---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- metric
widget:
- text: Damn, my condolences to you bro
- text: No Friday Im booked all day
- text: Im sorry.
- text: Hiding in the bush
- text: '*"The conservative party is a cult." Says the group that bans words and follows
socialism.??*'
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
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: metric
value: 0.6947118450822154
name: Metric
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 8 classes
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1 | - '@Josh Collins "Ben 0" lmao don\'t forget the facts, Ben has more wins than that'
- 'poop siht are the fake news'
- 'Thank god these fire chiefs are being heard. People have no idea that they have been trying to meet up with the Prime Minister even before this bushfire crisis trying to alert the public of the devastating impacts of climate change.'
|
| 3 | - 'Perfectly nailed by Ms.Zainab Sikander. Proud !'
- "You're so sincere Dia about people's life."
- 'No words to express my gratitude to this hero.'
|
| 6 | - 'I accept that.'
- '@Viji same here'
- 'Facing same problem'
|
| 5 | - "@Rhynni Yeah thanks for asking, Your profile picture actually caught my eyes, Where are you from if you wouldn't mind me asking?"
- 'For what what did they do?'
- 'Aditya Jagtap who?'
|
| 2 | - 'Or the save the world were gonna die people .......... No !!! the police joined in'
- 'No, I don\'t think I am missing the point at all. When they say "40% of people are obese" that\'s based on BMI, which is an inherently flawed measure by almost any standards. When you say "obesity is estimated to cost whatever," there\'s a lots of conflation of correlation and causation in that calculation. Diseases often correlated with obesity are not always caused by obesity. Either way, my point still stands. Weight should not be considered independently from all other measures of health, it\'s important to consider all the factors.'
- "This is a scam under the guise of socialist action. Climate change is caused mainly by geothermal activity, hence can't be stopped."
|
| 4 | - 'https://www.gov.uk/guidance/high-consequence-infectious-diseases-hcid#status-of-covid-19 Please somebody explain this to me. It makes absolutely no sense.'
