SetFit with BAAI/bge-small-en-v1.5
This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-en-v1.5 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 |
ORGANIZATIONAL |
- 'cryptonewton Shelby BitGet partner '
- 'trezor Trezor Crypto security made easy'
- 'forbes Forbes Sign up now for Forbes free daily newsletter for unmatched insights and exclusive reporting '
|
INDIVIDUAL |
- 'anbessa100 ANBESSA No paid service Never DM u'
- 'sbf_ftx SBF '
- 'machibigbrother Machi Big Brother '
|
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("kasparas12/is_organizational_model")
preds = model("tradermayne Mayne ")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
3 |
15.7338 |
35 |
Label |
Training Sample Count |
INDIVIDUAL |
423 |
ORGANIZATIONAL |
377 |
Training Hyperparameters
- batch_size: (32, 32)
- 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 |
0.0016 |
1 |
0.2511 |
- |
0.0789 |
50 |
0.2505 |
- |
0.1577 |
100 |
0.2225 |
- |
0.2366 |
150 |
0.2103 |
- |
0.3155 |
200 |
0.1383 |
- |
0.3943 |
250 |
0.0329 |
- |
0.4732 |
300 |
0.0098 |
- |
0.5521 |
350 |
0.0034 |
- |
0.6309 |
400 |
0.0019 |
- |
0.7098 |
450 |
0.0015 |
- |
0.7886 |
500 |
0.0014 |
- |
0.8675 |
550 |
0.0012 |
- |
0.0001 |
1 |
0.2524 |
- |
0.0050 |
50 |
0.2115 |
- |
0.0099 |
100 |
0.193 |
- |
0.0001 |
1 |
0.2424 |
- |
0.0050 |
50 |
0.2038 |
- |
0.0099 |
100 |
0.1782 |
- |
0.0001 |
1 |
0.2208 |
- |
0.0050 |
50 |
0.1931 |
- |
0.0099 |
100 |
0.1629 |
- |
0.0149 |
150 |
0.2716 |
- |
0.0199 |
200 |
0.18 |
- |
0.0249 |
250 |
0.2504 |
- |
0.0298 |
300 |
0.1936 |
- |
0.0348 |
350 |
0.1764 |
- |
0.0398 |
400 |
0.1817 |
- |
0.0447 |
450 |
0.0624 |
- |
0.0497 |
500 |
0.1183 |
- |
0.0547 |
550 |
0.0793 |
- |
0.0596 |
600 |
0.0281 |
- |
0.0646 |
650 |
0.0876 |
- |
0.0696 |
700 |
0.1701 |
- |
0.0746 |
750 |
0.0468 |
- |
0.0795 |
800 |
0.0525 |
- |
0.0845 |
850 |
0.0783 |
- |
0.0895 |
900 |
0.0342 |
- |
0.0944 |
950 |
0.0158 |
- |
0.0994 |
1000 |
0.0286 |
- |
0.1044 |
1050 |
0.0016 |
- |
0.1094 |
1100 |
0.0014 |
- |
0.1143 |
1150 |
0.0298 |
- |
0.1193 |
1200 |
0.018 |
- |
0.1243 |
1250 |
0.0299 |
- |
0.1292 |
1300 |
0.0019 |
- |
0.1342 |
1350 |
0.0253 |
- |
0.1392 |
1400 |
0.0009 |
- |
0.1441 |
1450 |
0.0009 |
- |
0.1491 |
1500 |
0.0011 |
- |
0.1541 |
1550 |
0.0006 |
- |
0.1591 |
1600 |
0.0006 |
- |
0.1640 |
1650 |
0.0008 |
- |
0.1690 |
1700 |
0.0005 |
- |
0.1740 |
1750 |
0.0007 |
- |
0.1789 |
1800 |
0.0006 |
- |
0.1839 |
1850 |
0.0006 |
- |
