---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: FacebookAI/roberta-base
metrics:
- accuracy
widget:
- text: Just checking in, how have you been feeling since our last chat?
- text: I’m looking forward to learning more from you.
- text: Take it easy!
- text: It was great seeing you. Let's catch up again soon!
- text: Let’s make sure you’re not carrying too much; how are you?
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with FacebookAI/roberta-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.94
name: Accuracy
---
# SetFit with FacebookAI/roberta-base
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) 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:** [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base)
- **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:** 2 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 |
|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| true | - 'How can we apply your findings to optimize our processes?'
- 'Your presence at the meeting was greatly appreciated.'
- 'Your journey is quite inspiring, can you share more about it?'
|
| false | - 'What book are you currently reading?'
- 'It’s important to acknowledge your feelings, what’s been going through your mind?'
- 'You’ve been working hard on your mental health; how are you finding the journey?'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.94 |
## 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("richie-ghost/setfit-FacebookAI-roberta-base-phatic")
# Run inference
preds = model("Take it easy!")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 9.8722 | 108 |
| Label | Training Sample Count |
|:------|:----------------------|
| false | 191 |
| true | 169 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (4, 4)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:---------:|:-------------:|:---------------:|
| 0.0002 | 1 | 0.4475 | - |
| 0.0122 | 50 | 0.4363 | - |
| 0.0245 | 100 | 0.3668 | - |
| 0.0367 | 150 | 0.177 | - |
| 0.0489 | 200 | 0.0999 | - |
| 0.0612 | 250 | 0.1043 | - |
| 0.0734 | 300 | 0.0191 | - |
| 0.0856 | 350 | 0.009 | - |
| 0.0978 | 400 | 0.0028 | - |
| 0.1101 | 450 | 0.0046 | - |
| 0.1223 | 500 | 0.0012 | - |
| 0.1345 | 550 | 0.0016 | - |
| 0.1468 | 600 | 0.0012 | - |
| 0.1590 | 650 | 0.0012 | - |
| 0.1712 | 700 | 0.0164 | - |