- "Look, you're obviously interested in this, so why don't you go an get a degree in climate science? Im sure the OU do one."
- 'All airports need to be stopped'
|
| 0 | - 'Oh ... Following the same drama.'
- '1st'
- 'Breaking news: England just left the EU!'
|
| 7 | - 'Oh no, I did not mean it that way, it was completely misunderstood what I was saying. Didnt mean to offend you, sorry!'
- 'Sorry, really.'
- "It's my fault, I shouldn't have done that, sorryyy!"
|
## Evaluation
### Metrics
| Label | Metric |
|:--------|:-------|
| **all** | 0.6947 |
## 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("CrisisNarratives/setfit-8classes-single_label")
# Run inference
preds = model("Im sorry.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:-----|
| Word count | 1 | 25.3789 | 1681 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 156 |
| 1 | 145 |
| 2 | 52 |
| 3 | 46 |
| 4 | 63 |
| 5 | 35 |
| 6 | 37 |
| 7 | 7 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 40
- body_learning_rate: (1.752e-05, 1.752e-05)
- head_learning_rate: 1.752e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 30
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0004 | 1 | 0.4094 | - |
| 0.0185 | 50 | 0.3207 | - |
| 0.0370 | 100 | 0.2635 | - |
| 0.0555 | 150 | 0.2347 | - |
| 0.0739 | 200 | 0.2686 | - |
| 0.0924 | 250 | 0.2575 | - |
| 0.1109 | 300 | 0.1983 | - |
| 0.1294 | 350 | 0.2387 | - |
| 0.1479 | 400 | 0.2002 | - |
| 0.1664 | 450 | 0.2112 | - |
| 0.1848 | 500 | 0.0913 | - |
| 0.2033 | 550 | 0.1715 | - |
| 0.2218 | 600 | 0.0686 | - |
| 0.2403 | 650 | 0.0166 | - |
| 0.2588 | 700 | 0.0128 | - |
| 0.2773 | 750 | 0.0102 | - |
| 0.2957 | 800 | 0.0071 | - |
| 0.3142 | 850 | 0.0012 | - |
| 0.3327 | 900 | 0.0016 | - |
| 0.3512 | 950 | 0.0035 | - |
| 0.3697 | 1000 | 0.0012 | - |
| 0.3882 | 1050 | 0.0003 | - |
| 0.4067 | 1100 | 0.001 | - |
| 0.4251 | 1150 | 0.0025 | - |
| 0.4436 | 1200 | 0.001 | - |
| 0.4621 | 1250 | 0.0006 | - |
| 0.4806 | 1300 | 0.0006 | - |
| 0.4991 | 1350 | 0.0004 | - |
| 0.5176 | 1400 | 0.0012 | - |
| 0.5360 | 1450 | 0.0051 | - |
| 0.5545 | 1500 | 0.0009 | - |
| 0.5730 | 1550 | 0.0003 | - |
| 0.5915 | 1600 | 0.0004 | - |
| 0.6100 | 1650 | 0.0009 | - |
| 0.6285 | 1700 | 0.0002 | - |
