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fefofico
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crisis_trained

Text Classification
setfit
Safetensors
sentence-transformers
qwen3
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
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SetFit with microsoft/harrier-oss-v1-0.6b

This is a SetFit model that can be used for Text Classification. This SetFit model uses microsoft/harrier-oss-v1-0.6b 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:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
positive
  • 'The 1980s Latin American debt crisis revealed the dangers of predatory lending, a problem that persists today in the form of sovereign debt traps.'
  • 'The widespread burnout among health workers is now a workforce crisis.'
  • 'The melting of permafrost in the Arctic is releasing vast amounts of methane, accelerating the pace of global warming.'
negative
  • 'Crisis: The Battle for Europe is a wargame where players must navigate the complex alliances and betrayals of World War I.'
  • 'With enhanced disaster preparedness exercises, emergency responders are improving coordination across agencies.'
  • 'The department is conducting a review of its environmental policies.'

Evaluation

Metrics

Label F1_Macro F1_Binary
all 0.9814 0.9795

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

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("fefofico/crisis_trained")
# Run inference
preds = model("The startup’s leadership is under intense strain after the product failed to gain users.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 5 16.7702 37
Label Training Sample Count
negative 973
positive 768

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (10, 10)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 20
  • body_learning_rate: (2e-05, 2e-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
  • l2_weight: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: False

