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model_cca_multilabel_mpnet-65max-full-poorf10-artificial

Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
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model_cca_multilabel_mpnet-65max-full-poorf10-artificial / README.md
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metadata
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      Targeted skills audits will identify gaps in the current rural workforce
      and inform training investments.
  - text: >-
      Policy will promote durable partnerships between public research
      institutions, universities, and private sector actors to accelerate the
      translation of agrifood R&D into market-ready technologies that improve
      productivity and resilience.
  - text: >-
      Interoperability across agencies will be enhanced through shared data
      platforms, common data standards, and legally anchored data sharing
      agreements that protect privacy while enabling timely access to agrifood
      data for policy formulation and M&E.
  - text: >-
      Digital surveillance will enable near-real-time anomaly detection using
      machine learning for pattern recognition.
  - text: >-
      The policy will support seed multiplications and farmer-led seed networks
      to ensure access to locally adapted, climate-resilient varieties.
metrics:
  - accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2

SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-mpnet-base-v2 as the Sentence Transformer embedding model. A OneVsRestClassifier 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

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("faodl/model_cca_multilabel_mpnet-65max-full-poorf10-artificial")
# Run inference
preds = model("Targeted skills audits will identify gaps in the current rural workforce and inform training investments.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 7 19.7924 100

Training Hyperparameters

  • batch_size: (8, 8)
  • num_epochs: (1, 1)
  • max_steps: -1
  • sampling_strategy: oversampling
  • num_iterations: 20
  • body_learning_rate: (2e-05, 2e-05)
  • head_learning_rate: 2e-05
  • 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
0.0000 1 0.2267 -
0.0014 50 0.2064 -
0.0029 100 0.2078 -
0.0043 150 0.1999 -
0.0058 200 0.1965 -
0.0072 250 0.1865 -
0.0086 300 0.1831 -
0.0101 350 0.1824 -
0.0115 400 0.1696 -
0.0130 450 0.1635 -
0.0144 500 0.1685 -
0.0158 550 0.1542 -
0.0173 600 0.15 -
0.0187 650 0.1511 -
0.0202 700 0.16 -
0.0216 750 0.1413 -
0.0230 800 0.1363 -
0.0245 850 0.1527 -
0.0259 900 0.1324 -
0.0273 950 0.1274 -
0.0288 1000 0.1526 -
0.0302 1050 0.1182 -
0.0317 1100 0.1327 -
0.0331 1150 0.1291 -
0.0345 1200 0.1285 -
0.0360 1250 0.1196 -
0.0374 1300 0.1265 -
0.0389 1350 0.1167 -
0.0403 1400 0.1144 -
0.0417 1450 0.1347 -
0.0432 1500 0.1258 -
0.0446 1550 0.1332 -
0.0461 1600 0.1128 -