0.1889 |
1900 |
0.0006 |
- |
0.1939 |
1950 |
0.0012 |
- |
0.1988 |
2000 |
0.0004 |
- |
0.2038 |
2050 |
0.0006 |
- |
0.2088 |
2100 |
0.0005 |
- |
0.2137 |
2150 |
0.0005 |
- |
0.2187 |
2200 |
0.0005 |
- |
0.2237 |
2250 |
0.0004 |
- |
0.2287 |
2300 |
0.0005 |
- |
0.2336 |
2350 |
0.0004 |
- |
0.2386 |
2400 |
0.0004 |
- |
0.2436 |
2450 |
0.0003 |
- |
0.2485 |
2500 |
0.0004 |
- |
0.2535 |
2550 |
0.0004 |
- |
0.2585 |
2600 |
0.0004 |
- |
0.2634 |
2650 |
0.0004 |
- |
0.2684 |
2700 |
0.0004 |
- |
0.2734 |
2750 |
0.0004 |
- |
0.2784 |
2800 |
0.0056 |
- |
0.2833 |
2850 |
0.0004 |
- |
0.2883 |
2900 |
0.0003 |
- |
0.2933 |
2950 |
0.0003 |
- |
0.2982 |
3000 |
0.0004 |
- |
0.3032 |
3050 |
0.0003 |
- |
0.3082 |
3100 |
0.0003 |
- |
0.3132 |
3150 |
0.0003 |
- |
0.3181 |
3200 |
0.0003 |
- |
0.3231 |
3250 |
0.0004 |
- |
0.3281 |
3300 |
0.0003 |
- |
0.3330 |
3350 |
0.0003 |
- |
0.3380 |
3400 |
0.0003 |
- |
0.3430 |
3450 |
0.0003 |
- |
0.3479 |
3500 |
0.0003 |
- |
0.3529 |
3550 |
0.0003 |
- |
0.3579 |
3600 |
0.0003 |
- |
0.3629 |
3650 |
0.0003 |
- |
0.3678 |
3700 |
0.0003 |
- |
0.3728 |
3750 |
0.0004 |
- |
0.3778 |
3800 |
0.0004 |
- |
0.3827 |
3850 |
0.0003 |
- |
0.3877 |
3900 |
0.0003 |
- |
0.3927 |
3950 |
0.0003 |
- |
0.3977 |
4000 |
0.0003 |
- |
0.4026 |
4050 |
0.0003 |
- |
0.4076 |
4100 |
0.0003 |
- |
0.4126 |
4150 |
0.0003 |
- |
0.4175 |
4200 |
0.0003 |
- |
0.4225 |
4250 |
0.0003 |
- |
0.4275 |
4300 |
0.0003 |
- |
0.4324 |
4350 |
0.0003 |
- |
0.4374 |
4400 |
0.0002 |
- |
0.4424 |
4450 |
0.0003 |
- |
0.4474 |
4500 |
0.0003 |
- |
0.4523 |
4550 |
0.0003 |
- |
0.4573 |
4600 |
0.0003 |
- |
0.4623 |
4650 |
0.0003 |
- |
0.4672 |
4700 |
0.0002 |
- |
0.4722 |
4750 |
0.0002 |
- |
0.4772 |
4800 |
0.0003 |
- |
0.4822 |
4850 |
0.0002 |
- |
0.4871 |
4900 |
0.0002 |
- |
0.4921 |
4950 |
0.0002 |
- |
0.4971 |
5000 |
0.0003 |
- |
0.5020 |
5050 |
0.0003 |
- |
0.5070 |
5100 |
0.0002 |
- |
0.5120 |
5150 |
0.0003 |
- |
0.5169 |
5200 |
0.0002 |
- |
0.5219 |
5250 |
0.0002 |
- |
0.5269 |
5300 |
0.0002 |
- |
0.5319 |
5350 |
0.0002 |
- |
0.5368 |
5400 |
0.0003 |
- |
0.5418 |
5450 |
0.0002 |
- |
0.5468 |
5500 |
0.0002 |
- |
0.5517 |
5550 |
0.0002 |
- |
0.5567 |
5600 |
0.0002 |
- |
0.5617 |
5650 |
0.0002 |
- |
0.5667 |
5700 |
0.0002 |
- |
0.5716 |
5750 |
0.0002 |
- |
0.5766 |
5800 |
0.0002 |
- |
0.5816 |
5850 |
0.0002 |
- |
0.5865 |
5900 |
0.0002 |
- |
0.5915 |
5950 |
0.0002 |
- |
0.5965 |
6000 |
0.0002 |
- |
0.6015 |
6050 |
0.0002 |
- |
0.6064 |