| 0.1835 | 750 | 0.025 | - |
| 0.1957 | 800 | 0.0007 | - |
| 0.2079 | 850 | 0.0013 | - |
| 0.2202 | 900 | 0.0008 | - |
| 0.2324 | 950 | 0.0005 | - |
| 0.2446 | 1000 | 0.0004 | - |
| 0.2568 | 1050 | 0.0002 | - |
| 0.2691 | 1100 | 0.0004 | - |
| 0.2813 | 1150 | 0.0003 | - |
| 0.2935 | 1200 | 0.0002 | - |
| 0.3058 | 1250 | 0.0002 | - |
| 0.3180 | 1300 | 0.0003 | - |
| 0.3302 | 1350 | 0.0002 | - |
| 0.3425 | 1400 | 0.0001 | - |
| 0.3547 | 1450 | 0.003 | - |
| 0.3669 | 1500 | 0.0003 | - |
| 0.3792 | 1550 | 0.0003 | - |
| 0.3914 | 1600 | 0.0001 | - |
| 0.4036 | 1650 | 0.0001 | - |
| 0.4159 | 1700 | 0.0001 | - |
| 0.4281 | 1750 | 0.0001 | - |
| 0.4403 | 1800 | 0.0001 | - |
| 0.4525 | 1850 | 0.0001 | - |
| 0.4648 | 1900 | 0.0001 | - |
| 0.4770 | 1950 | 0.0001 | - |
| 0.4892 | 2000 | 0.0001 | - |
| 0.5015 | 2050 | 0.0001 | - |
| 0.5137 | 2100 | 0.0001 | - |
| 0.5259 | 2150 | 0.0001 | - |
| 0.5382 | 2200 | 0.0 | - |
| 0.5504 | 2250 | 0.0 | - |
| 0.5626 | 2300 | 0.0 | - |
| 0.5749 | 2350 | 0.0001 | - |
| 0.5871 | 2400 | 0.0 | - |
| 0.5993 | 2450 | 0.0 | - |
| 0.6115 | 2500 | 0.0001 | - |
| 0.6238 | 2550 | 0.0001 | - |
| 0.6360 | 2600 | 0.0 | - |
| 0.6482 | 2650 | 0.0 | - |
| 0.6605 | 2700 | 0.0 | - |
| 0.6727 | 2750 | 0.0 | - |
| 0.6849 | 2800 | 0.0 | - |
| 0.6972 | 2850 | 0.0 | - |
| 0.7094 | 2900 | 0.0001 | - |
| 0.7216 | 2950 | 0.0001 | - |
| 0.7339 | 3000 | 0.0 | - |
| 0.7461 | 3050 | 0.0 | - |
| 0.7583 | 3100 | 0.0006 | - |
| 0.7705 | 3150 | 0.0606 | - |
| 0.7828 | 3200 | 0.0 | - |
| 0.7950 | 3250 | 0.0002 | - |
| 0.8072 | 3300 | 0.0 | - |
| 0.8195 | 3350 | 0.0001 | - |
| 0.8317 | 3400 | 0.0001 | - |
| 0.8439 | 3450 | 0.0 | - |
| 0.8562 | 3500 | 0.0001 | - |
| 0.8684 | 3550 | 0.0 | - |
| 0.8806 | 3600 | 0.0 | - |
| 0.8929 | 3650 | 0.0 | - |
| 0.9051 | 3700 | 0.0 | - |
| 0.9173 | 3750 | 0.0 | - |
| 0.9295 | 3800 | 0.0 | - |
| 0.9418 | 3850 | 0.0 | - |
| 0.9540 | 3900 | 0.0 | - |
| 0.9662 | 3950 | 0.0 | - |
| 0.9785 | 4000 | 0.0 | - |
| 0.9907 | 4050 | 0.0 | - |
| 1.0 | 4088 | - | 0.1621 |
| 1.0029 | 4100 | 0.0 | - |
| 1.0152 | 4150 | 0.0 | - |
| 1.0274 | 4200 | 0.0 | - |
| 1.0396 | 4250 | 0.0 | - |
| 1.0519 | 4300 | 0.0 | - |
| 1.0641 | 4350 | 0.0 | - |
| 1.0763 | 4400 | 0.0 | - |
| 1.0886 | 4450 | 0.0 | - |
| 1.1008 | 4500 | 0.0 | - |
| 1.1130 | 4550 | 0.0 | - |
| 1.1252 | 4600 | 0.0 | - |
