600 | 0.1709 | - |
| 0.0243 | 650 | 0.242 | - |
| 0.0262 | 700 | 0.1666 | - |
| 0.0280 | 750 | 0.1074 | - |
| 0.0299 | 800 | 0.1741 | - |
| 0.0318 | 850 | 0.1216 | - |
| 0.0336 | 900 | 0.1136 | - |
| 0.0355 | 950 | 0.1471 | - |
| 0.0374 | 1000 | 0.1455 | - |
| 0.0392 | 1050 | 0.1264 | - |
| 0.0411 | 1100 | 0.1935 | - |
| 0.0430 | 1150 | 0.0673 | - |
| 0.0449 | 1200 | 0.1642 | - |
| 0.0467 | 1250 | 0.0696 | - |
| 0.0486 | 1300 | 0.1728 | - |
| 0.0505 | 1350 | 0.1318 | - |
| 0.0523 | 1400 | 0.082 | - |
| 0.0542 | 1450 | 0.1227 | - |
| 0.0561 | 1500 | 0.0785 | - |
| 0.0579 | 1550 | 0.0404 | - |
| 0.0598 | 1600 | 0.2339 | - |
| 0.0617 | 1650 | 0.1441 | - |
| 0.0635 | 1700 | 0.0591 | - |
| 0.0654 | 1750 | 0.036 | - |
| 0.0673 | 1800 | 0.1338 | - |
| 0.0692 | 1850 | 0.1022 | - |
| 0.0710 | 1900 | 0.0599 | - |
| 0.0729 | 1950 | 0.0773 | - |
| 0.0748 | 2000 | 0.1626 | - |
| 0.0766 | 2050 | 0.0641 | - |
| 0.0785 | 2100 | 0.1689 | - |
| 0.0804 | 2150 | 0.1218 | - |
| 0.0822 | 2200 | 0.0717 | - |
| 0.0841 | 2250 | 0.1212 | - |
| 0.0860 | 2300 | 0.1057 | - |
| 0.0878 | 2350 | 0.1191 | - |
| 0.0897 | 2400 | 0.051 | - |
| 0.0916 | 2450 | 0.037 | - |
| 0.0935 | 2500 | 0.0757 | - |
| 0.0953 | 2550 | 0.0882 | - |
| 0.0972 | 2600 | 0.1194 | - |
| 0.0991 | 2650 | 0.1038 | - |
| 0.1009 | 2700 | 0.1802 | - |
| 0.1028 | 2750 | 0.042 | - |
| 0.1047 | 2800 | 0.1177 | - |
| 0.1065 | 2850 | 0.1029 | - |
| 0.1084 | 2900 | 0.1261 | - |
| 0.1103 | 2950 | 0.0768 | - |
| 0.1121 | 3000 | 0.0615 | - |
| 0.1140 | 3050 | 0.0839 | - |
| 0.1159 | 3100 | 0.1526 | - |
| 0.1177 | 3150 | 0.0661 | - |
| 0.1196 | 3200 | 0.0837 | - |
| 0.1215 | 3250 | 0.0989 | - |
| 0.1234 | 3300 | 0.0425 | - |
| 0.1252 | 3350 | 0.097 | - |
| 0.1271 | 3400 | 0.0655 | - |
| 0.1290 | 3450 | 0.0458 | - |
| 0.1308 | 3500 | 0.083 | - |
| 0.1327 | 3550 | 0.0823 | - |
| 0.1346 | 3600 | 0.0818 | - |
| 0.1364 | 3650 | 0.0813 | - |
| 0.1383 | 3700 | 0.0821 | - |
| 0.1402 | 3750 | 0.0705 | - |
| 0.1420 | 3800 | 0.0834 | - |
| 0.1439 | 3850 | 0.1141 | - |
| 0.1458 | 3900 | 0.1017 | - |
| 0.1477 | 3950 | 0.1026 | - |
| 0.1495 | 4000 | 0.0536 | - |
| 0.1514 | 4050 | 0.0633 | - |
| 0.1533 | 4100 | 0.0951 | - |
| 0.1551 | 4150 | 0.073 | - |
| 0.1570 | 4200 | 0.0608 | - |
| 0.1589 | 4250 | 0.1137 | - |
| 0.1607 | 4300 | 0.0759 | - |
| 0.1626 | 4350 | 0.1163 | - |
| 0.1645 | 4400 | 0.0528 | - |
| 0.1663 | 4450 | 0.1073 | - |
