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kasparas12
/
is_organizational_model

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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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:

  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
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

Label Accuracy
all 0.99

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("kasparas12/is_organizational_model")
# Run inference
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}
}
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