---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: FacebookAI/roberta-large
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-large
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.96
name: Accuracy
---
# SetFit with FacebookAI/roberta-large
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) 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-large](https://huggingface.co/FacebookAI/roberta-large)
- **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 | - 'See you soon!'
- 'You look well!'
- 'Your journey is quite inspiring, can you share more about it?'
|
| false | - 'What are the core components of your business model?'
- 'How do you balance your personal and professional life?'
- "There is a situation where a daughter of a narcissistic mother denigrated the father. When the mother complained to the daughter about the father and how poor he was a a husband and person and how badly he treated the wife. The mother's claims were inaccurate and overblown. The mother said I inappropriate things to the daughter such as he flirted with other women, or the mother could have done much better than marrying him. After such episodes, the daughter was dismissive and rude to the father. What are the signs of parental alienation and what are the impacts on a daughter growing up and as an adult?"
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.96 |
## 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-large-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 | 0.4745 | - |
| 0.0122 | 50 | 0.441 | - |
| 0.0245 | 100 | 0.4422 | - |
| 0.0367 | 150 | 0.2339 | - |
| 0.0489 | 200 | 0.1182 | - |
| 0.0612 | 250 | 0.0806 | - |
| 0.0734 | 300 | 0.1183 | - |
| 0.0856 | 350 | 0.0551 | - |
| 0.0978 | 400 | 0.0146 | - |
| 0.1101 | 450 | 0.0115 | - |
| 0.1223 | 500 | 0.0042 | - |
| 0.1345 | 550 | 0.0053 | - |
| 0.1468 | 600 | 0.0021 | - |
| 0.1590 | 650 | 0.0596 | - |
| 0.1712 | 700 | 0.0029 | - |
| 0.1835 | 750 | 0.0009 | - |
| 0.1957 | 800 | 0.0002 | - |
| 0.2079 | 850 | 0.0005 | - |
| 0.2202 | 900 | 0.0013 | - |
| 0.2324 | 950 | 0.0008 | - |
| 0.2446 | 1000 | 0.0004 | - |
| 0.2568 | 1050 | 0.0004 | - |
| 0.2691 | 1100 | 0.0004 | - |
| 0.2813 | 1150 | 0.0003 | - |
| 0.2935 | 1200 | 0.0003 | - |
| 0.3058 | 1250 | 0.0012 | - |
| 0.3180 | 1300 | 0.0001 | - |
| 0.3302 | 1350 | 0.0002 | - |
| 0.3425 | 1400 | 0.0003 | - |
| 0.3547 | 1450 | 0.0024 | - |
| 0.3669 | 1500 | 0.0008 | - |
| 0.3792 | 1550 | 0.0015 | - |
| 0.3914 | 1600 | 0.0002 | - |
| 0.4036 | 1650 | 0.0002 | - |
| 0.4159 | 1700 | 0.1842 | - |
| 0.4281 | 1750 | 0.0009 | - |