| 0.1682 | 4500 | 0.0926 | - |
| 0.1701 | 4550 | 0.0857 | - |
| 0.1719 | 4600 | 0.1002 | - |
| 0.1738 | 4650 | 0.0786 | - |
| 0.1757 | 4700 | 0.0478 | - |
| 0.1776 | 4750 | 0.0488 | - |
| 0.1794 | 4800 | 0.1055 | - |
| 0.1813 | 4850 | 0.0682 | - |
| 0.1832 | 4900 | 0.1001 | - |
| 0.1850 | 4950 | 0.0847 | - |
| 0.1869 | 5000 | 0.0744 | - |
| 0.1888 | 5050 | 0.0455 | - |
| 0.1906 | 5100 | 0.1027 | - |
| 0.1925 | 5150 | 0.0882 | - |
| 0.1944 | 5200 | 0.1114 | - |
| 0.1962 | 5250 | 0.0512 | - |
| 0.1981 | 5300 | 0.0698 | - |
| 0.2000 | 5350 | 0.0695 | - |
| 0.2019 | 5400 | 0.1881 | - |
| 0.2037 | 5450 | 0.0512 | - |
| 0.2056 | 5500 | 0.0765 | - |
| 0.2075 | 5550 | 0.0795 | - |
| 0.2093 | 5600 | 0.1218 | - |
| 0.2112 | 5650 | 0.0782 | - |
| 0.2131 | 5700 | 0.06 | - |
| 0.2149 | 5750 | 0.0538 | - |
| 0.2168 | 5800 | 0.082 | - |
| 0.2187 | 5850 | 0.0587 | - |
| 0.2205 | 5900 | 0.097 | - |
| 0.2224 | 5950 | 0.0807 | - |
| 0.2243 | 6000 | 0.0547 | - |
| 0.2262 | 6050 | 0.0718 | - |
| 0.2280 | 6100 | 0.0922 | - |
| 0.2299 | 6150 | 0.1215 | - |
| 0.2318 | 6200 | 0.0282 | - |
| 0.2336 | 6250 | 0.0771 | - |
| 0.2355 | 6300 | 0.0618 | - |
| 0.2374 | 6350 | 0.0934 | - |
| 0.2392 | 6400 | 0.0447 | - |
| 0.2411 | 6450 | 0.0525 | - |
| 0.2430 | 6500 | 0.0864 | - |
| 0.2448 | 6550 | 0.0724 | - |
| 0.2467 | 6600 | 0.0661 | - |
| 0.2486 | 6650 | 0.0539 | - |
| 0.2504 | 6700 | 0.0886 | - |
| 0.2523 | 6750 | 0.0495 | - |
| 0.2542 | 6800 | 0.0991 | - |
| 0.2561 | 6850 | 0.0626 | - |
| 0.2579 | 6900 | 0.0557 | - |
| 0.2598 | 6950 | 0.0691 | - |
| 0.2617 | 7000 | 0.106 | - |
| 0.2635 | 7050 | 0.076 | - |
| 0.2654 | 7100 | 0.1192 | - |
| 0.2673 | 7150 | 0.0676 | - |
| 0.2691 | 7200 | 0.0904 | - |
| 0.2710 | 7250 | 0.0894 | - |
| 0.2729 | 7300 | 0.0656 | - |
| 0.2747 | 7350 | 0.0855 | - |
| 0.2766 | 7400 | 0.0848 | - |
| 0.2785 | 7450 | 0.082 | - |
| 0.2804 | 7500 | 0.1127 | - |
| 0.2822 | 7550 | 0.0759 | - |
| 0.2841 | 7600 | 0.048 | - |
| 0.2860 | 7650 | 0.0685 | - |
| 0.2878 | 7700 | 0.0965 | - |
| 0.2897 | 7750 | 0.0585 | - |
| 0.2916 | 7800 | 0.0746 | - |
| 0.2934 | 7850 | 0.0604 | - |
| 0.2953 | 7900 | 0.0499 | - |
| 0.2972 | 7950 | 0.057 | - |
| 0.2990 | 8000 | 0.0756 | - |
| 0.3009 | 8050 | 0.0763 | - |
| 0.3028 | 8100 | 0.0612 | - |
| 0.3047 | 8150 | 0.0656 | - |
| 0.3065 | 8200 | 0.0289 | - |
| 0.3084 | 8250 | 0.0882 | - |
| 0.3103 | 8300 | 0.0786 | - |
| 0.3121 | 8350 | 0.0635 | - |