0.0475 1650 0.1168 -
0.0489 1700 0.1203 -
0.0504 1750 0.1042 -
0.0518 1800 0.1182 -
0.0533 1850 0.114 -
0.0547 1900 0.1139 -
0.0561 1950 0.1061 -
0.0576 2000 0.108 -
0.0590 2050 0.115 -
0.0605 2100 0.0995 -
0.0619 2150 0.1053 -
0.0633 2200 0.1227 -
0.0648 2250 0.112 -
0.0662 2300 0.1092 -
0.0677 2350 0.1136 -
0.0691 2400 0.092 -
0.0705 2450 0.099 -
0.0720 2500 0.1091 -
0.0734 2550 0.1192 -
0.0749 2600 0.1148 -
0.0763 2650 0.0921 -
0.0777 2700 0.0917 -
0.0792 2750 0.1148 -
0.0806 2800 0.1055 -
0.0820 2850 0.0943 -
0.0835 2900 0.0926 -
0.0849 2950 0.115 -
0.0864 3000 0.0928 -
0.0878 3050 0.092 -
0.0892 3100 0.0917 -
0.0907 3150 0.1149 -
0.0921 3200 0.1072 -
0.0936 3250 0.0791 -
0.0950 3300 0.0968 -
0.0964 3350 0.1018 -
0.0979 3400 0.1077 -
0.0993 3450 0.0992 -
0.1008 3500 0.0817 -
0.1022 3550 0.0955 -
0.1036 3600 0.0824 -
0.1051 3650 0.0835 -
0.1065 3700 0.101 -
0.1080 3750 0.0913 -
0.1094 3800 0.1014 -
0.1108 3850 0.0849 -
0.1123 3900 0.0855 -
0.1137 3950 0.0802 -
0.1152 4000 0.0904 -
0.1166 4050 0.0767 -
0.1180 4100 0.0829 -
0.1195 4150 0.0912 -
0.1209 4200 0.0788 -
0.1224 4250 0.0861 -
0.1238 4300 0.089 -
0.1252 4350 0.0652 -
0.1267 4400 0.0946 -
0.1281 4450 0.0819 -
0.1296 4500 0.0829 -
0.1310 4550 0.0491 -
0.1324 4600 0.0875 -
0.1339 4650 0.0675 -
0.1353 4700 0.0838 -
0.1367 4750 0.0637 -
0.1382 4800 0.0907 -
0.1396 4850 0.0803 -
0.1411 4900 0.06 -
0.1425 4950 0.0866 -
0.1439 5000 0.0654 -
0.1454 5050 0.0695 -
0.1468 5100 0.0723 -
0.1483 5150 0.0725 -
0.1497 5200 0.0762 -
0.1511 5250 0.0738 -
0.1526 5300 0.0732 -
0.1540 5350 0.0619 -
0.1555 5400 0.0768 -
0.1569 5450 0.0749 -
0.1583 5500 0.083 -
0.1598 5550 0.0638 -
0.1612 5600 0.0651 -
0.1627 5650 0.0633 -
0.1641 5700 0.0639 -
0.1655 5750 0.0615 -
0.1670 5800 0.0684 -
0.1684 5850 0.0539 -
0.1699 5900 0.054 -
0.1713 5950 0.0544 -
0.1727 6000 0.0532 -
0.1742 6050 0.0665 -
0.1756 6100 0.0669 -
0.1771 6150 0.0722 -
0.1785 6200 0.0581 -
0.1799 6250 0.0515 -
0.1814 6300 0.057 -
0.1828 6350 0.0509 -
0.1843 6400 0.0671 -
0.1857 6450 0.0452 -
0.1871 6500 0.0641 -
0.1886 6550 0.0746 -
0.1900 6600 0.0623 -
0.1914 6650 0.0534 -
0.1929 6700 0.0542 -
0.1943 6750 0.0576 -
0.1958 6800 0.0638 -
0.1972 6850 0.0463 -
0.1986 6900 0.0561 -
0.2001 6950 0.0789 -
0.2015 7000 0.0705 -
0.2030 7050 0.0516 -
0.2044 7100 0.0508 -
0.2058 7150 0.0537 -
0.2073 7200 0.0567 -
0.2087 7250 0.05 -
0.2102 7300 0.056 -
0.2116 7350 0.0495 -
0.2130 7400 0.0576 -
0.2145 7450 0.0574 -
0.2159 7500 0.0497 -
0.2174 7550 0.0556 -
0.2188 7600 0.0597 -
0.2202 7650 0.044 -
0.2217 7700 0.0373 -
0.2231 7750 0.0409 -
0.2246 7800 0.0532 -
0.2260 7850 0.0477 -
0.2274 7900 0.0502 -
0.2289 7950 0.0467 -
0.2303 8000 0.0507 -
0.2318 8050 0.0519 -
0.2332 8100 0.0345 -
0.2346 8150 0.052 -
0.2361 8200 0.0439 -
0.2375 8250 0.0446 -
0.2390 8300 0.049 -
0.2404 8350 0.0749 -
0.2418 8400 0.0367 -
0.2433 8450 0.0371 -
0.2447 8500 0.0631 -
0.2461 8550 0.0451 -