| 0.4403 | 1800 | 0.0001 | - |
| 0.4525 | 1850 | 0.0013 | - |
| 0.4648 | 1900 | 0.0637 | - |
| 0.4770 | 1950 | 0.0002 | - |
| 0.4892 | 2000 | 0.0007 | - |
| 0.5015 | 2050 | 0.0001 | - |
| 0.5137 | 2100 | 0.0 | - |
| 0.5259 | 2150 | 0.0 | - |
| 0.5382 | 2200 | 0.0 | - |
| 0.5504 | 2250 | 0.0 | - |
| 0.5626 | 2300 | 0.0001 | - |
| 0.5749 | 2350 | 0.0 | - |
| 0.5871 | 2400 | 0.0 | - |
| 0.5993 | 2450 | 0.0 | - |
| 0.6115 | 2500 | 0.0 | - |
| 0.6238 | 2550 | 0.0 | - |
| 0.6360 | 2600 | 0.0 | - |
| 0.6482 | 2650 | 0.0 | - |
| 0.6605 | 2700 | 0.0001 | - |
| 0.6727 | 2750 | 0.0 | - |
| 0.6849 | 2800 | 0.0 | - |
| 0.6972 | 2850 | 0.0 | - |
| 0.7094 | 2900 | 0.0 | - |
| 0.7216 | 2950 | 0.0 | - |
| 0.7339 | 3000 | 0.0 | - |
| 0.7461 | 3050 | 0.0 | - |
| 0.7583 | 3100 | 0.0001 | - |
| 0.7705 | 3150 | 0.0 | - |
| 0.7828 | 3200 | 0.0 | - |
| 0.7950 | 3250 | 0.0 | - |
| 0.8072 | 3300 | 0.0 | - |
| 0.8195 | 3350 | 0.0 | - |
| 0.8317 | 3400 | 0.0 | - |
| 0.8439 | 3450 | 0.0001 | - |
| 0.8562 | 3500 | 0.0 | - |
| 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** | **-** | **0.0815** |
| 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 | 0.0112 | - |
| 1.5166 | 6200 | 0.4712 | - |
| 1.5289 | 6250 | 0.3977 | - |
| 1.5411 | 6300 | 0.2112 | - |
| 1.5533 | 6350 | 0.318 | - |
| 1.5656 | 6400 | 0.2523 | - |
| 1.5778 | 6450 | 0.2829 | - |
| 1.5900 | 6500 | 0.2736 | - |
| 1.6023 | 6550 | 0.2493 | - |
| 1.6145 | 6600 | 0.3112 | - |
| 1.6267 | 6650 | 0.2291 | - |
| 1.6389 | 6700 | 0.2855 | - |
| 1.6512 | 6750 | 0.2642 | - |
| 1.6634 | 6800 | 0.2376 | - |
| 1.6756 | 6850 | 0.2983 | - |
| 1.6879 | 6900 | 0.2853 | - |
| 1.7001 | 6950 | 0.3095 | - |
| 1.7123 | 7000 | 0.2497 | - |
| 1.7246 | 7050 | 0.2305 | - |
| 1.7368 | 7100 | 0.2433 | - |
| 1.7490 | 7150 | 0.2505 | - |
| 1.7613 | 7200 | 0.2292 | - |
| 1.7735 | 7250 | 0.3028 | - |
| 1.7857 | 7300 | 0.2394 | - |
| 1.7979 | 7350 | 0.2601 | - |
| 1.8102 | 7400 | 0.2417 | - |
| 1.8224 | 7450 | 0.2086 | - |
| 1.8346 | 7500 | 0.2573 | - |
| 1.8469 | 7550 | 0.2344 | - |
| 1.8591 | 7600 | 0.2381 | - |
| 1.8713 | 7650 | 0.2772 | - |
| 1.8836 | 7700 | 0.2614 | - |
| 1.8958 | 7750 | 0.2659 | - |
| 1.9080 | 7800 | 0.2536 | - |
| 1.9203 | 7850 | 0.2385 | - |
| 1.9325 | 7900 | 0.2695 | - |
| 1.9447 | 7950 | 0.2512 | - |
| 1.9569 | 8000 | 0.2216 | - |
| 1.9692 | 8050 | 0.2291 | - |
| 1.9814 | 8100 | 0.2443 | - |
| 1.9936 | 8150 | 0.2579 | - |
| 2.0 | 8176 | - | 0.5 |
| 2.0059 | 8200 | 0.2605 | - |
| 2.0181 | 8250 | 0.2528 | - |
| 2.0303 | 8300 | 0.2361 | - |
| 2.0426 | 8350 | 0.2891 | - |
| 2.0548 | 8400 | 0.2692 | - |