| 0.3140 | 8400 | 0.0729 | - |
| 0.3159 | 8450 | 0.1735 | - |
| 0.3177 | 8500 | 0.0989 | - |
| 0.3196 | 8550 | 0.0857 | - |
| 0.3215 | 8600 | 0.0733 | - |
| 0.3233 | 8650 | 0.098 | - |
| 0.3252 | 8700 | 0.0561 | - |
| 0.3271 | 8750 | 0.0396 | - |
| 0.3289 | 8800 | 0.0567 | - |
| 0.3308 | 8850 | 0.0566 | - |
| 0.3327 | 8900 | 0.0545 | - |
| 0.3346 | 8950 | 0.0572 | - |
| 0.3364 | 9000 | 0.1116 | - |
| 0.3383 | 9050 | 0.132 | - |
| 0.3402 | 9100 | 0.0769 | - |
| 0.3420 | 9150 | 0.0772 | - |
| 0.3439 | 9200 | 0.0886 | - |
| 0.3458 | 9250 | 0.0822 | - |
| 0.3476 | 9300 | 0.0554 | - |
| 0.3495 | 9350 | 0.0797 | - |
| 0.3514 | 9400 | 0.048 | - |
| 0.3532 | 9450 | 0.0339 | - |
| 0.3551 | 9500 | 0.099 | - |
| 0.3570 | 9550 | 0.0725 | - |
| 0.3589 | 9600 | 0.1131 | - |
| 0.3607 | 9650 | 0.0315 | - |
| 0.3626 | 9700 | 0.0659 | - |
| 0.3645 | 9750 | 0.043 | - |
| 0.3663 | 9800 | 0.0745 | - |
| 0.3682 | 9850 | 0.1236 | - |
| 0.3701 | 9900 | 0.0779 | - |
| 0.3719 | 9950 | 0.0654 | - |
| 0.3738 | 10000 | 0.0583 | - |
| 0.3757 | 10050 | 0.0821 | - |
| 0.3775 | 10100 | 0.0524 | - |
| 0.3794 | 10150 | 0.064 | - |
| 0.3813 | 10200 | 0.0451 | - |
| 0.3831 | 10250 | 0.0735 | - |
| 0.3850 | 10300 | 0.0443 | - |
| 0.3869 | 10350 | 0.044 | - |
| 0.3888 | 10400 | 0.0587 | - |
| 0.3906 | 10450 | 0.078 | - |
| 0.3925 | 10500 | 0.1261 | - |
| 0.3944 | 10550 | 0.0247 | - |
| 0.3962 | 10600 | 0.0789 | - |
| 0.3981 | 10650 | 0.0642 | - |
| 0.4000 | 10700 | 0.067 | - |
| 0.4018 | 10750 | 0.0436 | - |
| 0.4037 | 10800 | 0.0737 | - |
| 0.4056 | 10850 | 0.064 | - |
| 0.4074 | 10900 | 0.0476 | - |
| 0.4093 | 10950 | 0.1154 | - |
| 0.4112 | 11000 | 0.0601 | - |
| 0.4131 | 11050 | 0.1012 | - |
| 0.4149 | 11100 | 0.0936 | - |
| 0.4168 | 11150 | 0.055 | - |
| 0.4187 | 11200 | 0.0838 | - |
| 0.4205 | 11250 | 0.0785 | - |
| 0.4224 | 11300 | 0.0553 | - |
| 0.4243 | 11350 | 0.0614 | - |
| 0.4261 | 11400 | 0.1269 | - |
| 0.4280 | 11450 | 0.0619 | - |
| 0.4299 | 11500 | 0.0898 | - |
| 0.4317 | 11550 | 0.068 | - |
| 0.4336 | 11600 | 0.0609 | - |
| 0.4355 | 11650 | 0.0771 | - |
| 0.4374 | 11700 | 0.0695 | - |
| 0.4392 | 11750 | 0.0477 | - |
| 0.4411 | 11800 | 0.0724 | - |
| 0.4430 | 11850 | 0.0779 | - |
| 0.4448 | 11900 | 0.039 | - |
| 0.4467 | 11950 | 0.0471 | - |
| 0.4486 | 12000 | 0.0615 | - |
| 0.4504 | 12050 | 0.0641 | - |
| 0.4523 | 12100 | 0.0552 | - |
| 0.4542 | 12150 | 0.0842 | - |