0.2476 8600 0.0405 -
0.2490 8650 0.0403 -
0.2505 8700 0.0501 -
0.2519 8750 0.046 -
0.2533 8800 0.0431 -
0.2548 8850 0.0474 -
0.2562 8900 0.0444 -
0.2577 8950 0.0288 -
0.2591 9000 0.0527 -
0.2605 9050 0.0434 -
0.2620 9100 0.0423 -
0.2634 9150 0.0554 -
0.2649 9200 0.0419 -
0.2663 9250 0.0465 -
0.2677 9300 0.0398 -
0.2692 9350 0.0448 -
0.2706 9400 0.0338 -
0.2721 9450 0.0545 -
0.2735 9500 0.0417 -
0.2749 9550 0.0401 -
0.2764 9600 0.0452 -
0.2778 9650 0.0403 -
0.2793 9700 0.0374 -
0.2807 9750 0.0547 -
0.2821 9800 0.0401 -
0.2836 9850 0.0381 -
0.2850 9900 0.0396 -
0.2865 9950 0.0482 -
0.2879 10000 0.0406 -
0.2893 10050 0.0454 -
0.2908 10100 0.0274 -
0.2922 10150 0.0324 -
0.2937 10200 0.0466 -
0.2951 10250 0.0322 -
0.2965 10300 0.0479 -
0.2980 10350 0.0414 -
0.2994 10400 0.0374 -
0.3008 10450 0.0383 -
0.3023 10500 0.0475 -
0.3037 10550 0.0327 -
0.3052 10600 0.0448 -
0.3066 10650 0.0507 -
0.3080 10700 0.0299 -
0.3095 10750 0.0346 -
0.3109 10800 0.0317 -
0.3124 10850 0.033 -
0.3138 10900 0.0351 -
0.3152 10950 0.0324 -
0.3167 11000 0.0401 -
0.3181 11050 0.0308 -
0.3196 11100 0.0314 -
0.3210 11150 0.0317 -
0.3224 11200 0.0352 -
0.3239 11250 0.0314 -
0.3253 11300 0.0278 -
0.3268 11350 0.0413 -
0.3282 11400 0.0272 -
0.3296 11450 0.0424 -
0.3311 11500 0.0316 -
0.3325 11550 0.0351 -
0.3340 11600 0.0332 -
0.3354 11650 0.0295 -
0.3368 11700 0.0251 -
0.3383 11750 0.027 -
0.3397 11800 0.0306 -
0.3412 11850 0.0332 -
0.3426 11900 0.0308 -
0.3440 11950 0.0269 -
0.3455 12000 0.0354 -
0.3469 12050 0.0231 -
0.3484 12100 0.0341 -
0.3498 12150 0.0299 -
0.3512 12200 0.0224 -
0.3527 12250 0.0238 -
0.3541 12300 0.026 -
0.3555 12350 0.0336 -
0.3570 12400 0.0366 -
0.3584 12450 0.0305 -
0.3599 12500 0.0362 -
0.3613 12550 0.0202 -
0.3627 12600 0.0219 -
0.3642 12650 0.021 -
0.3656 12700 0.0395 -
0.3671 12750 0.031 -
0.3685 12800 0.0234 -
0.3699 12850 0.0374 -
0.3714 12900 0.0214 -
0.3728 12950 0.0307 -
0.3743 13000 0.0283 -
0.3757 13050 0.0284 -
0.3771 13100 0.0311 -
0.3786 13150 0.0206 -
0.3800 13200 0.0322 -
0.3815 13250 0.0255 -
0.3829 13300 0.0275 -
0.3843 13350 0.0301 -
0.3858 13400 0.0366 -
0.3872 13450 0.033 -
0.3887 13500 0.0159 -
0.3901 13550 0.0327 -
0.3915 13600 0.0229 -
0.3930 13650 0.0333 -
0.3944 13700 0.0192 -
0.3959 13750 0.0272 -
0.3973 13800 0.0173 -
0.3987 13850 0.0257 -
0.4002 13900 0.0187 -
0.4016 13950 0.0235 -
0.4031 14000 0.0223 -
0.4045 14050 0.0212 -
0.4059 14100 0.0235 -
0.4074 14150 0.0268 -
0.4088 14200 0.0282 -
0.4102 14250 0.0211 -
0.4117 14300 0.0207 -
0.4131 14350 0.0175 -
0.4146 14400 0.0267 -
0.4160 14450 0.0246 -
0.4174 14500 0.0266 -
0.4189 14550 0.021 -
0.4203 14600 0.028 -
0.4218 14650 0.0229 -
0.4232 14700 0.0216 -
0.4246 14750 0.04 -
0.4261 14800 0.0233 -
0.4275 14850 0.0256 -
0.4290 14900 0.0216 -
0.4304 14950 0.0296 -
0.4318 15000 0.0168 -
0.4333 15050 0.0215 -
0.4347 15100 0.0135 -
0.4362 15150 0.0158 -
0.4376 15200 0.02 -
0.4390 15250 0.0302 -
