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thegenerativegeneration
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stay_or_go_conversation_classifier_s

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
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SetFit with intfloat/multilingual-e5-small

This is a SetFit model that can be used for Text Classification. This SetFit model uses intfloat/multilingual-e5-small as the Sentence Transformer embedding model. A SetFitHead 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
1
  • 'query: ਚੰਗਾ ਜੀ, ਫਿਰ ਮਿਲਦੇ ਹਾਂ.'
  • 'query: Agur, gero arte.'
  • "query: Me'n vaig ara."
0
  • 'query: Dobro, hvala. Kaj pa ti?'
  • 'query: हाँ अगली बार जब तुम जाओ मुझे भी ले चलो मुझे भी प्रकृति में और गतिविधियाँ करनी हैं'
  • 'query: Mirë, faleminderit. Po ju?'

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("setfit_model_id")
# Run inference
preds = model("query: Ναι, ας πάμε!")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 7.4364 21
Label Training Sample Count
0 292
1 290

Training Hyperparameters

  • batch_size: (16, 2)
  • num_epochs: (1, 16)
  • max_steps: -1
  • sampling_strategy: undersampling
  • body_learning_rate: (1e-05, 1e-05)
  • head_learning_rate: 0.001
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.1
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • seed: 42
  • run_name: intfloat/multilingual-e5-small
  • eval_max_steps: -1
  • load_best_model_at_end: True