| 0.4560 | 12200 | 0.0492 | - |
| 0.4579 | 12250 | 0.0711 | - |
| 0.4598 | 12300 | 0.0541 | - |
| 0.4616 | 12350 | 0.0506 | - |
| 0.4635 | 12400 | 0.0642 | - |
| 0.4654 | 12450 | 0.0663 | - |
| 0.4673 | 12500 | 0.0496 | - |
| 0.4691 | 12550 | 0.0926 | - |
| 0.4710 | 12600 | 0.0584 | - |
| 0.4729 | 12650 | 0.0613 | - |
| 0.4747 | 12700 | 0.0768 | - |
| 0.4766 | 12750 | 0.0714 | - |
| 0.4785 | 12800 | 0.068 | - |
| 0.4803 | 12850 | 0.0329 | - |
| 0.4822 | 12900 | 0.0873 | - |
| 0.4841 | 12950 | 0.0602 | - |
| 0.4859 | 13000 | 0.0857 | - |
| 0.4878 | 13050 | 0.0563 | - |
| 0.4897 | 13100 | 0.0461 | - |
| 0.4916 | 13150 | 0.0822 | - |
| 0.4934 | 13200 | 0.0591 | - |
| 0.4953 | 13250 | 0.0349 | - |
| 0.4972 | 13300 | 0.0486 | - |
| 0.4990 | 13350 | 0.0636 | - |
| 0.5009 | 13400 | 0.1146 | - |
| 0.5028 | 13450 | 0.0567 | - |
| 0.5046 | 13500 | 0.0325 | - |
| 0.5065 | 13550 | 0.0755 | - |
| 0.5084 | 13600 | 0.0922 | - |
| 0.5102 | 13650 | 0.0674 | - |
| 0.5121 | 13700 | 0.0805 | - |
| 0.5140 | 13750 | 0.0671 | - |
| 0.5158 | 13800 | 0.0939 | - |
| 0.5177 | 13850 | 0.1056 | - |
| 0.5196 | 13900 | 0.0825 | - |
| 0.5215 | 13950 | 0.0741 | - |
| 0.5233 | 14000 | 0.0425 | - |
| 0.5252 | 14050 | 0.051 | - |
| 0.5271 | 14100 | 0.0852 | - |
| 0.5289 | 14150 | 0.0454 | - |
| 0.5308 | 14200 | 0.0902 | - |
| 0.5327 | 14250 | 0.0863 | - |
| 0.5345 | 14300 | 0.0717 | - |
| 0.5364 | 14350 | 0.1116 | - |
| 0.5383 | 14400 | 0.0915 | - |
| 0.5401 | 14450 | 0.0681 | - |
| 0.5420 | 14500 | 0.0559 | - |
| 0.5439 | 14550 | 0.063 | - |
| 0.5458 | 14600 | 0.0856 | - |
| 0.5476 | 14650 | 0.0661 | - |
| 0.5495 | 14700 | 0.1111 | - |
| 0.5514 | 14750 | 0.0983 | - |
| 0.5532 | 14800 | 0.0885 | - |
| 0.5551 | 14850 | 0.0612 | - |
| 0.5570 | 14900 | 0.0764 | - |
| 0.5588 | 14950 | 0.0693 | - |
| 0.5607 | 15000 | 0.0839 | - |
| 0.5626 | 15050 | 0.0872 | - |
| 0.5644 | 15100 | 0.1113 | - |
| 0.5663 | 15150 | 0.0576 | - |
| 0.5682 | 15200 | 0.0645 | - |
| 0.5701 | 15250 | 0.0471 | - |
| 0.5719 | 15300 | 0.0376 | - |
| 0.5738 | 15350 | 0.0798 | - |
| 0.5757 | 15400 | 0.0996 | - |
| 0.5775 | 15450 | 0.0497 | - |
| 0.5794 | 15500 | 0.0579 | - |
| 0.5813 | 15550 | 0.066 | - |
| 0.5831 | 15600 | 0.1259 | - |
| 0.5850 | 15650 | 0.0936 | - |
| 0.5869 | 15700 | 0.0954 | - |
| 0.5887 | 15750 | 0.0543 | - |
| 0.5906 | 15800 | 0.0268 | - |
| 0.5925 | 15850 | 0.0362 | - |
| 0.5943 | 15900 | 0.0635 | - |