0.4405 15300 0.0242 -
0.4419 15350 0.0255 -
0.4434 15400 0.0145 -
0.4448 15450 0.0161 -
0.4462 15500 0.0238 -
0.4477 15550 0.0083 -
0.4491 15600 0.0213 -
0.4506 15650 0.0241 -
0.4520 15700 0.0253 -
0.4534 15750 0.0196 -
0.4549 15800 0.0285 -
0.4563 15850 0.0225 -
0.4578 15900 0.0262 -
0.4592 15950 0.017 -
0.4606 16000 0.0251 -
0.4621 16050 0.0212 -
0.4635 16100 0.023 -
0.4649 16150 0.0173 -
0.4664 16200 0.0355 -
0.4678 16250 0.0205 -
0.4693 16300 0.0114 -
0.4707 16350 0.0157 -
0.4721 16400 0.0304 -
0.4736 16450 0.0163 -
0.4750 16500 0.0208 -
0.4765 16550 0.0124 -
0.4779 16600 0.0327 -
0.4793 16650 0.0228 -
0.4808 16700 0.0161 -
0.4822 16750 0.0217 -
0.4837 16800 0.0151 -
0.4851 16850 0.0255 -
0.4865 16900 0.0283 -
0.4880 16950 0.0192 -
0.4894 17000 0.0217 -
0.4909 17050 0.02 -
0.4923 17100 0.0296 -
0.4937 17150 0.0263 -
0.4952 17200 0.0196 -
0.4966 17250 0.019 -
0.4981 17300 0.0185 -
0.4995 17350 0.018 -
0.5009 17400 0.0146 -
0.5024 17450 0.0144 -
0.5038 17500 0.0143 -
0.5053 17550 0.0179 -
0.5067 17600 0.0213 -
0.5081 17650 0.022 -
0.5096 17700 0.0136 -
0.5110 17750 0.012 -
0.5125 17800 0.0148 -
0.5139 17850 0.0189 -
0.5153 17900 0.0209 -
0.5168 17950 0.0191 -
0.5182 18000 0.0155 -
0.5196 18050 0.0223 -
0.5211 18100 0.0172 -
0.5225 18150 0.0147 -
0.5240 18200 0.0205 -
0.5254 18250 0.0196 -
0.5268 18300 0.018 -
0.5283 18350 0.0123 -
0.5297 18400 0.0146 -
0.5312 18450 0.0154 -
0.5326 18500 0.0099 -
0.5340 18550 0.0113 -
0.5355 18600 0.0191 -
0.5369 18650 0.0161 -
0.5384 18700 0.0113 -
0.5398 18750 0.0236 -
0.5412 18800 0.021 -
0.5427 18850 0.0107 -
0.5441 18900 0.021 -
0.5456 18950 0.0213 -
0.5470 19000 0.028 -
0.5484 19050 0.0164 -
0.5499 19100 0.0197 -
0.5513 19150 0.0074 -
0.5528 19200 0.0108 -
0.5542 19250 0.0118 -
0.5556 19300 0.013 -
0.5571 19350 0.0215 -
0.5585 19400 0.0124 -
0.5600 19450 0.0163 -
0.5614 19500 0.01 -
0.5628 19550 0.0188 -
0.5643 19600 0.019 -
0.5657 19650 0.0075 -
0.5672 19700 0.0168 -
0.5686 19750 0.0073 -
0.5700 19800 0.0151 -
0.5715 19850 0.0236 -
0.5729 19900 0.0197 -
0.5743 19950 0.0207 -
0.5758 20000 0.0106 -
0.5772 20050 0.0137 -
0.5787 20100 0.0155 -
0.5801 20150 0.0118 -
0.5815 20200 0.0231 -
0.5830 20250 0.0186 -
0.5844 20300 0.0139 -
0.5859 20350 0.0183 -
0.5873 20400 0.0136 -
0.5887 20450 0.0139 -
0.5902 20500 0.0131 -
0.5916 20550 0.014 -
0.5931 20600 0.021 -
0.5945 20650 0.0172 -
0.5959 20700 0.016 -
0.5974 20750 0.0136 -
0.5988 20800 0.0144 -
0.6003 20850 0.0142 -
0.6017 20900 0.0148 -
0.6031 20950 0.0197 -
0.6046 21000 0.0081 -
0.6060 21050 0.0088 -
0.6075 21100 0.0216 -
0.6089 21150 0.0231 -
0.6103 21200 0.0182 -
0.6118 21250 0.0132 -
0.6132 21300 0.0104 -
0.6147 21350 0.0107 -
0.6161 21400 0.0051 -
0.6175 21450 0.0131 -
0.6190 21500 0.0118 -
0.6204 21550 0.0122 -
0.6219 21600 0.0154 -
0.6233 21650 0.0138 -
0.6247 21700 0.0197 -
0.6262 21750 0.0159 -
0.6276 21800 0.0101 -
0.6290 21850 0.0105 -
0.6305 21900 0.0108 -
0.6319 21950 0.0098 -