| 1.1375 | 4650 | 0.0 | - |
| 1.1497 | 4700 | 0.0 | - |
| 1.1619 | 4750 | 0.0 | - |
| 1.1742 | 4800 | 0.0 | - |
| 1.1864 | 4850 | 0.0 | - |
| 1.1986 | 4900 | 0.0 | - |
| 1.2109 | 4950 | 0.0 | - |
| 1.2231 | 5000 | 0.0 | - |
| 1.2353 | 5050 | 0.0 | - |
| 1.2476 | 5100 | 0.0 | - |
| 1.2598 | 5150 | 0.0 | - |
| 1.2720 | 5200 | 0.0 | - |
| 1.2842 | 5250 | 0.0 | - |
| 1.2965 | 5300 | 0.0 | - |
| 1.3087 | 5350 | 0.0 | - |
| 1.3209 | 5400 | 0.0 | - |
| 1.3332 | 5450 | 0.0 | - |
| 1.3454 | 5500 | 0.0 | - |
| 1.3576 | 5550 | 0.0 | - |
| 1.3699 | 5600 | 0.0 | - |
| 1.3821 | 5650 | 0.0 | - |
| 1.3943 | 5700 | 0.0 | - |
| 1.4066 | 5750 | 0.0 | - |
| 1.4188 | 5800 | 0.0 | - |
| 1.4310 | 5850 | 0.0 | - |
| 1.4432 | 5900 | 0.0 | - |
| 1.4555 | 5950 | 0.0 | - |
| 1.4677 | 6000 | 0.0 | - |
| 1.4799 | 6050 | 0.0 | - |
| 1.4922 | 6100 | 0.0 | - |
| 1.5044 | 6150 | 0.0 | - |
| 1.5166 | 6200 | 0.0 | - |
| 1.5289 | 6250 | 0.0 | - |
| 1.5411 | 6300 | 0.0 | - |
| 1.5533 | 6350 | 0.0 | - |
| 1.5656 | 6400 | 0.0 | - |
| 1.5778 | 6450 | 0.0 | - |
| 1.5900 | 6500 | 0.0 | - |
| 1.6023 | 6550 | 0.0 | - |
| 1.6145 | 6600 | 0.0 | - |
| 1.6267 | 6650 | 0.0 | - |
| 1.6389 | 6700 | 0.0 | - |
| 1.6512 | 6750 | 0.0 | - |
| 1.6634 | 6800 | 0.0 | - |
| 1.6756 | 6850 | 0.0 | - |
| 1.6879 | 6900 | 0.0 | - |
| 1.7001 | 6950 | 0.0 | - |
| 1.7123 | 7000 | 0.0 | - |
| 1.7246 | 7050 | 0.0 | - |
| 1.7368 | 7100 | 0.0 | - |
| 1.7490 | 7150 | 0.0 | - |
| 1.7613 | 7200 | 0.0 | - |
| 1.7735 | 7250 | 0.0 | - |
| 1.7857 | 7300 | 0.0 | - |
| 1.7979 | 7350 | 0.0 | - |
| 1.8102 | 7400 | 0.0 | - |
| 1.8224 | 7450 | 0.0 | - |
| 1.8346 | 7500 | 0.0 | - |
| 1.8469 | 7550 | 0.0 | - |
| 1.8591 | 7600 | 0.0 | - |
| 1.8713 | 7650 | 0.0 | - |
| 1.8836 | 7700 | 0.0 | - |
| 1.8958 | 7750 | 0.0 | - |
| 1.9080 | 7800 | 0.0 | - |
| 1.9203 | 7850 | 0.0 | - |
| 1.9325 | 7900 | 0.0 | - |
| 1.9447 | 7950 | 0.0 | - |
| 1.9569 | 8000 | 0.0 | - |
| 1.9692 | 8050 | 0.0 | - |
| 1.9814 | 8100 | 0.0 | - |
| 1.9936 | 8150 | 0.0 | - |
| 2.0 | 8176 | - | 0.1131 |
| 2.0059 | 8200 | 0.0 | - |
| 2.0181 | 8250 | 0.0 | - |
| 2.0303 | 8300 | 0.0 | - |
| 2.0426 | 8350 | 0.0 | - |
| 2.0548 | 8400 | 0.0 | - |
| 2.0670 | 8450 | 0.0 | - |
| 2.0793 | 8500 | 0.0 | - |
| 2.0915 | 8550 | 0.0 | - |
| 2.1037 | 8600 | 0.0 | - |
| 2.1159 | 8650 | 0.0 | - |
| 2.1282 | 8700 | 0.0 | - |