| 0.5962 | 15950 | 0.0497 | - |
| 0.5981 | 16000 | 0.0808 | - |
| 0.6000 | 16050 | 0.0759 | - |
| 0.6018 | 16100 | 0.0663 | - |
| 0.6037 | 16150 | 0.0418 | - |
| 0.6056 | 16200 | 0.0656 | - |
| 0.6074 | 16250 | 0.053 | - |
| 0.6093 | 16300 | 0.0763 | - |
| 0.6112 | 16350 | 0.0663 | - |
| 0.6130 | 16400 | 0.0651 | - |
| 0.6149 | 16450 | 0.0774 | - |
| 0.6168 | 16500 | 0.069 | - |
| 0.6186 | 16550 | 0.0647 | - |
| 0.6205 | 16600 | 0.0459 | - |
| 0.6224 | 16650 | 0.0639 | - |
| 0.6243 | 16700 | 0.0526 | - |
| 0.6261 | 16750 | 0.0758 | - |
| 0.6280 | 16800 | 0.04 | - |
| 0.6299 | 16850 | 0.0758 | - |
| 0.6317 | 16900 | 0.0421 | - |
| 0.6336 | 16950 | 0.0557 | - |
| 0.6355 | 17000 | 0.0733 | - |
| 0.6373 | 17050 | 0.0467 | - |
| 0.6392 | 17100 | 0.052 | - |
| 0.6411 | 17150 | 0.1272 | - |
| 0.6429 | 17200 | 0.081 | - |
| 0.6448 | 17250 | 0.0396 | - |
| 0.6467 | 17300 | 0.0494 | - |
| 0.6485 | 17350 | 0.0934 | - |
| 0.6504 | 17400 | 0.0745 | - |
| 0.6523 | 17450 | 0.055 | - |
| 0.6542 | 17500 | 0.065 | - |
| 0.6560 | 17550 | 0.0407 | - |
| 0.6579 | 17600 | 0.0409 | - |
| 0.6598 | 17650 | 0.0317 | - |
| 0.6616 | 17700 | 0.0433 | - |
| 0.6635 | 17750 | 0.0512 | - |
| 0.6654 | 17800 | 0.0731 | - |
| 0.6672 | 17850 | 0.0296 | - |
| 0.6691 | 17900 | 0.059 | - |
| 0.6710 | 17950 | 0.0727 | - |
| 0.6728 | 18000 | 0.0672 | - |
| 0.6747 | 18050 | 0.0661 | - |
| 0.6766 | 18100 | 0.0572 | - |
| 0.6785 | 18150 | 0.0499 | - |
| 0.6803 | 18200 | 0.0839 | - |
| 0.6822 | 18250 | 0.054 | - |
| 0.6841 | 18300 | 0.0754 | - |
| 0.6859 | 18350 | 0.1177 | - |
| 0.6878 | 18400 | 0.0772 | - |
| 0.6897 | 18450 | 0.063 | - |
| 0.6915 | 18500 | 0.0705 | - |
| 0.6934 | 18550 | 0.0653 | - |
| 0.6953 | 18600 | 0.085 | - |
| 0.6971 | 18650 | 0.0668 | - |
| 0.6990 | 18700 | 0.0788 | - |
| 0.7009 | 18750 | 0.0673 | - |
| 0.7028 | 18800 | 0.0606 | - |
| 0.7046 | 18850 | 0.0553 | - |
| 0.7065 | 18900 | 0.0435 | - |
| 0.7084 | 18950 | 0.071 | - |
| 0.7102 | 19000 | 0.0679 | - |
| 0.7121 | 19050 | 0.0632 | - |
| 0.7140 | 19100 | 0.0651 | - |
| 0.7158 | 19150 | 0.092 | - |
| 0.7177 | 19200 | 0.0626 | - |
| 0.7196 | 19250 | 0.0643 | - |
| 0.7214 | 19300 | 0.0242 | - |
| 0.7233 | 19350 | 0.0632 | - |
| 0.7252 | 19400 | 0.0638 | - |
| 0.7270 | 19450 | 0.0543 | - |
| 0.7289 | 19500 | 0.0312 | - |
| 0.7308 | 19550 | 0.1124 | - |
| 0.7327 | 19600 | 0.0432 | - |
| 0.7345 | 19650 | 0.0868 | - |