Training Results

Epoch Step Training Loss Validation Loss
0.0002 1 0.2579 -
0.0046 20 0.244 -
0.0092 40 0.2415 -
0.0138 60 0.2461 -
0.0184 80 0.2425 -
0.0230 100 0.2317 -
0.0276 120 0.2398 -
0.0322 140 0.2295 -
0.0368 160 0.2279 -
0.0414 180 0.214 -
0.0459 200 0.1889 -
0.0505 220 0.1617 -
0.0551 240 0.0965 -
0.0597 260 0.0372 -
0.0643 280 0.0179 -
0.0689 300 0.0047 -
0.0735 320 0.0046 -
0.0781 340 0.0043 -
0.0827 360 0.0038 -
0.0873 380 0.0018 -
0.0919 400 0.0006 -
0.0965 420 0.0013 -
0.1011 440 0.0004 -
0.1057 460 0.0004 -
0.1103 480 0.0003 -
0.1149 500 0.0003 -
0.1195 520 0.0002 -
0.1241 540 0.0002 -
0.1286 560 0.0002 -
0.1332 580 0.0002 -
0.1378 600 0.0002 -
0.1424 620 0.0002 -
0.1470 640 0.0001 -
0.1516 660 0.0002 -
0.1562 680 0.0001 -
0.1608 700 0.0001 -
0.1654 720 0.0001 -
0.1700 740 0.0001 -
0.1746 760 0.0001 -
0.1792 780 0.0001 -
0.1838 800 0.0001 -
0.1884 820 0.0001 -
0.1930 840 0.0001 -
0.1976 860 0.0001 -
0.2022 880 0.0001 -
0.2068 900 0.0001 -
0.2113 920 0.0001 -
0.2159 940 0.0 -
0.2205 960 0.0 -
0.2251 980 0.0 -
0.2297 1000 0.0 -
0.2343 1020 0.0 -
0.2389 1040 0.0 -
0.2435 1060 0.0 -
0.2481 1080 0.0 -
0.2527 1100 0.0 -
0.2573 1120 0.0 -
0.2619 1140 0.0 -
0.2665 1160 0.0 -
0.2711 1180 0.0 -
0.2757 1200 0.0 -
0.2803 1220 0.0 -
0.2849 1240 0.0 -
0.2895 1260 0.0 -
0.2941 1280 0.0 -
0.2986 1300 0.0 -
0.3032 1320 0.0 -
0.3078 1340 0.0 -
0.3124 1360 0.0 -
0.3170 1380 0.0 -
0.3216 1400 0.0 -
0.3262 1420 0.0 -
0.3308 1440 0.0 -
0.3354 1460 0.0 -
0.3400 1480 0.0 -
0.3446 1500 0.0 -
0.3492 1520 0.0 -
0.3538 1540 0.0 -
0.3584 1560 0.0 -
0.3630 1580 0.0 -
0.3676 1600 0.0 -
0.3722 1620 0.0 -
0.3768 1640 0.0 -
0.3813 1660 0.0 -
0.3859 1680 0.0 -
0.3905 1700 0.0 -
0.3951 1720 0.0 -
0.3997 1740 0.0 -
0.4043 1760 0.0 -
0.4089 1780 0.0 -
0.4135 1800 0.0 -
0.4181 1820 0.0 -
0.4227 1840 0.0 -
0.4273 1860 0.0 -
0.4319 1880 0.0 -
0.4365 1900 0.0 -
0.4411 1920 0.0 -
0.4457 1940 0.0 -
0.4503 1960 0.0 -
0.4549 1980 0.0 -
0.4595 2000 0.0 -
0.4640 2020 0.0 -
0.4686 2040 0.0 -
0.4732 2060 0.0 -
0.4778 2080 0.0 -
0.4824 2100 0.0 -
0.4870 2120 0.0 -
0.4916 2140 0.0 -
0.4962 2160 0.0 -
0.5008 2180 0.0 -
0.5054 2200 0.0 -
0.5100 2220 0.0 -
0.5146 2240 0.0 -
0.5192 2260 0.0 -
0.5238 2280 0.0 -
0.5284 2300 0.0 -
0.5330 2320 0.0 -
0.5376 2340 0.0 -
0.5422 2360 0.0 -
0.5467 2380 0.0 -
0.5513 2400 0.0 -
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0.5743 2500 0.0 -
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0.5973 2600 0.0 -
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0.6065 2640 0.0 -
0.6111 2660 0.0 -
0.6157 2680 0.0 -
0.6203 2700 0.0 -
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0.6295 2740 0.0 -
0.6340 2760 0.0 -
0.6386 2780 0.0 -
0.6432 2800 0.0 -
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0.6570 2860 0.0 -
0.6616 2880 0.0 -
0.6662 2900 0.0 -
0.6708 2920 0.0 -
0.6754 2940 0.0 -
0.6800 2960 0.0 -
0.6846 2980 0.0 -
0.6892 3000 0.0 -
0.6938 3020 0.0 -