| 0.6470 | 1750 | 0.0003 | - |
| 0.6654 | 1800 | 0.0005 | - |
| 0.6839 | 1850 | 0.0003 | - |
| 0.7024 | 1900 | 0.0003 | - |
| 0.7209 | 1950 | 0.0005 | - |
| 0.7394 | 2000 | 0.0004 | - |
| 0.7579 | 2050 | 0.0008 | - |
| 0.7763 | 2100 | 0.0009 | - |
| 0.7948 | 2150 | 0.0002 | - |
| 0.8133 | 2200 | 0.0002 | - |
| 0.8318 | 2250 | 0.0002 | - |
| 0.8503 | 2300 | 0.0008 | - |
| 0.8688 | 2350 | 0.0002 | - |
| 0.8872 | 2400 | 0.0002 | - |
| 0.9057 | 2450 | 0.0003 | - |
| 0.9242 | 2500 | 0.0013 | - |
| 0.9427 | 2550 | 0.0003 | - |
| 0.9612 | 2600 | 0.0002 | - |
| 0.9797 | 2650 | 0.0002 | - |
| 0.9982 | 2700 | 0.0003 | - |
| 1.0166 | 2750 | 0.0002 | - |
| 1.0351 | 2800 | 0.0008 | - |
| 1.0536 | 2850 | 0.0001 | - |
| 1.0721 | 2900 | 0.0004 | - |
| 1.0906 | 2950 | 0.0001 | - |
| 1.1091 | 3000 | 0.0001 | - |
| 1.1275 | 3050 | 0.0002 | - |
| 1.1460 | 3100 | 0.0002 | - |
| 1.1645 | 3150 | 0.0002 | - |
| 1.1830 | 3200 | 0.0001 | - |
| 1.2015 | 3250 | 0.0001 | - |
| 1.2200 | 3300 | 0.0001 | - |
| 1.2384 | 3350 | 0.0041 | - |
| 1.2569 | 3400 | 0.0002 | - |
| 1.2754 | 3450 | 0.0001 | - |
| 1.2939 | 3500 | 0.0001 | - |
| 1.3124 | 3550 | 0.0002 | - |
| 1.3309 | 3600 | 0.0 | - |
| 1.3494 | 3650 | 0.0001 | - |
| 1.3678 | 3700 | 0.0001 | - |
| 1.3863 | 3750 | 0.0002 | - |
| 1.4048 | 3800 | 0.0001 | - |
| 1.4233 | 3850 | 0.0 | - |
| 1.4418 | 3900 | 0.0001 | - |
| 1.4603 | 3950 | 0.0001 | - |
| 1.4787 | 4000 | 0.0001 | - |
| 1.4972 | 4050 | 0.0001 | - |
| 1.5157 | 4100 | 0.0001 | - |
| 1.5342 | 4150 | 0.0001 | - |
| 1.5527 | 4200 | 0.0001 | - |
| 1.5712 | 4250 | 0.0001 | - |
| 1.5896 | 4300 | 0.0001 | - |
| 1.6081 | 4350 | 0.0 | - |
| 1.6266 | 4400 | 0.0001 | - |
| 1.6451 | 4450 | 0.0019 | - |
| 1.6636 | 4500 | 0.0001 | - |
| 1.6821 | 4550 | 0.0003 | - |
| 1.7006 | 4600 | 0.0002 | - |
| 1.7190 | 4650 | 0.0001 | - |
| 1.7375 | 4700 | 0.0001 | - |
| 1.7560 | 4750 | 0.0002 | - |
| 1.7745 | 4800 | 0.0001 | - |
| 1.7930 | 4850 | 0.0001 | - |
| 1.8115 | 4900 | 0.0003 | - |
| 1.8299 | 4950 | 0.056 | - |
| 1.8484 | 5000 | 0.0001 | - |
| 1.8669 | 5050 | 0.0001 | - |
| 1.8854 | 5100 | 0.0001 | - |
| 1.9039 | 5150 | 0.0001 | - |
| 1.9224 | 5200 | 0.0 | - |
| 1.9409 | 5250 | 0.0001 | - |
| 1.9593 | 5300 | 0.0001 | - |
| 1.9778 | 5350 | 0.0001 | - |