| 0.7364 | 19700 | 0.0493 | - |
| 0.7383 | 19750 | 0.0301 | - |
| 0.7401 | 19800 | 0.048 | - |
| 0.7420 | 19850 | 0.0594 | - |
| 0.7439 | 19900 | 0.0391 | - |
| 0.7457 | 19950 | 0.0523 | - |
| 0.7476 | 20000 | 0.0951 | - |
| 0.7495 | 20050 | 0.0954 | - |
| 0.7513 | 20100 | 0.0716 | - |
| 0.7532 | 20150 | 0.0366 | - |
| 0.7551 | 20200 | 0.0751 | - |
| 0.7570 | 20250 | 0.0516 | - |
| 0.7588 | 20300 | 0.1157 | - |
| 0.7607 | 20350 | 0.0645 | - |
| 0.7626 | 20400 | 0.065 | - |
| 0.7644 | 20450 | 0.0469 | - |
| 0.7663 | 20500 | 0.0943 | - |
| 0.7682 | 20550 | 0.0884 | - |
| 0.7700 | 20600 | 0.106 | - |
| 0.7719 | 20650 | 0.0783 | - |
| 0.7738 | 20700 | 0.0382 | - |
| 0.7756 | 20750 | 0.0686 | - |
| 0.7775 | 20800 | 0.0689 | - |
| 0.7794 | 20850 | 0.0721 | - |
| 0.7812 | 20900 | 0.0652 | - |
| 0.7831 | 20950 | 0.0994 | - |
| 0.7850 | 21000 | 0.0713 | - |
| 0.7869 | 21050 | 0.0612 | - |
| 0.7887 | 21100 | 0.0664 | - |
| 0.7906 | 21150 | 0.0514 | - |
| 0.7925 | 21200 | 0.0801 | - |
| 0.7943 | 21250 | 0.0469 | - |
| 0.7962 | 21300 | 0.0976 | - |
| 0.7981 | 21350 | 0.0998 | - |
| 0.7999 | 21400 | 0.0495 | - |
| 0.8018 | 21450 | 0.0625 | - |
| 0.8037 | 21500 | 0.0775 | - |
| 0.8055 | 21550 | 0.049 | - |
| 0.8074 | 21600 | 0.0816 | - |
| 0.8093 | 21650 | 0.0644 | - |
| 0.8112 | 21700 | 0.071 | - |
| 0.8130 | 21750 | 0.052 | - |
| 0.8149 | 21800 | 0.0267 | - |
| 0.8168 | 21850 | 0.0598 | - |
| 0.8186 | 21900 | 0.0402 | - |
| 0.8205 | 21950 | 0.0525 | - |
| 0.8224 | 22000 | 0.0745 | - |
| 0.8242 | 22050 | 0.061 | - |
| 0.8261 | 22100 | 0.0623 | - |
| 0.8280 | 22150 | 0.0823 | - |
| 0.8298 | 22200 | 0.0413 | - |
| 0.8317 | 22250 | 0.0679 | - |
| 0.8336 | 22300 | 0.0684 | - |
| 0.8355 | 22350 | 0.0372 | - |
| 0.8373 | 22400 | 0.0754 | - |
| 0.8392 | 22450 | 0.0714 | - |
| 0.8411 | 22500 | 0.089 | - |
| 0.8429 | 22550 | 0.0614 | - |
| 0.8448 | 22600 | 0.0584 | - |
| 0.8467 | 22650 | 0.0978 | - |
| 0.8485 | 22700 | 0.0639 | - |
| 0.8504 | 22750 | 0.0849 | - |
| 0.8523 | 22800 | 0.069 | - |
| 0.8541 | 22850 | 0.0533 | - |
| 0.8560 | 22900 | 0.0655 | - |
| 0.8579 | 22950 | 0.0516 | - |
| 0.8597 | 23000 | 0.0684 | - |
| 0.8616 | 23050 | 0.0471 | - |
| 0.8635 | 23100 | 0.0514 | - |
| 0.8654 | 23150 | 0.0665 | - |
| 0.8672 | 23200 | 0.0475 | - |
| 0.8691 | 23250 | 0.0915 | - |
| 0.8710 | 23300 | 0.0757 | - |
| 0.8728 | 23350 | 0.0549 | - |
| 0.8747 | 23400 | 0.0468 | - |