6100 |
0.0002 |
- |
0.6114 |
6150 |
0.0002 |
- |
0.6164 |
6200 |
0.0002 |
- |
0.6213 |
6250 |
0.0002 |
- |
0.6263 |
6300 |
0.0002 |
- |
0.6313 |
6350 |
0.0002 |
- |
0.6362 |
6400 |
0.0002 |
- |
0.6412 |
6450 |
0.0002 |
- |
0.6462 |
6500 |
0.0002 |
- |
0.6512 |
6550 |
0.0002 |
- |
0.6561 |
6600 |
0.0002 |
- |
0.6611 |
6650 |
0.0002 |
- |
0.6661 |
6700 |
0.0002 |
- |
0.6710 |
6750 |
0.0002 |
- |
0.6760 |
6800 |
0.0002 |
- |
0.6810 |
6850 |
0.0002 |
- |
0.6860 |
6900 |
0.0002 |
- |
0.6909 |
6950 |
0.0002 |
- |
0.6959 |
7000 |
0.0002 |
- |
0.7009 |
7050 |
0.0002 |
- |
0.7058 |
7100 |
0.0002 |
- |
0.7108 |
7150 |
0.0002 |
- |
0.7158 |
7200 |
0.0002 |
- |
0.7207 |
7250 |
0.0002 |
- |
0.7257 |
7300 |
0.0002 |
- |
0.7307 |
7350 |
0.0002 |
- |
0.7357 |
7400 |
0.0002 |
- |
0.7406 |
7450 |
0.0002 |
- |
0.7456 |
7500 |
0.0002 |
- |
0.7506 |
7550 |
0.0002 |
- |
0.7555 |
7600 |
0.0002 |
- |
0.7605 |
7650 |
0.0002 |
- |
0.7655 |
7700 |
0.0248 |
- |
0.7705 |
7750 |
0.0002 |
- |
0.7754 |
7800 |
0.0002 |
- |
0.7804 |
7850 |
0.0002 |
- |
0.7854 |
7900 |
0.0002 |
- |
0.7903 |
7950 |
0.0002 |
- |
0.7953 |
8000 |
0.0002 |
- |
0.8003 |
8050 |
0.0002 |
- |
0.8052 |
8100 |
0.0002 |
- |
0.8102 |
8150 |
0.0002 |
- |
0.8152 |
8200 |
0.0002 |
- |
0.8202 |
8250 |
0.0002 |
- |
0.8251 |
8300 |
0.0002 |
- |
0.8301 |
8350 |
0.0002 |
- |
0.8351 |
8400 |
0.0002 |
- |
0.8400 |
8450 |
0.0001 |
- |
0.8450 |
8500 |
0.0002 |
- |
0.8500 |
8550 |
0.0002 |
- |
0.8550 |
8600 |
0.0001 |
- |
0.8599 |
8650 |
0.0002 |
- |
0.8649 |
8700 |
0.0002 |
- |
0.8699 |
8750 |
0.0002 |
- |
0.8748 |
8800 |
0.0002 |
- |
0.8798 |
8850 |
0.0002 |
- |
0.8848 |
8900 |
0.0002 |
- |
0.8898 |
8950 |
0.0003 |
- |
0.8947 |
9000 |
0.0002 |
- |
0.8997 |
9050 |
0.0001 |
- |
0.9047 |
9100 |
0.0002 |
- |
0.9096 |
9150 |
0.0002 |
- |
0.9146 |
9200 |
0.0002 |
- |
0.9196 |
9250 |
0.0002 |
- |
0.9245 |
9300 |
0.0002 |
- |
0.9295 |
9350 |
0.0002 |
- |
0.9345 |
9400 |
0.0002 |
- |
0.9395 |
9450 |
0.0002 |
- |
0.9444 |
9500 |
0.0002 |
- |
0.9494 |
9550 |
0.0001 |
- |
0.9544 |
9600 |
0.0001 |
- |
0.9593 |
9650 |
0.0002 |
- |
0.9643 |
9700 |
0.0002 |
- |
0.9693 |
9750 |
0.0002 |
- |
0.9743 |
9800 |
0.0001 |
- |
0.9792 |
9850 |
0.0002 |
- |
0.9842 |
9900 |
0.0002 |
- |
0.9892 |
9950 |
0.0002 |
- |
0.9941 |
10000 |
0.0002 |
- |
0.9991 |
10050 |
0.0002 |
- |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.0
- Tokenizers: 0.15.1
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}
}