| 1.9963 | 5400 | 0.0002 | - |
| 2.0148 | 5450 | 0.0 | - |
| 2.0333 | 5500 | 0.0001 | - |
| 2.0518 | 5550 | 0.0 | - |
| 2.0702 | 5600 | 0.0004 | - |
| 2.0887 | 5650 | 0.0001 | - |
| 2.1072 | 5700 | 0.0001 | - |
| 2.1257 | 5750 | 0.0001 | - |
| 2.1442 | 5800 | 0.0001 | - |
| 2.1627 | 5850 | 0.0001 | - |
| 2.1811 | 5900 | 0.0 | - |
| 2.1996 | 5950 | 0.0001 | - |
| 2.2181 | 6000 | 0.0001 | - |
| 2.2366 | 6050 | 0.0001 | - |
| 2.2551 | 6100 | 0.0001 | - |
| 2.2736 | 6150 | 0.0001 | - |
| 2.2921 | 6200 | 0.0 | - |
| 2.3105 | 6250 | 0.0001 | - |
| 2.3290 | 6300 | 0.0 | - |
| 2.3475 | 6350 | 0.0001 | - |
| 2.3660 | 6400 | 0.0001 | - |
| 2.3845 | 6450 | 0.0001 | - |
| 2.4030 | 6500 | 0.0 | - |
| 2.4214 | 6550 | 0.0001 | - |
| 2.4399 | 6600 | 0.0001 | - |
| 2.4584 | 6650 | 0.0 | - |
| 2.4769 | 6700 | 0.0 | - |
| 2.4954 | 6750 | 0.0002 | - |
| 2.5139 | 6800 | 0.0001 | - |
| 2.5323 | 6850 | 0.0001 | - |
| 2.5508 | 6900 | 0.0001 | - |
| 2.5693 | 6950 | 0.0001 | - |
| 2.5878 | 7000 | 0.0 | - |
| 2.6063 | 7050 | 0.0001 | - |
| 2.6248 | 7100 | 0.0001 | - |
| 2.6433 | 7150 | 0.0001 | - |
| 2.6617 | 7200 | 0.0001 | - |
| 2.6802 | 7250 | 0.0001 | - |
| 2.6987 | 7300 | 0.0003 | - |
| 2.7172 | 7350 | 0.0001 | - |
| 2.7357 | 7400 | 0.0 | - |
| 2.7542 | 7450 | 0.0 | - |
| 2.7726 | 7500 | 0.0 | - |
| 2.7911 | 7550 | 0.0001 | - |
| 2.8096 | 7600 | 0.0001 | - |
| 2.8281 | 7650 | 0.0001 | - |
| 2.8466 | 7700 | 0.0001 | - |
| 2.8651 | 7750 | 0.0001 | - |
| 2.8835 | 7800 | 0.0001 | - |
| 2.9020 | 7850 | 0.0001 | - |
| 2.9205 | 7900 | 0.0002 | - |
| 2.9390 | 7950 | 0.0001 | - |
| 2.9575 | 8000 | 0.0 | - |
| 2.9760 | 8050 | 0.0 | - |
| 2.9945 | 8100 | 0.0001 | - |
| 0.0004 | 1 | 0.0001 | - |
| 0.0185 | 50 | 0.0001 | - |
| 0.0370 | 100 | 0.0001 | - |
| 0.0555 | 150 | 0.0001 | - |
| 0.0739 | 200 | 0.0001 | - |
| 0.0924 | 250 | 0.0001 | - |
| 0.1109 | 300 | 0.0001 | - |
| 0.1294 | 350 | 0.0001 | - |
| 0.1479 | 400 | 0.0001 | - |
| 0.1664 | 450 | 0.0005 | - |
| 0.1848 | 500 | 0.0007 | - |
| 0.2033 | 550 | 0.0003 | - |
| 0.2218 | 600 | 0.0003 | - |
| 0.2403 | 650 | 0.0 | - |
| 0.2588 | 700 | 0.0001 | - |
| 0.2773 | 750 | 0.0001 | - |
| 0.2957 | 800 | 0.0002 | - |
| 0.3142 | 850 | 0.0 | - |
| 0.3327 | 900 | 0.0001 | - |
| 0.3512 | 950 | 0.0044 | - |
| 0.3697 | 1000 | 0.0001 | - |