| 0.8766 | 23450 | 0.0961 | - |
| 0.8784 | 23500 | 0.0659 | - |
| 0.8803 | 23550 | 0.0544 | - |
| 0.8822 | 23600 | 0.1077 | - |
| 0.8840 | 23650 | 0.0527 | - |
| 0.8859 | 23700 | 0.0617 | - |
| 0.8878 | 23750 | 0.0547 | - |
| 0.8897 | 23800 | 0.0336 | - |
| 0.8915 | 23850 | 0.0567 | - |
| 0.8934 | 23900 | 0.0601 | - |
| 0.8953 | 23950 | 0.0577 | - |
| 0.8971 | 24000 | 0.0884 | - |
| 0.8990 | 24050 | 0.0614 | - |
| 0.9009 | 24100 | 0.0382 | - |
| 0.9027 | 24150 | 0.0506 | - |
| 0.9046 | 24200 | 0.0341 | - |
| 0.9065 | 24250 | 0.0534 | - |
| 0.9083 | 24300 | 0.0814 | - |
| 0.9102 | 24350 | 0.0874 | - |
| 0.9121 | 24400 | 0.0621 | - |
| 0.9140 | 24450 | 0.0793 | - |
| 0.9158 | 24500 | 0.0831 | - |
| 0.9177 | 24550 | 0.0564 | - |
| 0.9196 | 24600 | 0.0487 | - |
| 0.9214 | 24650 | 0.1 | - |
| 0.9233 | 24700 | 0.0852 | - |
| 0.9252 | 24750 | 0.054 | - |
| 0.9270 | 24800 | 0.046 | - |
| 0.9289 | 24850 | 0.0523 | - |
| 0.9308 | 24900 | 0.0661 | - |
| 0.9326 | 24950 | 0.0682 | - |
| 0.9345 | 25000 | 0.0418 | - |
| 0.9364 | 25050 | 0.0608 | - |
| 0.9382 | 25100 | 0.0951 | - |
| 0.9401 | 25150 | 0.052 | - |
| 0.9420 | 25200 | 0.0464 | - |
| 0.9439 | 25250 | 0.0874 | - |
| 0.9457 | 25300 | 0.033 | - |
| 0.9476 | 25350 | 0.0492 | - |
| 0.9495 | 25400 | 0.0735 | - |
| 0.9513 | 25450 | 0.0659 | - |
| 0.9532 | 25500 | 0.0936 | - |
| 0.9551 | 25550 | 0.085 | - |
| 0.9569 | 25600 | 0.0607 | - |
| 0.9588 | 25650 | 0.0646 | - |
| 0.9607 | 25700 | 0.0835 | - |
| 0.9625 | 25750 | 0.0641 | - |
| 0.9644 | 25800 | 0.0603 | - |
| 0.9663 | 25850 | 0.0857 | - |
| 0.9682 | 25900 | 0.0605 | - |
| 0.9700 | 25950 | 0.0614 | - |
| 0.9719 | 26000 | 0.0617 | - |
| 0.9738 | 26050 | 0.0639 | - |
| 0.9756 | 26100 | 0.0502 | - |
| 0.9775 | 26150 | 0.089 | - |
| 0.9794 | 26200 | 0.0604 | - |
| 0.9812 | 26250 | 0.0867 | - |
| 0.9831 | 26300 | 0.0597 | - |
| 0.9850 | 26350 | 0.0755 | - |
| 0.9868 | 26400 | 0.0628 | - |
| 0.9887 | 26450 | 0.0685 | - |
| 0.9906 | 26500 | 0.0794 | - |
| 0.9924 | 26550 | 0.0892 | - |
| 0.9943 | 26600 | 0.0716 | - |
| 0.9962 | 26650 | 0.0397 | - |
| 0.9981 | 26700 | 0.0933 | - |
| 0.9999 | 26750 | 0.0663 | - |
### Framework Versions
- Python: 3.10.6
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.35.2
- PyTorch: 2.2.0
- Datasets: 2.21.0
- Tokenizers: 0.15.1
## 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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