0.6984 3040 0.0 -
0.7030 3060 0.0 -
0.7076 3080 0.0 -
0.7122 3100 0.0 -
0.7167 3120 0.0 -
0.7213 3140 0.0 -
0.7259 3160 0.0 -
0.7305 3180 0.0 -
0.7351 3200 0.0 -
0.7397 3220 0.0 -
0.7443 3240 0.0 -
0.7489 3260 0.0 -
0.7535 3280 0.0 -
0.7581 3300 0.0 -
0.7627 3320 0.0 -
0.7673 3340 0.0 -
0.7719 3360 0.0 -
0.7765 3380 0.0 -
0.7811 3400 0.0 -
0.7857 3420 0.0 -
0.7903 3440 0.0 -
0.7949 3460 0.0 -
0.7994 3480 0.0 -
0.8040 3500 0.0 -
0.8086 3520 0.0 -
0.8132 3540 0.0 -
0.8178 3560 0.0 -
0.8224 3580 0.0 -
0.8270 3600 0.0 -
0.8316 3620 0.0 -
0.8362 3640 0.0 -
0.8408 3660 0.0 -
0.8454 3680 0.0 -
0.8500 3700 0.0 -
0.8546 3720 0.0 -
0.8592 3740 0.0 -
0.8638 3760 0.0 -
0.8684 3780 0.0 -
0.8730 3800 0.0 -
0.8776 3820 0.0 -
0.8822 3840 0.0 -
0.8867 3860 0.0 -
0.8913 3880 0.0 -
0.8959 3900 0.0 -
0.9005 3920 0.0 -
0.9051 3940 0.0 -
0.9097 3960 0.0 -
0.9143 3980 0.0 -
0.9189 4000 0.0 -
0.9235 4020 0.0 -
0.9281 4040 0.0 -
0.9327 4060 0.0 -
0.9373 4080 0.0 -
0.9419 4100 0.0 -
0.9465 4120 0.0 -
0.9511 4140 0.0 -
0.9557 4160 0.0 -
0.9603 4180 0.0 -
0.9649 4200 0.0 -
0.9694 4220 0.0 -
0.9740 4240 0.0 -
0.9786 4260 0.0 -
0.9832 4280 0.0 -
0.9878 4300 0.0 -
0.9924 4320 0.0 -
0.9970 4340 0.0 -
1.0 4353 - 0.0050
1.0016 4360 0.0 -
1.0062 4380 0.0 -
1.0108 4400 0.0 -
1.0154 4420 0.0 -
1.0200 4440 0.0 -
1.0246 4460 0.0 -
1.0292 4480 0.0 -
1.0338 4500 0.0 -
1.0384 4520 0.0 -
1.0430 4540 0.0 -
1.0476 4560 0.0 -
1.0521 4580 0.0 -
1.0567 4600 0.0 -
1.0613 4620 0.0 -
1.0659 4640 0.0 -
1.0705 4660 0.0 -
1.0751 4680 0.0 -
1.0797 4700 0.0 -
1.0843 4720 0.0 -
1.0889 4740 0.0 -
1.0935 4760 0.0 -
1.0981 4780 0.0 -
1.1027 4800 0.0 -
1.1073 4820 0.0 -
1.1119 4840 0.0 -
1.1165 4860 0.0 -
1.1211 4880 0.0 -
1.1257 4900 0.0 -
1.1303 4920 0.0 -
1.1348 4940 0.0 -
1.1394 4960 0.0 -
1.1440 4980 0.0 -
1.1486 5000 0.0 -
1.1532 5020 0.0 -
1.1578 5040 0.0 -
1.1624 5060 0.0 -
1.1670 5080 0.0 -
1.1716 5100 0.0 -
1.1762 5120 0.0 -
1.1808 5140 0.0 -
1.1854 5160 0.0 -
1.1900 5180 0.0 -
1.1946 5200 0.0 -
1.1992 5220 0.0 -
1.2038 5240 0.0 -
1.2084 5260 0.0 -
1.2130 5280 0.0 -
1.2176 5300 0.0 -
1.2221 5320 0.0 -
1.2267 5340 0.0 -
1.2313 5360 0.0 -
1.2359 5380 0.0 -
1.2405 5400 0.0 -
1.2451 5420 0.0 -
1.2497 5440 0.0 -
1.2543 5460 0.0 -
1.2589 5480 0.0 -
1.2635 5500 0.0 -
1.2681 5520 0.0 -
1.2727 5540 0.0 -
1.2773 5560 0.0 -
1.2819 5580 0.0 -
1.2865 5600 0.0 -
1.2911 5620 0.0 -
1.2957 5640 0.0 -
1.3003 5660 0.0 -
1.3048 5680 0.0 -
1.3094 5700 0.0 -
1.3140 5720 0.0 -
1.3186 5740 0.0 -
1.3232 5760 0.0 -
1.3278 5780 0.0 -
1.3324 5800 0.0 -
1.3370 5820 0.0 -
1.3416 5840 0.0 -
1.3462 5860 0.0 -
1.3508 5880 0.0 -
1.3554 5900 0.0 -
1.3600 5920 0.0 -
1.3646 5940 0.0 -
1.3692 5960 0.0 -
1.3738 5980 0.0 -
1.3784 6000 0.0 -
1.3830 6020 0.0 -
1.3875 6040 0.0 -
1.3921 6060 0.0 -
1.3967 6080 0.0 -
1.4013 6100 0.0 -
1.4059 6120 0.0 -
1.4105 6140 0.0 -
1.4151 6160 0.0 -
1.4197 6180 0.0 -
1.4243 6200 0.0 -
1.4289 6220 0.0 -
1.4335 6240 0.0 -
1.4381 6260 0.0 -
1.4427 6280 0.0 -