| 0.3882 | 1050 | 0.0004 | - |
| 0.4067 | 1100 | 0.0006 | - |
| 0.4251 | 1150 | 0.0012 | - |
| 0.4436 | 1200 | 0.0002 | - |
| 0.4621 | 1250 | 0.0001 | - |
| 0.4806 | 1300 | 0.0 | - |
| 0.4991 | 1350 | 0.0001 | - |
| 0.5176 | 1400 | 0.0003 | - |
| 0.5360 | 1450 | 0.0001 | - |
| 0.5545 | 1500 | 0.0001 | - |
| 0.5730 | 1550 | 0.0002 | - |
| 0.5915 | 1600 | 0.0001 | - |
| 0.6100 | 1650 | 0.0002 | - |
| 0.6285 | 1700 | 0.0 | - |
| 0.6470 | 1750 | 0.0001 | - |
| 0.6654 | 1800 | 0.0001 | - |
| 0.6839 | 1850 | 0.0001 | - |
| 0.7024 | 1900 | 0.0001 | - |
| 0.7209 | 1950 | 0.0017 | - |
| 0.7394 | 2000 | 0.0001 | - |
| 0.7579 | 2050 | 0.0002 | - |
| 0.7763 | 2100 | 0.0002 | - |
| 0.7948 | 2150 | 0.0003 | - |
| 0.8133 | 2200 | 0.0001 | - |
| 0.8318 | 2250 | 0.0001 | - |
| 0.8503 | 2300 | 0.0002 | - |
| 0.8688 | 2350 | 0.0 | - |
| 0.8872 | 2400 | 0.0001 | - |
| 0.9057 | 2450 | 0.0001 | - |
| 0.9242 | 2500 | 0.0002 | - |
| 0.9427 | 2550 | 0.0001 | - |
| 0.9612 | 2600 | 0.0 | - |
| 0.9797 | 2650 | 0.0 | - |
| 0.9982 | 2700 | 0.0001 | - |
| 1.0166 | 2750 | 0.0001 | - |
| 1.0351 | 2800 | 0.0001 | - |
| 1.0536 | 2850 | 0.0 | - |
| 1.0721 | 2900 | 0.0 | - |
| 1.0906 | 2950 | 0.0001 | - |
| 1.1091 | 3000 | 0.0 | - |
| 1.1275 | 3050 | 0.0001 | - |
| 1.1460 | 3100 | 0.0001 | - |
| 1.1645 | 3150 | 0.0 | - |
| 1.1830 | 3200 | 0.0 | - |
| 1.2015 | 3250 | 0.0 | - |
| 1.2200 | 3300 | 0.0 | - |
| 1.2384 | 3350 | 0.0002 | - |
| 1.2569 | 3400 | 0.0001 | - |
| 1.2754 | 3450 | 0.0 | - |
| 1.2939 | 3500 | 0.0001 | - |
| 1.3124 | 3550 | 0.0001 | - |
| 1.3309 | 3600 | 0.0 | - |
| 1.3494 | 3650 | 0.0 | - |
| 1.3678 | 3700 | 0.0 | - |
| 1.3863 | 3750 | 0.0001 | - |
| 1.4048 | 3800 | 0.0 | - |
| 1.4233 | 3850 | 0.0 | - |
| 1.4418 | 3900 | 0.0 | - |
| 1.4603 | 3950 | 0.0 | - |
| 1.4787 | 4000 | 0.0001 | - |
| 1.4972 | 4050 | 0.0 | - |
| 1.5157 | 4100 | 0.0 | - |
| 1.5342 | 4150 | 0.0 | - |
| 1.5527 | 4200 | 0.0001 | - |
| 1.5712 | 4250 | 0.0001 | - |
| 1.5896 | 4300 | 0.0 | - |
| 1.6081 | 4350 | 0.0 | - |
| 1.6266 | 4400 | 0.0001 | - |
| 1.6451 | 4450 | 0.0 | - |
| 1.6636 | 4500 | 0.0001 | - |
| 1.6821 | 4550 | 0.0001 | - |
| 1.7006 | 4600 | 0.0001 | - |
| 1.7190 | 4650 | 0.0 | - |
| 1.7375 | 4700 | 0.0 | - |
| 1.7560 | 4750 | 0.0 | - |
| 1.7745 | 4800 | 0.0 | - |
