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jamiehudson
/
725_model_v3

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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SetFit with BAAI/bge-base-en-v1.5

This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-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
neither
  • 'ai becomes so much easier to spot when you realize it can replicate, but never understand. its why product usually gives its answers in lists. its a standardized format meant to hide its ignorance to prose.'
  • "hakeem jeffries' tweets are getting so productian it's not even funny and boring any more. he may have brand cranking these out."
  • 'have you tried this with product? i did this with music and got amazing results'
peak
  • 'thats rad man. i have adhd and dyslexia and some other cognitive disabilities and honestly brand is a lifesaver.'
  • "product is like having a coding partner that understands my style, enhancing my productivity significantly. i've even changed the way i code. my code and process is more modular so it's easier to use the output from product in my code base!"
  • 'product is an incredible tool for explaining concepts in i prompted it to describe how k-means clustering could be applied to an engagement survey. it generated sample data, explained the concept and how the insights could be applied.'
pit
  • 'many similar posts popping up on my timeline frustrated with chatproduct not performing to previous levels defeats the purpose of having an ai assitant available 24/7 if it never wants to do any of the tasks you ask of it'
  • "the stuff brand gives is entirely too scripted and impractical, which is what i'm trying to avoid:/"
  • 'so disappointed theyve programmed product to think starvation mode is real'

Evaluation

Metrics

Label Accuracy F1 Precision Recall
all 0.86 [0.2857142857142857, 0.5945945945945945, 0.9195402298850575] [1.0, 0.9166666666666666, 0.8547008547008547] [0.16666666666666666, 0.44, 0.9950248756218906]

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("jamiehudson/725_model_v3")
# Run inference
preds = model("brand's product is product's newest and greatest competitor yet: here's how you can use it within product dlvr.it/szs9nh")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 3 27.8534 91
Label Training Sample Count
pit 26
peak 51
neither 1137