| 2.1404 | 8750 | 0.0 | - |
| 2.1526 | 8800 | 0.0 | - |
| 2.1649 | 8850 | 0.0 | - |
| 2.1771 | 8900 | 0.0 | - |
| 2.1893 | 8950 | 0.0 | - |
| 2.2016 | 9000 | 0.0 | - |
| 2.2138 | 9050 | 0.0 | - |
| 2.2260 | 9100 | 0.0 | - |
| 2.2383 | 9150 | 0.0 | - |
| 2.2505 | 9200 | 0.0 | - |
| 2.2627 | 9250 | 0.0 | - |
| 2.2750 | 9300 | 0.0 | - |
| 2.2872 | 9350 | 0.0 | - |
| 2.2994 | 9400 | 0.0 | - |
| 2.3116 | 9450 | 0.0 | - |
| 2.3239 | 9500 | 0.0 | - |
| 2.3361 | 9550 | 0.0 | - |
| 2.3483 | 9600 | 0.0 | - |
| 2.3606 | 9650 | 0.0 | - |
| 2.3728 | 9700 | 0.0 | - |
| 2.3850 | 9750 | 0.0 | - |
| 2.3973 | 9800 | 0.0 | - |
| 2.4095 | 9850 | 0.0 | - |
| 2.4217 | 9900 | 0.0 | - |
| 2.4340 | 9950 | 0.0 | - |
| 2.4462 | 10000 | 0.0 | - |
| 2.4584 | 10050 | 0.0 | - |
| 2.4706 | 10100 | 0.0 | - |
| 2.4829 | 10150 | 0.0 | - |
| 2.4951 | 10200 | 0.0 | - |
| 2.5073 | 10250 | 0.0 | - |
| 2.5196 | 10300 | 0.0 | - |
| 2.5318 | 10350 | 0.0 | - |
| 2.5440 | 10400 | 0.0 | - |
| 2.5563 | 10450 | 0.0 | - |
| 2.5685 | 10500 | 0.0 | - |
| 2.5807 | 10550 | 0.0 | - |
| 2.5930 | 10600 | 0.0 | - |
| 2.6052 | 10650 | 0.0 | - |
| 2.6174 | 10700 | 0.0 | - |
| 2.6296 | 10750 | 0.0 | - |
| 2.6419 | 10800 | 0.0 | - |
| 2.6541 | 10850 | 0.0 | - |
| 2.6663 | 10900 | 0.0 | - |
| 2.6786 | 10950 | 0.0 | - |
| 2.6908 | 11000 | 0.0 | - |
| 2.7030 | 11050 | 0.0 | - |
| 2.7153 | 11100 | 0.0 | - |
| 2.7275 | 11150 | 0.0 | - |
| 2.7397 | 11200 | 0.0 | - |
| 2.7520 | 11250 | 0.0 | - |
| 2.7642 | 11300 | 0.0 | - |
| 2.7764 | 11350 | 0.0 | - |
| 2.7886 | 11400 | 0.0 | - |
| 2.8009 | 11450 | 0.0 | - |
| 2.8131 | 11500 | 0.0 | - |
| 2.8253 | 11550 | 0.0 | - |
| 2.8376 | 11600 | 0.0 | - |
| 2.8498 | 11650 | 0.0 | - |
| 2.8620 | 11700 | 0.0 | - |
| 2.8743 | 11750 | 0.0 | - |
| 2.8865 | 11800 | 0.0 | - |
| 2.8987 | 11850 | 0.0 | - |
| 2.9110 | 11900 | 0.0 | - |
| 2.9232 | 11950 | 0.0 | - |
| 2.9354 | 12000 | 0.0 | - |
| 2.9477 | 12050 | 0.0 | - |
| 2.9599 | 12100 | 0.0 | - |
| 2.9721 | 12150 | 0.0 | - |
| 2.9843 | 12200 | 0.0 | - |
| 2.9966 | 12250 | 0.0 | - |
| 3.0 | 12264 | - | 0.1127 |
| 3.0088 | 12300 | 0.0 | - |
| 3.0210 | 12350 | 0.0 | - |
| 3.0333 | 12400 | 0.0 | - |
| 3.0455 | 12450 | 0.0 | - |
| 3.0577 | 12500 | 0.0 | - |
| 3.0700 | 12550 | 0.0 | - |
| 3.0822 | 12600 | 0.0 | - |
| 3.0944 | 12650 | 0.0 | - |
| 3.1067 | 12700 | 0.0 | - |