| 1.7930 | 4850 | 0.0001 | - |
| 1.8115 | 4900 | 0.0001 | - |
| 1.8299 | 4950 | 0.0 | - |
| 1.8484 | 5000 | 0.0001 | - |
| 1.8669 | 5050 | 0.0 | - |
| 1.8854 | 5100 | 0.0 | - |
| 1.9039 | 5150 | 0.0 | - |
| 1.9224 | 5200 | 0.0 | - |
| 1.9409 | 5250 | 0.0 | - |
| 1.9593 | 5300 | 0.0 | - |
| 1.9778 | 5350 | 0.0 | - |
| 1.9963 | 5400 | 0.0 | - |
| 2.0148 | 5450 | 0.0 | - |
| 2.0333 | 5500 | 0.0 | - |
| 2.0518 | 5550 | 0.0 | - |
| 2.0702 | 5600 | 0.0001 | - |
| 2.0887 | 5650 | 0.0 | - |
| 2.1072 | 5700 | 0.0 | - |
| 2.1257 | 5750 | 0.0 | - |
| 2.1442 | 5800 | 0.0 | - |
| 2.1627 | 5850 | 0.0001 | - |
| 2.1811 | 5900 | 0.0 | - |
| 2.1996 | 5950 | 0.0 | - |
| 2.2181 | 6000 | 0.0 | - |
| 2.2366 | 6050 | 0.0 | - |
| 2.2551 | 6100 | 0.0 | - |
| 2.2736 | 6150 | 0.0001 | - |
| 2.2921 | 6200 | 0.0 | - |
| 2.3105 | 6250 | 0.0 | - |
| 2.3290 | 6300 | 0.0 | - |
| 2.3475 | 6350 | 0.0 | - |
| 2.3660 | 6400 | 0.0 | - |
| 2.3845 | 6450 | 0.0 | - |
| 2.4030 | 6500 | 0.0 | - |
| 2.4214 | 6550 | 0.0 | - |
| 2.4399 | 6600 | 0.0 | - |
| 2.4584 | 6650 | 0.0 | - |
| 2.4769 | 6700 | 0.0 | - |
| 2.4954 | 6750 | 0.0001 | - |
| 2.5139 | 6800 | 0.0001 | - |
| 2.5323 | 6850 | 0.0 | - |
| 2.5508 | 6900 | 0.0 | - |
| 2.5693 | 6950 | 0.0 | - |
| 2.5878 | 7000 | 0.0 | - |
| 2.6063 | 7050 | 0.0 | - |
| 2.6248 | 7100 | 0.0 | - |
| 2.6433 | 7150 | 0.0001 | - |
| 2.6617 | 7200 | 0.0 | - |
| 2.6802 | 7250 | 0.0 | - |
| 2.6987 | 7300 | 0.0001 | - |
| 2.7172 | 7350 | 0.0 | - |
| 2.7357 | 7400 | 0.0 | - |
| 2.7542 | 7450 | 0.0 | - |
| 2.7726 | 7500 | 0.0 | - |
| 2.7911 | 7550 | 0.0 | - |
| 2.8096 | 7600 | 0.0 | - |
| 2.8281 | 7650 | 0.0 | - |
| 2.8466 | 7700 | 0.0001 | - |
| 2.8651 | 7750 | 0.0 | - |
| 2.8835 | 7800 | 0.0001 | - |
| 2.9020 | 7850 | 0.0 | - |
| 2.9205 | 7900 | 0.0001 | - |
| 2.9390 | 7950 | 0.0001 | - |
| 2.9575 | 8000 | 0.0 | - |
| 2.9760 | 8050 | 0.0 | - |
| 2.9945 | 8100 | 0.0 | - |
| 0.0004 | 1 | 0.0 | - |
| 0.0185 | 50 | 0.0 | - |
| 0.0370 | 100 | 0.0 | - |
| 0.0555 | 150 | 0.0 | - |
| 0.0739 | 200 | 0.0 | - |
| 0.0924 | 250 | 0.0 | - |
| 0.1109 | 300 | 0.0 | - |
| 0.1294 | 350 | 0.0005 | - |
| 0.1479 | 400 | 0.0002 | - |
| 0.1664 | 450 | 0.0001 | - |
| 0.1848 | 500 | 0.0009 | - |
| 0.2033 | 550 | 0.1068 | - |
| 0.2218 | 600 | 0.0 | - |