1.4473 6300 0.0 -
1.4519 6320 0.0 -
1.4565 6340 0.0 -
1.4611 6360 0.0 -
1.4657 6380 0.0 -
1.4703 6400 0.0 -
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1.4840 6460 0.0 -
1.4886 6480 0.0 -
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1.5116 6580 0.0 -
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1.5208 6620 0.0 -
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1.5346 6680 0.0 -
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1.5530 6760 0.0 -
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1.6035 6980 0.0 -
1.6081 7000 0.0 -
1.6127 7020 0.0 -
1.6173 7040 0.0 -
1.6219 7060 0.0 -
1.6265 7080 0.0 -
1.6311 7100 0.0 -
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1.6448 7160 0.0 -
1.6494 7180 0.0 -
1.6540 7200 0.0 -
1.6586 7220 0.0 -
1.6632 7240 0.0 -
1.6678 7260 0.0 -
1.6724 7280 0.0 -
1.6770 7300 0.0 -
1.6816 7320 0.0 -
1.6862 7340 0.0 -
1.6908 7360 0.0 -
1.6954 7380 0.0 -
1.7000 7400 0.0 -
1.7046 7420 0.0 -
1.7092 7440 0.0 -
1.7138 7460 0.0 -
1.7184 7480 0.0 -
1.7229 7500 0.0 -
1.7275 7520 0.0 -
1.7321 7540 0.0 -
1.7367 7560 0.0 -
1.7413 7580 0.0 -
1.7459 7600 0.0 -
1.7505 7620 0.0 -
1.7551 7640 0.0 -
1.7597 7660 0.0 -
1.7643 7680 0.0 -
1.7689 7700 0.0 -
1.7735 7720 0.0001 -
1.7781 7740 0.0001 -
1.7827 7760 0.0 -
1.7873 7780 0.0 -
1.7919 7800 0.0 -
1.7965 7820 0.0 -
1.8011 7840 0.0 -
1.8057 7860 0.0 -
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1.8240 7940 0.0 -
1.8286 7960 0.0 -
1.8332 7980 0.0 -
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1.9848 8640 0.0 -
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1.9940 8680 0.0 -
1.9986 8700 0.0 -
2.0 8706 - 0.0051
2.0032 8720 0.0 -
2.0078 8740 0.0 -
2.0124 8760 0.0 -
2.0170 8780 0.0 -
2.0216 8800 0.0 -
2.0262 8820 0.0 -
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2.0354 8860 0.0 -
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2.6510 11540 0.0 -
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2.6924 11720 0.0775 -
2.6970 11740 0.088 -
2.7016 11760 0.0338 -
2.7062 11780 0.0416 -
2.7108 11800 0.03 -
2.7154 11820 0.0141 -
2.7200 11840 0.0278 -
2.7246 11860 0.0073 -
2.7292 11880 0.0161 -
2.7337 11900 0.0037 -
2.7383 11920 0.0101 -
2.7429 11940 0.0059 -
2.7475 11960 0.0032 -
2.7521 11980 0.0001 -
2.7567 12000 0.0135 -
2.7613 12020 0.0044 -
2.7659 12040 0.0003 -
2.7705 12060 0.0048 -
2.7751 12080 0.0002 -
2.7797 12100 0.0 -
2.7843 12120 0.0012 -
2.7889 12140 0.0028 -
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2.8164 12260 0.0 -
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2.8256 12300 0.0 -
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Framework Versions

  • Python: 3.12.13
  • SetFit: 1.1.3
  • Sentence Transformers: 3.4.1
  • Transformers: 4.57.6
  • PyTorch: 2.10.0+cu128
  • Datasets: 4.8.5
  • Tokenizers: 0.22.2

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}
}
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