| 2.0670 | 8450 | 0.25 | - |
| 2.0793 | 8500 | 0.2362 | - |
| 2.0915 | 8550 | 0.2833 | - |
| 2.1037 | 8600 | 0.2698 | - |
| 2.1159 | 8650 | 0.2195 | - |
| 2.1282 | 8700 | 0.2621 | - |
| 2.1404 | 8750 | 0.2564 | - |
| 2.1526 | 8800 | 0.2657 | - |
| 2.1649 | 8850 | 0.2629 | - |
| 2.1771 | 8900 | 0.2503 | - |
| 2.1893 | 8950 | 0.2583 | - |
| 2.2016 | 9000 | 0.2694 | - |
| 2.2138 | 9050 | 0.2824 | - |
| 2.2260 | 9100 | 0.2675 | - |
| 2.2383 | 9150 | 0.2699 | - |
| 2.2505 | 9200 | 0.2515 | - |
| 2.2627 | 9250 | 0.2511 | - |
| 2.2750 | 9300 | 0.2518 | - |
| 2.2872 | 9350 | 0.2555 | - |
| 2.2994 | 9400 | 0.2512 | - |
| 2.3116 | 9450 | 0.2374 | - |
| 2.3239 | 9500 | 0.2546 | - |
| 2.3361 | 9550 | 0.2846 | - |
| 2.3483 | 9600 | 0.2617 | - |
| 2.3606 | 9650 | 0.2474 | - |
| 2.3728 | 9700 | 0.2454 | - |
| 2.3850 | 9750 | 0.2265 | - |
| 2.3973 | 9800 | 0.2272 | - |
| 2.4095 | 9850 | 0.2442 | - |
| 2.4217 | 9900 | 0.236 | - |
| 2.4340 | 9950 | 0.2382 | - |
| 2.4462 | 10000 | 0.2645 | - |
| 2.4584 | 10050 | 0.2707 | - |
| 2.4706 | 10100 | 0.2573 | - |
| 2.4829 | 10150 | 0.2435 | - |
| 2.4951 | 10200 | 0.2705 | - |
| 2.5073 | 10250 | 0.2808 | - |
| 2.5196 | 10300 | 0.2581 | - |
| 2.5318 | 10350 | 0.2544 | - |
| 2.5440 | 10400 | 0.2333 | - |
| 2.5563 | 10450 | 0.2544 | - |
| 2.5685 | 10500 | 0.2497 | - |
| 2.5807 | 10550 | 0.2575 | - |
| 2.5930 | 10600 | 0.2382 | - |
| 2.6052 | 10650 | 0.2451 | - |
| 2.6174 | 10700 | 0.2702 | - |
| 2.6296 | 10750 | 0.2569 | - |
| 2.6419 | 10800 | 0.249 | - |
| 2.6541 | 10850 | 0.2366 | - |
| 2.6663 | 10900 | 0.2278 | - |
| 2.6786 | 10950 | 0.2568 | - |
| 2.6908 | 11000 | 0.2721 | - |
| 2.7030 | 11050 | 0.2593 | - |
| 2.7153 | 11100 | 0.2439 | - |
| 2.7275 | 11150 | 0.2543 | - |
| 2.7397 | 11200 | 0.2478 | - |
| 2.7520 | 11250 | 0.2325 | - |
| 2.7642 | 11300 | 0.2538 | - |
| 2.7764 | 11350 | 0.2968 | - |
| 2.7886 | 11400 | 0.2505 | - |
| 2.8009 | 11450 | 0.2377 | - |
| 2.8131 | 11500 | 0.2547 | - |
| 2.8253 | 11550 | 0.2529 | - |
| 2.8376 | 11600 | 0.2502 | - |
| 2.8498 | 11650 | 0.2293 | - |
| 2.8620 | 11700 | 0.2676 | - |
| 2.8743 | 11750 | 0.2371 | - |
| 2.8865 | 11800 | 0.2495 | - |
| 2.8987 | 11850 | 0.2937 | - |
| 2.9110 | 11900 | 0.2355 | - |
| 2.9232 | 11950 | 0.2482 | - |
| 2.9354 | 12000 | 0.2336 | - |
| 2.9477 | 12050 | 0.2344 | - |
| 2.9599 | 12100 | 0.257 | - |
| 2.9721 | 12150 | 0.2557 | - |
| 2.9843 | 12200 | 0.2854 | - |
| 2.9966 | 12250 | 0.2455 | - |
| 3.0 | 12264 | - | 0.5 |
| 3.0088 | 12300 | 0.2323 | - |
| 3.0210 | 12350 | 0.2566 | - |
| 3.0333 | 12400 | 0.2319 | - |
| 3.0455 | 12450 | 0.2552 | - |