Training Results

Epoch Step Training Loss Validation Loss
0.0001 1 0.3645 -
0.0047 50 0.3527 -
0.0094 100 0.3424 0.3165
0.0142 150 0.3108 -
0.0189 200 0.2684 0.2215
0.0236 250 0.2197 -
0.0283 300 0.1707 0.1792
0.0331 350 0.1501 -
0.0378 400 0.0865 0.1607
0.0425 450 0.0534 -
0.0472 500 0.0307 0.1519
0.0520 550 0.0342 -
0.0567 600 0.0078 0.1478
0.0614 650 0.0144 -
0.0661 700 0.0658 0.1399
0.0709 750 0.0021 -
0.0756 800 0.0009 0.1512
0.0803 850 0.0005 -
0.0850 900 0.0018 0.1516
0.0897 950 0.0011 -
0.0945 1000 0.0012 0.1541
0.0992 1050 0.0003 -
0.1039 1100 0.0003 0.1415
0.1086 1150 0.0003 -
0.1134 1200 0.0002 0.1442
0.1181 1250 0.0006 -
0.1228 1300 0.0002 0.1298
0.1275 1350 0.0002 -
0.1323 1400 0.0001 0.1356
0.1370 1450 0.0002 -
0.1417 1500 0.0003 0.1493
0.1464 1550 0.0003 -
0.1512 1600 0.0002 0.15
0.1559 1650 0.0002 -
0.1606 1700 0.0003 0.1469
0.1653 1750 0.0001 -
0.1701 1800 0.0001 0.1554
0.1748 1850 0.0002 -
0.1795 1900 0.0001 0.168
0.1842 1950 0.0001 -
0.1889 2000 0.0004 0.1568
0.1937 2050 0.0001 -
0.1984 2100 0.0001 0.1513
0.2031 2150 0.0001 -
0.2078 2200 0.0003 0.1503
0.2126 2250 0.0002 -
0.2173 2300 0.0604 0.155
0.2220 2350 0.0001 -
0.2267 2400 0.0002 0.1739
0.2315 2450 0.0006 -
0.2362 2500 0.0002 0.1558
0.2409 2550 0.0002 -
0.2456 2600 0.0001 0.1393
0.2504 2650 0.0004 -
0.2551 2700 0.0003 0.1642
0.2598 2750 0.0002 -
0.2645 2800 0.0002 0.1776
0.2692 2850 0.0 -
0.2740 2900 0.0002 0.1794
0.2787 2950 0.0001 -
0.2834 3000 0.0001 0.183
0.2881 3050 0.0001 -
0.2929 3100 0.0001 0.1805
0.2976 3150 0.0001 -
0.3023 3200 0.0001 0.1757
0.3070 3250 0.0001 -
0.3118 3300 0.0001 0.1302
0.3165 3350 0.0001 -
0.3212 3400 0.0001 0.1348
0.3259 3450 0.0001 -
0.3307 3500 0.0005 0.1623
0.3354 3550 0.0 -
0.3401 3600 0.0 0.1286
0.3448 3650 0.0 -
0.3496 3700 0.0001 0.1736
0.3543 3750 0.0 -
0.3590 3800 0.0 0.127
0.3637 3850 0.0 -
0.3684 3900 0.0001 0.1231
0.3732 3950 0.0 -
0.3779 4000 0.0001 0.1261
0.3826 4050 0.0001 -
0.3873 4100 0.0 0.1216
0.3921 4150 0.0 -
0.3968 4200 0.0 0.1404
0.4015 4250 0.0 -
0.4062 4300 0.0 0.1466
0.4110 4350 0.0 -
0.4157 4400 0.0 0.1482
0.4204 4450 0.0 -
0.4251 4500 0.0 0.1547
0.4299 4550 0.0 -
0.4346 4600 0.0 0.1566
0.4393 4650 0.0 -
0.4440 4700 0.0 0.1684
0.4487 4750 0.0 -
0.4535 4800 0.0 0.1746
0.4582 4850 0.0 -
0.4629 4900 0.0 0.167
0.4676 4950 0.0 -
0.4724 5000 0.0001 0.1683
0.4771 5050 0.0 -
0.4818 5100 0.0 0.1693
0.4865 5150 0.0 -
0.4913 5200 0.0 0.1694
0.4960 5250 0.0 -
0.5007 5300 0.0 0.162
0.5054 5350 0.0 -
0.5102 5400 0.0 0.1388
0.5149 5450 0.0 -
0.5196 5500 0.0 0.1353
0.5243 5550 0.0 -
0.5291 5600 0.0 0.1401
0.5338 5650 0.0 -
0.5385 5700 0.0 0.1466
0.5432 5750 0.0 -
0.5479 5800 0.0 0.1529
0.5527 5850 0.0 -
0.5574 5900 0.0 0.1488
0.5621 5950 0.0 -
0.5668 6000 0.0 0.147
0.5716 6050 0.0 -
0.5763 6100 0.0 0.1493
0.5810 6150 0.0 -
0.5857 6200 0.0 0.1525
0.5905 6250 0.0 -
0.5952 6300 0.0 0.1505
0.5999 6350 0.0 -
0.6046 6400 0.0 0.1554
0.6094 6450 0.0 -
0.6141 6500 0.0 0.1546
0.6188 6550 0.0 -
0.6235 6600 0.0 0.1598
0.6282 6650 0.0 -
0.6330 6700 0.0 0.179
0.6377 6750 0.0 -
0.6424 6800 0.0 0.1719
0.6471 6850 0.0001 -
0.6519 6900 0.0 0.1812
0.6566 6950 0.0 -
0.6613 7000 0.0 0.1648
0.6660 7050 0.0 -
0.6708 7100 0.0 0.1717
0.6755 7150 0.0 -
0.6802 7200 0.0 0.1793
0.6849 7250 0.0 -
0.6897 7300 0.0 0.1766
0.6944 7350 0.0 -
0.6991 7400 0.0 0.177
0.7038 7450 0.0 -
0.7085 7500 0.0 0.1749
0.7133 7550 0.0 -
0.7180 7600 0.0 0.1814
0.7227 7650 0.0 -
0.7274 7700 0.0 0.1742
0.7322 7750 0.0 -
0.7369 7800 0.0 0.179
0.7416 7850 0.0 -
0.7463 7900 0.0 0.1767
0.7511 7950 0.0 -
0.7558 8000 0.0 0.1809
0.7605 8050 0.0 -
0.7652 8100 0.0 0.1767
0.7700 8150 0.0 -
0.7747 8200 0.0 0.1698
0.7794 8250 0.0 -
0.7841 8300 0.0 0.1772
0.7889 8350 0.0 -
0.7936 8400 0.0 0.1722
0.7983 8450 0.0 -
0.8030 8500 0.0 0.1671
0.8077 8550 0.0 -
0.8125 8600 0.0 0.181
0.8172 8650 0.0 -
0.8219 8700 0.0 0.1788
0.8266 8750 0.0 -
0.8314 8800 0.0 0.1784
0.8361 8850 0.0 -
0.8408 8900 0.0 0.1806
0.8455 8950 0.0 -
0.8503 9000 0.0 0.1783
0.8550 9050 0.0 -
0.8597 9100 0.0 0.1783
0.8644 9150 0.0 -
0.8692 9200 0.0 0.1785
0.8739 9250 0.0 -
0.8786 9300 0.0 0.1772
0.8833 9350 0.0 -
0.8880 9400 0.0 0.1816
0.8928 9450 0.0 -
0.8975 9500 0.0 0.1794
0.9022 9550 0.0 -
0.9069 9600 0.0 0.168
0.9117 9650 0.0 -
0.9164 9700 0.0 0.1771
0.9211 9750 0.0 -
0.9258 9800 0.0 0.1675
0.9306 9850 0.0 -
0.9353 9900 0.0 0.1746
0.9400 9950 0.0 -
0.9447 10000 0.0 0.1769
0.9495 10050 0.0 -
0.9542 10100 0.0 0.177
0.9589 10150 0.0 -
0.9636 10200 0.0 0.1771
0.9684 10250 0.0 -
0.9731 10300 0.0 0.1794
0.9778 10350 0.0 -
0.9825 10400 0.0 0.177
0.9872 10450 0.0 -
0.9920 10500 0.0 0.1794
0.9967 10550 0.0 -
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.11
  • SetFit: 1.0.3
  • Sentence Transformers: 2.7.0
  • Transformers: 4.39.0
  • PyTorch: 2.3.1
  • Datasets: 2.20.0
  • Tokenizers: 0.15.2

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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