0.6334 22000 0.013 -
0.6348 22050 0.0188 -
0.6362 22100 0.008 -
0.6377 22150 0.0159 -
0.6391 22200 0.0211 -
0.6406 22250 0.0128 -
0.6420 22300 0.0136 -
0.6434 22350 0.0152 -
0.6449 22400 0.0105 -
0.6463 22450 0.0129 -
0.6478 22500 0.0119 -
0.6492 22550 0.0177 -
0.6506 22600 0.0085 -
0.6521 22650 0.0119 -
0.6535 22700 0.0033 -
0.6550 22750 0.0115 -
0.6564 22800 0.0068 -
0.6578 22850 0.0241 -
0.6593 22900 0.0135 -
0.6607 22950 0.0134 -
0.6622 23000 0.0109 -
0.6636 23050 0.0151 -
0.6650 23100 0.0106 -
0.6665 23150 0.0125 -
0.6679 23200 0.007 -
0.6694 23250 0.0171 -
0.6708 23300 0.0108 -
0.6722 23350 0.0163 -
0.6737 23400 0.0196 -
0.6751 23450 0.0054 -
0.6766 23500 0.0068 -
0.6780 23550 0.0157 -
0.6794 23600 0.0183 -
0.6809 23650 0.0153 -
0.6823 23700 0.0143 -
0.6837 23750 0.0072 -
0.6852 23800 0.0168 -
0.6866 23850 0.0157 -
0.6881 23900 0.0056 -
0.6895 23950 0.0196 -
0.6909 24000 0.0094 -
0.6924 24050 0.0107 -
0.6938 24100 0.0177 -
0.6953 24150 0.0143 -
0.6967 24200 0.0088 -
0.6981 24250 0.0148 -
0.6996 24300 0.0171 -
0.7010 24350 0.0079 -
0.7025 24400 0.0171 -
0.7039 24450 0.0161 -
0.7053 24500 0.0066 -
0.7068 24550 0.0142 -
0.7082 24600 0.0139 -
0.7097 24650 0.0122 -
0.7111 24700 0.0188 -
0.7125 24750 0.008 -
0.7140 24800 0.0142 -
0.7154 24850 0.0114 -
0.7169 24900 0.0104 -
0.7183 24950 0.0204 -
0.7197 25000 0.0137 -
0.7212 25050 0.0096 -
0.7226 25100 0.0075 -
0.7241 25150 0.0143 -
0.7255 25200 0.0095 -
0.7269 25250 0.0068 -
0.7284 25300 0.0092 -
0.7298 25350 0.01 -
0.7313 25400 0.0064 -
0.7327 25450 0.0066 -
0.7341 25500 0.023 -
0.7356 25550 0.0137 -
0.7370 25600 0.0062 -
0.7384 25650 0.0105 -
0.7399 25700 0.0043 -
0.7413 25750 0.0137 -
0.7428 25800 0.0097 -
0.7442 25850 0.0124 -
0.7456 25900 0.0112 -
0.7471 25950 0.0101 -
0.7485 26000 0.0149 -
0.7500 26050 0.0111 -
0.7514 26100 0.006 -
0.7528 26150 0.0126 -
0.7543 26200 0.0122 -
0.7557 26250 0.0049 -
0.7572 26300 0.0126 -
0.7586 26350 0.0133 -
0.7600 26400 0.0035 -
0.7615 26450 0.018 -
0.7629 26500 0.0175 -
0.7644 26550 0.0068 -
0.7658 26600 0.0079 -
0.7672 26650 0.0084 -
0.7687 26700 0.014 -
0.7701 26750 0.0113 -
0.7716 26800 0.0153 -
0.7730 26850 0.0251 -
0.7744 26900 0.0102 -
0.7759 26950 0.0135 -
0.7773 27000 0.0079 -
0.7788 27050 0.0081 -
0.7802 27100 0.0055 -
0.7816 27150 0.0014 -
0.7831 27200 0.0134 -
0.7845 27250 0.0058 -
0.7860 27300 0.0071 -
0.7874 27350 0.0045 -
0.7888 27400 0.0067 -
0.7903 27450 0.0125 -
0.7917 27500 0.0094 -
0.7931 27550 0.0129 -
0.7946 27600 0.0096 -
0.7960 27650 0.0032 -
0.7975 27700 0.0061 -
0.7989 27750 0.0054 -
0.8003 27800 0.0121 -
0.8018 27850 0.0124 -
0.8032 27900 0.0065 -
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Framework Versions

  • Python: 3.12.12
  • SetFit: 1.1.3
  • Sentence Transformers: 5.1.2
  • Transformers: 4.57.1
  • PyTorch: 2.8.0+cu126
  • Datasets: 4.0.0
  • Tokenizers: 0.22.1

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}
}