| 3.0577 | 12500 | 0.2796 | - |
| 3.0700 | 12550 | 0.2823 | - |
| 3.0822 | 12600 | 0.2303 | - |
| 3.0944 | 12650 | 0.2448 | - |
| 3.1067 | 12700 | 0.2502 | - |
| 3.1189 | 12750 | 0.2516 | - |
| 3.1311 | 12800 | 0.2537 | - |
| 3.1433 | 12850 | 0.251 | - |
| 3.1556 | 12900 | 0.2639 | - |
| 3.1678 | 12950 | 0.2321 | - |
| 3.1800 | 13000 | 0.282 | - |
| 3.1923 | 13050 | 0.2577 | - |
| 3.2045 | 13100 | 0.2448 | - |
| 3.2167 | 13150 | 0.2352 | - |
| 3.2290 | 13200 | 0.281 | - |
| 3.2412 | 13250 | 0.2337 | - |
| 3.2534 | 13300 | 0.268 | - |
| 3.2657 | 13350 | 0.261 | - |
| 3.2779 | 13400 | 0.2378 | - |
| 3.2901 | 13450 | 0.2588 | - |
| 3.3023 | 13500 | 0.266 | - |
| 3.3146 | 13550 | 0.2604 | - |
| 3.3268 | 13600 | 0.2202 | - |
| 3.3390 | 13650 | 0.2217 | - |
| 3.3513 | 13700 | 0.2464 | - |
| 3.3635 | 13750 | 0.2684 | - |
| 3.3757 | 13800 | 0.2279 | - |
| 3.3880 | 13850 | 0.2379 | - |
| 3.4002 | 13900 | 0.2741 | - |
| 3.4124 | 13950 | 0.2713 | - |
| 3.4247 | 14000 | 0.2581 | - |
| 3.4369 | 14050 | 0.2638 | - |
| 3.4491 | 14100 | 0.2125 | - |
| 3.4614 | 14150 | 0.2348 | - |
| 3.4736 | 14200 | 0.2253 | - |
| 3.4858 | 14250 | 0.2627 | - |
| 3.4980 | 14300 | 0.2463 | - |
| 3.5103 | 14350 | 0.2533 | - |
| 3.5225 | 14400 | 0.2422 | - |
| 3.5347 | 14450 | 0.2296 | - |
| 3.5470 | 14500 | 0.2532 | - |
| 3.5592 | 14550 | 0.2733 | - |
| 3.5714 | 14600 | 0.2258 | - |
| 3.5837 | 14650 | 0.2253 | - |
| 3.5959 | 14700 | 0.2388 | - |
| 3.6081 | 14750 | 0.2217 | - |
| 3.6204 | 14800 | 0.3033 | - |
| 3.6326 | 14850 | 0.2349 | - |
| 3.6448 | 14900 | 0.2596 | - |
| 3.6570 | 14950 | 0.2415 | - |
| 3.6693 | 15000 | 0.2494 | - |
| 3.6815 | 15050 | 0.2826 | - |
| 3.6937 | 15100 | 0.2633 | - |
| 3.7060 | 15150 | 0.2636 | - |
| 3.7182 | 15200 | 0.2351 | - |
| 3.7304 | 15250 | 0.264 | - |
| 3.7427 | 15300 | 0.2652 | - |
| 3.7549 | 15350 | 0.2724 | - |
| 3.7671 | 15400 | 0.2731 | - |
| 3.7794 | 15450 | 0.2825 | - |
| 3.7916 | 15500 | 0.2611 | - |
| 3.8038 | 15550 | 0.2574 | - |
| 3.8160 | 15600 | 0.261 | - |
| 3.8283 | 15650 | 0.219 | - |
| 3.8405 | 15700 | 0.2323 | - |
| 3.8527 | 15750 | 0.2442 | - |
| 3.8650 | 15800 | 0.2509 | - |
| 3.8772 | 15850 | 0.26 | - |
| 3.8894 | 15900 | 0.2475 | - |
| 3.9017 | 15950 | 0.2452 | - |
| 3.9139 | 16000 | 0.2598 | - |
| 3.9261 | 16050 | 0.2377 | - |
| 3.9384 | 16100 | 0.2445 | - |
| 3.9506 | 16150 | 0.2451 | - |
| 3.9628 | 16200 | 0.2714 | - |
| 3.9750 | 16250 | 0.2755 | - |
| 3.9873 | 16300 | 0.2579 | - |
| 3.9995 | 16350 | 0.2338 | - |
| 4.0 | 16352 | - | 0.5 |
* 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}
}
```