| 3.1189 | 12750 | 0.0 | - |
| 3.1311 | 12800 | 0.0 | - |
| 3.1433 | 12850 | 0.0 | - |
| 3.1556 | 12900 | 0.0 | - |
| 3.1678 | 12950 | 0.0 | - |
| 3.1800 | 13000 | 0.0 | - |
| 3.1923 | 13050 | 0.0 | - |
| 3.2045 | 13100 | 0.0 | - |
| 3.2167 | 13150 | 0.0 | - |
| 3.2290 | 13200 | 0.0 | - |
| 3.2412 | 13250 | 0.0 | - |
| 3.2534 | 13300 | 0.0 | - |
| 3.2657 | 13350 | 0.0 | - |
| 3.2779 | 13400 | 0.0 | - |
| 3.2901 | 13450 | 0.0 | - |
| 3.3023 | 13500 | 0.0 | - |
| 3.3146 | 13550 | 0.0 | - |
| 3.3268 | 13600 | 0.0 | - |
| 3.3390 | 13650 | 0.0 | - |
| 3.3513 | 13700 | 0.0 | - |
| 3.3635 | 13750 | 0.0 | - |
| 3.3757 | 13800 | 0.0 | - |
| 3.3880 | 13850 | 0.0 | - |
| 3.4002 | 13900 | 0.0 | - |
| 3.4124 | 13950 | 0.0 | - |
| 3.4247 | 14000 | 0.0 | - |
| 3.4369 | 14050 | 0.0 | - |
| 3.4491 | 14100 | 0.0 | - |
| 3.4614 | 14150 | 0.0 | - |
| 3.4736 | 14200 | 0.0 | - |
| 3.4858 | 14250 | 0.0 | - |
| 3.4980 | 14300 | 0.0 | - |
| 3.5103 | 14350 | 0.0 | - |
| 3.5225 | 14400 | 0.0 | - |
| 3.5347 | 14450 | 0.0 | - |
| 3.5470 | 14500 | 0.0 | - |
| 3.5592 | 14550 | 0.0 | - |
| 3.5714 | 14600 | 0.0 | - |
| 3.5837 | 14650 | 0.0 | - |
| 3.5959 | 14700 | 0.0 | - |
| 3.6081 | 14750 | 0.0 | - |
| 3.6204 | 14800 | 0.0 | - |
| 3.6326 | 14850 | 0.0 | - |
| 3.6448 | 14900 | 0.0 | - |
| 3.6570 | 14950 | 0.0 | - |
| 3.6693 | 15000 | 0.0 | - |
| 3.6815 | 15050 | 0.0 | - |
| 3.6937 | 15100 | 0.0 | - |
| 3.7060 | 15150 | 0.0 | - |
| 3.7182 | 15200 | 0.0 | - |
| 3.7304 | 15250 | 0.0 | - |
| 3.7427 | 15300 | 0.0 | - |
| 3.7549 | 15350 | 0.0 | - |
| 3.7671 | 15400 | 0.0 | - |
| 3.7794 | 15450 | 0.0 | - |
| 3.7916 | 15500 | 0.0 | - |
| 3.8038 | 15550 | 0.0 | - |
| 3.8160 | 15600 | 0.0 | - |
| 3.8283 | 15650 | 0.0 | - |
| 3.8405 | 15700 | 0.0 | - |
| 3.8527 | 15750 | 0.0 | - |
| 3.8650 | 15800 | 0.0 | - |
| 3.8772 | 15850 | 0.0 | - |
| 3.8894 | 15900 | 0.0 | - |
| 3.9017 | 15950 | 0.0 | - |
| 3.9139 | 16000 | 0.0 | - |
| 3.9261 | 16050 | 0.0 | - |
| 3.9384 | 16100 | 0.0 | - |
| 3.9506 | 16150 | 0.0 | - |
| 3.9628 | 16200 | 0.0 | - |
| 3.9750 | 16250 | 0.0 | - |
| 3.9873 | 16300 | 0.0 | - |
| 3.9995 | 16350 | 0.0 | - |
| **4.0** | **16352** | **-** | **0.1019** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.0
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1
## 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}
}
```