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
0.0012 1 0.2612 -
0.0621 50 0.2009 -
0.1242 100 0.0339 -
0.1863 150 0.0062 -
0.2484 200 0.0039 -
0.3106 250 0.0017 -
0.3727 300 0.003 -
0.4348 350 0.0015 -
0.4969 400 0.002 -
0.5590 450 0.0022 -
0.6211 500 0.0013 -
0.6832 550 0.0013 -
0.7453 600 0.0014 -
0.8075 650 0.0014 -
0.8696 700 0.0012 -
0.9317 750 0.0014 -
0.9938 800 0.0016 -
0.0000 1 0.0897 -
0.0012 50 0.1107 -
0.0025 100 0.065 -
0.0037 150 0.1892 -
0.0049 200 0.0774 -
0.0062 250 0.0391 -
0.0074 300 0.117 -
0.0086 350 0.0954 -
0.0099 400 0.0292 -
0.0111 450 0.0327 -
0.0123 500 0.0041 -
0.0136 550 0.0018 -
0.0148 600 0.03 -
0.0160 650 0.0015 -
0.0173 700 0.0036 -
0.0185 750 0.0182 -
0.0197 800 0.0017 -
0.0210 850 0.0012 -
0.0222 900 0.0014 -
0.0234 950 0.0011 -
0.0247 1000 0.0014 -
0.0259 1050 0.0301 -
0.0271 1100 0.001 -
0.0284 1150 0.0011 -
0.0296 1200 0.0009 -
0.0308 1250 0.0011 -
0.0321 1300 0.0012 -
0.0333 1350 0.001 -
0.0345 1400 0.0008 -
0.0358 1450 0.005 -
0.0370 1500 0.0008 -
0.0382 1550 0.0044 -
0.0395 1600 0.0008 -
0.0407 1650 0.0007 -
0.0419 1700 0.0014 -
0.0432 1750 0.0006 -
0.0444 1800 0.001 -
0.0456 1850 0.0007 -
0.0469 1900 0.0006 -
0.0481 1950 0.0006 -
0.0493 2000 0.0005 -
0.0506 2050 0.0006 -
0.0518 2100 0.0041 -
0.0530 2150 0.0006 -
0.0543 2200 0.0006 -
0.0555 2250 0.0007 -
0.0567 2300 0.0006 -
0.0580 2350 0.0005 -
0.0592 2400 0.0007 -
0.0604 2450 0.0005 -
0.0617 2500 0.0004 -
0.0629 2550 0.0005 -
0.0641 2600 0.0004 -
0.0654 2650 0.0007 -
0.0666 2700 0.0004 -
0.0678 2750 0.0005 -
0.0691 2800 0.0004 -
0.0703 2850 0.0004 -
0.0715 2900 0.0004 -
0.0728 2950 0.0005 -
0.0740 3000 0.0004 -
0.0752 3050 0.0004 -
0.0765 3100 0.0003 -
0.0777 3150 0.0003 -
0.0789 3200 0.0003 -
0.0802 3250 0.0003 -
0.0814 3300 0.0004 -
0.0826 3350 0.0003 -
0.0839 3400 0.0003 -
0.0851 3450 0.0007 -
0.0863 3500 0.0003 -
0.0876 3550 0.0003 -
0.0888 3600 0.0004 -
0.0900 3650 0.0003 -
0.0913 3700 0.0003 -
0.0925 3750 0.0004 -
0.0937 3800 0.0004 -
0.0950 3850 0.0232 -
0.0962 3900 0.0004 -
0.0974 3950 0.0165 -
0.0987 4000 0.0003 -
0.0999 4050 0.0229 -
0.1011 4100 0.0004 -
0.1024 4150 0.0003 -
0.1036 4200 0.0004 -
0.1048 4250 0.0002 -
0.1061 4300 0.0002 -
0.1073 4350 0.0002 -
0.1085 4400 0.0003 -
0.1098 4450 0.0002 -
0.1110 4500 0.0002 -
0.1122 4550 0.0003 -
0.1135 4600 0.0002 -
0.1147 4650 0.0002 -
0.1159 4700 0.0002 -
0.1172 4750 0.0002 -
0.1184 4800 0.0002 -
0.1196 4850 0.0002 -
0.1209 4900 0.0002 -
0.1221 4950 0.0002 -
0.1233 5000 0.0002 -
0.1246 5050 0.0002 -
0.1258 5100 0.0002 -
0.1270 5150 0.0003 -
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0.1295 5250 0.0002 -
0.1307 5300 0.0002 -
0.1320 5350 0.0002 -
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0.1566 6350 0.0001 -
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0.1603 6500 0.0001 -
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0.1702 6900 0.0001 -
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0.1875 7600 0.0 -
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0.1912 7750 0.0001 -
0.1924 7800 0.0001 -
0.1936 7850 0.0 -
0.1949 7900 0.0001 -
0.1961 7950 0.0 -
0.1973 8000 0.0001 -
0.1986 8050 0.0 -
0.1998 8100 0.0 -
0.2010 8150 0.0 -
0.2023 8200 0.0 -
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0.2060 8350 0.0 -
0.2072 8400 0.0001 -
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0.2158 8750 0.0001 -
0.2171 8800 0.0002 -
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0.2467 10000 0.0 -
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0.2491 10100 0.0 -
0.2504 10150 0.0 -
0.2516 10200 0.0 -
0.2528 10250 0.0 -
0.2541 10300 0.0001 -
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0.2602 10550 0.0 -
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0.2763 11200 0.0 -
0.2775 11250 0.0 -
0.2787 11300 0.0 -
0.2800 11350 0.0 -
0.2812 11400 0.0 -
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0.2837 11500 0.0 -
0.2849 11550 0.0 -
0.2861 11600 0.0 -
0.2874 11650 0.0001 -
0.2886 11700 0.0301 -
0.2898 11750 0.0 -
0.2911 11800 0.0 -
0.2923 11850 0.0 -
0.2935 11900 0.0 -
0.2948 11950 0.0 -
0.2960 12000 0.0 -
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0.2985 12100 0.0 -
0.2997 12150 0.0 -
0.3009 12200 0.0001 -
0.3022 12250 0.0 -
0.3034 12300 0.0 -
0.3046 12350 0.0 -
0.3059 12400 0.0 -
0.3071 12450 0.0 -
0.3083 12500 0.0 -
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0.3552 14400 0.0001 -
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0.4132 16750 0.0 -
0.4144 16800 0.0001 -
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Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.5.1
  • Transformers: 4.38.1
  • PyTorch: 2.1.0+cu121
  • Datasets: 2.18.0
  • 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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