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diwank
/
hn-upvote-classifier

Text Classification
setfit
Safetensors
sentence-transformers
new
generated_from_setfit_trainer
custom_code
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SetFit with Alibaba-NLP/gte-base-en-v1.5

This is a SetFit model trained on the diwank/hn-upvote-data dataset that can be used for Text Classification. This SetFit model uses Alibaba-NLP/gte-base-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
0
  • 'The telltale words that could identify generative AI text'
  • 'The telltale words that could identify generative AI text'
  • 'The telltale words that could identify generative AI text'
1
  • 'Dangerous Feelings\nSource: www.collaborativefund.com'
  • 'The Modos Paper Monitor\nSource: www.modos.tech'
  • 'What did Mary know? A thought experiment about consciousness (2013)\nSource: philosophynow.org'

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("diwank/hn-upvote-classifier")
# Run inference
preds = model("My Python code is a neural network")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 3 8.6577 18
Label Training Sample Count
0 4577
1 252

Training Hyperparameters

  • batch_size: (320, 32)
  • num_epochs: (1, 16)
  • max_steps: -1
  • sampling_strategy: undersampling
  • body_learning_rate: (4e-05, 2e-05)
  • head_learning_rate: 0.01
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: True
  • use_amp: True
  • warmup_proportion: 0.05
  • l2_weight: 0.2
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: True

Training Results

Epoch Step Training Loss Validation Loss
0.0001 1 0.208 -
0.0069 50 0.0121 -
0.0139 100 0.002 -
0.0208 150 0.0032 -
0.0277 200 0.001 -
0.0347 250 0.0006 -
0.0416 300 0.0005 -
0.0486 350 0.0004 -
0.0555 400 0.0003 -
0.0624 450 0.0002 -
0.0694 500 0.0002 -
0.0763 550 0.0002 -
0.0832 600 0.0002 -
0.0902 650 0.0001 -
0.0971 700 0.0001 -
0.1040 750 0.0001 -
0.1110 800 0.0001 -
0.1179 850 0.0001 -
0.1248 900 0.0001 -
0.1318 950 0.0001 -
0.1387 1000 0.0001 -
0.1457 1050 0.0001 -
0.1526 1100 0.0001 -
0.1595 1150 0.0001 -
0.1665 1200 0.0001 -
0.1734 1250 0.0001 -
0.1803 1300 0.0001 -
0.1873 1350 0.0001 -
0.1942 1400 0.0001 -
0.2011 1450 0.0001 -
0.2081 1500 0.0001 -
0.2150 1550 0.0001 -
0.2219 1600 0.0 -
0.2289 1650 0.0 -
0.2358 1700 0.0 -
0.2428 1750 0.0 -
0.2497 1800 0.0001 -
0.2566 1850 0.0 -
0.2636 1900 0.0 -
0.2705 1950 0.0 -
0.2774 2000 0.0 -
0.2844 2050 0.0 -
0.2913 2100 0.0 -
0.2982 2150 0.0 -
0.3052 2200 0.0 -
0.3121 2250 0.0 -
0.3190 2300 0.0 -
0.3260 2350 0.0 -
0.3329 2400 0.0 -
0.3399 2450 0.0 -
0.3468 2500 0.0 -
0.3537 2550 0.0 -
0.3607 2600 0.0 -
0.3676 2650 0.0 -
0.3745 2700 0.0 -
0.3815 2750 0.0 -
0.3884 2800 0.0 -
0.3953 2850 0.0 -
0.4023 2900 0.0 -
0.4092 2950 0.0 -
0.4161 3000 0.0 -
0.4231 3050 0.0 -
0.4300 3100 0.0 -
0.4370 3150 0.0 -
0.4439 3200 0.0 -
0.4508 3250 0.0 -
0.4578 3300 0.0 -
0.4647 3350 0.0 -
0.4716 3400 0.0 -
0.4786 3450 0.0 -
0.4855 3500 0.0 -
0.4924 3550 0.0 -
0.4994 3600 0.0 -
0.5063 3650 0.0 -
0.5132 3700 0.0 -
0.5202 3750 0.0 -
0.5271 3800 0.0 -
0.5341 3850 0.0 -
0.5410 3900 0.0 -
0.5479 3950 0.0 -
0.5549 4000 0.0 -
0.5618 4050 0.0 -
0.5687 4100 0.0 -
0.5757 4150 0.0 -
0.5826 4200 0.0 -
0.5895 4250 0.0 -
0.5965 4300 0.0 -
0.6034 4350 0.0 -
0.6103 4400 0.0 -
0.6173 4450 0.0 -
0.6242 4500 0.0 -
0.6312 4550 0.0 -
0.6381 4600 0.0 -
0.6450 4650 0.0 -
0.6520 4700 0.0 -
0.6589 4750 0.0 -
0.6658 4800 0.0 -
0.6728 4850 0.0 -
0.6797 4900 0.0 -
0.6866 4950 0.0 -
0.6936 5000 0.0 -
0.7005 5050 0.0 -
0.7074 5100 0.0 -
0.7144 5150 0.0 -
0.7213 5200 0.0 -
0.7283 5250 0.0 -
0.7352 5300 0.0 -
0.7421 5350 0.0 -
0.7491 5400 0.0 -
0.7560 5450 0.0 -
0.7629 5500 0.0 -
0.7699 5550 0.0 -
0.7768 5600 0.0 -
0.7837 5650 0.0 -
0.7907 5700 0.0 -
0.7976 5750 0.0 -
0.8045 5800 0.0 -
0.8115 5850 0.0 -
0.8184 5900 0.0 -
0.8254 5950 0.0 -
0.8323 6000 0.0 -
0.8392 6050 0.0 -
0.8462 6100 0.0 -
0.8531 6150 0.0 -
0.8600 6200 0.0 -
0.8670 6250 0.0 -
0.8739 6300 0.0 -
0.8808 6350 0.0 -
0.8878 6400 0.0 -
0.8947 6450 0.0 -
0.9017 6500 0.0 -
0.9086 6550 0.0 -
0.9155 6600 0.0 -
0.9225 6650 0.0 -
0.9294 6700 0.0 -
0.9363 6750 0.0 -
0.9433 6800 0.0 -
0.9502 6850 0.0 -
0.9571 6900 0.0 -
0.9641 6950 0.0 -
0.9710 7000 0.0 -
0.9779 7050 0.0 -
0.9849 7100 0.0 -
0.9918 7150 0.0 -
0.9988 7200 0.0 -

Framework Versions

  • Python: 3.10.14
  • SetFit: 1.0.3
  • Sentence Transformers: 3.0.1
  • Transformers: 4.41.2
  • PyTorch: 2.3.1+cu121
  • Datasets: 2.20.0
  • Tokenizers: 0.19.1

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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