SetFit with FacebookAI/roberta-large
This is a SetFit model that can be used for Text Classification. This SetFit model uses FacebookAI/roberta-large as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
- Fine-tuning a Sentence Transformer with contrastive learning.
- Training a classification head with features from the fine-tuned Sentence Transformer.
Model Details
Model Description
Model Sources
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
Uses
Direct Use for Inference
First install the SetFit library:
pip install setfit
Then you can load this model and run inference.
from setfit import SetFitModel
model = SetFitModel.from_pretrained("richie-ghost/setfit-FacebookAI-roberta-large-phatic")
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
@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}
}