| 0.2403 | 650 | 0.0 | - |
| 0.2588 | 700 | 0.0 | - |
| 0.2773 | 750 | 0.0374 | - |
| 0.2957 | 800 | 0.0001 | - |
| 0.3142 | 850 | 0.0 | - |
| 0.3327 | 900 | 0.0 | - |
| 0.3512 | 950 | 0.0 | - |
| 0.3697 | 1000 | 0.0001 | - |
| 0.3882 | 1050 | 0.0 | - |
| 0.4067 | 1100 | 0.0001 | - |
| 0.4251 | 1150 | 0.0002 | - |
| 0.4436 | 1200 | 0.0001 | - |
| 0.4621 | 1250 | 0.0012 | - |
| 0.4806 | 1300 | 0.0 | - |
| 0.4991 | 1350 | 0.0001 | - |
| 0.5176 | 1400 | 0.0001 | - |
| 0.5360 | 1450 | 0.0 | - |
| 0.5545 | 1500 | 0.0001 | - |
| 0.5730 | 1550 | 0.0 | - |
| 0.5915 | 1600 | 0.0267 | - |
| 0.6100 | 1650 | 0.0001 | - |
| 0.6285 | 1700 | 0.0 | - |
| 0.6470 | 1750 | 0.0 | - |
| 0.6654 | 1800 | 0.0 | - |
| 0.6839 | 1850 | 0.0 | - |
| 0.7024 | 1900 | 0.0 | - |
| 0.7209 | 1950 | 0.0 | - |
| 0.7394 | 2000 | 0.0 | - |
| 0.7579 | 2050 | 0.0001 | - |
| 0.7763 | 2100 | 0.0 | - |
| 0.7948 | 2150 | 0.0001 | - |
| 0.8133 | 2200 | 0.0001 | - |
| 0.8318 | 2250 | 0.0 | - |
| 0.8503 | 2300 | 0.0001 | - |
| 0.8688 | 2350 | 0.1116 | - |
| 0.8872 | 2400 | 0.0042 | - |
| 0.9057 | 2450 | 0.0001 | - |
| 0.9242 | 2500 | 0.0006 | - |
| 0.9427 | 2550 | 0.0 | - |
| 0.9612 | 2600 | 0.0615 | - |
| 0.9797 | 2650 | 0.0002 | - |
| 0.9982 | 2700 | 0.0 | - |
| 1.0166 | 2750 | 0.0003 | - |
| 1.0351 | 2800 | 0.0001 | - |
| 1.0536 | 2850 | 0.0 | - |
| 1.0721 | 2900 | 0.0 | - |
| 1.0906 | 2950 | 0.0 | - |
| 1.1091 | 3000 | 0.0 | - |
| 1.1275 | 3050 | 0.0001 | - |
| 1.1460 | 3100 | 0.0 | - |
| 1.1645 | 3150 | 0.0 | - |
| 1.1830 | 3200 | 0.0 | - |
| 1.2015 | 3250 | 0.0 | - |
| 1.2200 | 3300 | 0.0 | - |
| 1.2384 | 3350 | 0.0 | - |
| 1.2569 | 3400 | 0.0 | - |
| 1.2754 | 3450 | 0.0 | - |
| 1.2939 | 3500 | 0.0 | - |
| 1.3124 | 3550 | 0.0 | - |
| 1.3309 | 3600 | 0.0 | - |
| 1.3494 | 3650 | 0.0 | - |
| 1.3678 | 3700 | 0.0 | - |
| 1.3863 | 3750 | 0.0 | - |
| 1.4048 | 3800 | 0.0003 | - |
| 1.4233 | 3850 | 0.0 | - |
| 1.4418 | 3900 | 0.0001 | - |
| 1.4603 | 3950 | 0.0 | - |
| 1.4787 | 4000 | 0.0001 | - |
| 1.4972 | 4050 | 0.0 | - |
| 1.5157 | 4100 | 0.0 | - |
| 1.5342 | 4150 | 0.0 | - |
| 1.5527 | 4200 | 0.0 | - |
| 1.5712 | 4250 | 0.0 | - |
| 1.5896 | 4300 | 0.0 | - |
| 1.6081 | 4350 | 0.0 | - |
| 1.6266 | 4400 | 0.0 | - |
| 1.6451 | 4450 | 0.0 | - |
| 1.6636 | 4500 | 0.0 | - |
| 1.6821 | 4550 | 0.0001 | - |
| 1.7006 | 4600 | 0.0 | - |
| 1.7190 | 4650 | 0.0 | - |
| 1.7375 | 4700 | 0.0 | - |
| 1.7560 | 4750 | 0.0 | - |
| 1.7745 | 4800 | 0.0 | - |
| 1.7930 | 4850 | 0.0 | - |
| 1.8115 | 4900 | 0.0 | - |
| 1.8299 | 4950 | 0.0 | - |
| 1.8484 | 5000 | 0.0 | - |
| 1.8669 | 5050 | 0.0 | - |
| 1.8854 | 5100 | 0.0 | - |
| 1.9039 | 5150 | 0.0 | - |
| 1.9224 | 5200 | 0.0 | - |
| 1.9409 | 5250 | 0.0 | - |
| 1.9593 | 5300 | 0.0 | - |
| 1.9778 | 5350 | 0.0 | - |
| 1.9963 | 5400 | 0.0 | - |
| 2.0148 | 5450 | 0.0 | - |
| 2.0333 | 5500 | 0.0 | - |
| 2.0518 | 5550 | 0.0 | - |
| 2.0702 | 5600 | 0.0001 | - |
| 2.0887 | 5650 | 0.0 | - |
| 2.1072 | 5700 | 0.0 | - |
| 2.1257 | 5750 | 0.0 | - |
| 2.1442 | 5800 | 0.0001 | - |
| 2.1627 | 5850 | 0.0 | - |
| 2.1811 | 5900 | 0.0 | - |
| 2.1996 | 5950 | 0.0 | - |
| 2.2181 | 6000 | 0.0 | - |
| 2.2366 | 6050 | 0.0 | - |
| 2.2551 | 6100 | 0.0 | - |
| 2.2736 | 6150 | 0.0 | - |
| 2.2921 | 6200 | 0.0 | - |
| 2.3105 | 6250 | 0.0 | - |
| 2.3290 | 6300 | 0.0 | - |
| 2.3475 | 6350 | 0.0 | - |
| 2.3660 | 6400 | 0.0 | - |
| 2.3845 | 6450 | 0.0 | - |
| 2.4030 | 6500 | 0.0 | - |
| 2.4214 | 6550 | 0.0 | - |
| 2.4399 | 6600 | 0.0 | - |
| 2.4584 | 6650 | 0.0 | - |
| 2.4769 | 6700 | 0.0 | - |
| 2.4954 | 6750 | 0.0 | - |
| 2.5139 | 6800 | 0.0001 | - |
| 2.5323 | 6850 | 0.0 | - |
| 2.5508 | 6900 | 0.0 | - |
| 2.5693 | 6950 | 0.0 | - |
| 2.5878 | 7000 | 0.0 | - |
| 2.6063 | 7050 | 0.0 | - |
| 2.6248 | 7100 | 0.0 | - |
| 2.6433 | 7150 | 0.0 | - |
| 2.6617 | 7200 | 0.0 | - |
| 2.6802 | 7250 | 0.0 | - |
| 2.6987 | 7300 | 0.0 | - |
| 2.7172 | 7350 | 0.0 | - |
| 2.7357 | 7400 | 0.0 | - |
| 2.7542 | 7450 | 0.0 | - |
| 2.7726 | 7500 | 0.0 | - |
| 2.7911 | 7550 | 0.0 | - |
| 2.8096 | 7600 | 0.0 | - |
| 2.8281 | 7650 | 0.0 | - |
| 2.8466 | 7700 | 0.0 | - |
| 2.8651 | 7750 | 0.0 | - |
| 2.8835 | 7800 | 0.0 | - |
| 2.9020 | 7850 | 0.0 | - |
| 2.9205 | 7900 | 0.0 | - |
| 2.9390 | 7950 | 0.0 | - |
| 2.9575 | 8000 | 0.0 | - |
| 2.9760 | 8050 | 0.0 | - |
| 2.9945 | 8100 | 0.0 | - |
### Framework Versions
- Python: 3.9.16
- SetFit: 1.0.1
- Sentence Transformers: 2.2.2
- Transformers: 4.35.0
- PyTorch: 2.1.0+cu121
- Datasets: 2.14.6
- Tokenizers: 0.14.1
## 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}
}
```