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jamiehudson
/
725_model_v5

Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results
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SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 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
  • 'i asked brand to write it and then let it translate back. so in reality i have no clue what i am sending...'
  • "i saw someone summarize brand the other day; it doesn't give answers, it gives answer-shaped responses."
  • 'thank you comrade i mean colleague. i will have brand summarize.'
peak
  • 'brand!! it helped me finish my resume. i just asked it if it could write my resume based on horribly written descriptions i came up with. and it made it all pretty:)'
  • 'been building products for a bit now and your product (audio pen) is simple, useful and just works (like the early magic when product came out). congratulations and keep the flag flying high. not surprised that india is producing apps like yours. high time:-)'
  • 'just got access to personalization in brand!! totally unexpected. very happy'
pit
  • 'brand recently i came across a very unwell patient in a psychiatric unit who was using product & this was reinforcing his delusional state & detrimentally impacting his mental health. anyone looking into this type of usage of product? what safe guards are being put in place?'
  • 'brand product is def better at extracting numbers from images, product failed (pro version) twice...'
  • "the stuff brand gives is entirely too scripted and impractical, which is what i'm trying to avoid:/"

Evaluation

Metrics

Label Accuracy F1 Precision Recall
all 0.964 [0.8837209302325582, 0.9130434782608696, 0.9781021897810218] [1.0, 1.0, 0.9571428571428572] [0.7916666666666666, 0.84, 1.0]

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_v5")
# Run inference
preds = model("product the way it shows the sources is so fucking cool, this new ai is amazing")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 3 31.6606 98
Label Training Sample Count
pit 277
peak 265
neither 1105

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
0.0000 1 0.3157 -
0.0012 50 0.2756 -
0.0023 100 0.2613 -
0.0035 150 0.278 -
0.0047 200 0.2617 -
0.0058 250 0.214 -
0.0070 300 0.2192 -
0.0082 350 0.1914 -
0.0093 400 0.1246 -
0.0105 450 0.1343 -
0.0117 500 0.0937 -
0.0129 550 0.075 -
0.0140 600 0.0479 -
0.0152 650 0.0976 -
0.0164 700 0.0505 -
0.0175 750 0.0149 -
0.0187 800 0.0227 -
0.0199 850 0.0276 -
0.0210 900 0.0033 -
0.0222 950 0.0015 -
0.0234 1000 0.0008 -
0.0245 1050 0.0005 -
0.0257 1100 0.001 -
0.0269 1150 0.0009 -
0.0280 1200 0.0004 -
0.0292 1250 0.0007 -
0.0304 1300 0.001 -
0.0315 1350 0.0004 -
0.0327 1400 0.0005 -
0.0339 1450 0.0003 -
0.0350 1500 0.0004 -
0.0362 1550 0.0002 -
0.0374 1600 0.0004 -
0.0386 1650 0.0003 -
0.0397 1700 0.0003 -
0.0409 1750 0.0005 -
0.0421 1800 0.0004 -
0.0432 1850 0.0003 -
0.0444 1900 0.0002 -
0.0456 1950 0.0002 -
0.0467 2000 0.0003 -
0.0479 2050 0.0002 -
0.0491 2100 0.0001 -
0.0502 2150 0.0002 -
0.0514 2200 0.0256 -
0.0526 2250 0.0001 -
0.0537 2300 0.0124 -
0.0549 2350 0.0004 -
0.0561 2400 0.0125 -
0.0572 2450 0.0001 -
0.0584 2500 0.0002 -
0.0596 2550 0.0002 -
0.0607 2600 0.0001 -
0.0619 2650 0.0002 -
0.0631 2700 0.0002 -
0.0643 2750 0.0243 -
0.0654 2800 0.0001 -
0.0666 2850 0.0001 -
0.0678 2900 0.0001 -
0.0689 2950 0.0002 -
0.0701 3000 0.006 -
0.0713 3050 0.0021 -
0.0724 3100 0.0003 -
0.0736 3150 0.0003 -
0.0748 3200 0.0001 -
0.0759 3250 0.0 -
0.0771 3300 0.0002 -
0.0783 3350 0.0001 -
0.0794 3400 0.0 -
0.0806 3450 0.0124 -
0.0818 3500 0.0001 -
0.0829 3550 0.0001 -
0.0841 3600 0.0001 -
0.0853 3650 0.0 -
0.0864 3700 0.0042 -
0.0876 3750 0.0001 -
0.0888 3800 0.0004 -
0.0900 3850 0.0001 -
0.0911 3900 0.0 -
0.0923 3950 0.004 -
0.0935 4000 0.0002 -
0.0946 4050 0.0001 -
0.0958 4100 0.0001 -
0.0970 4150 0.0 -
0.0981 4200 0.0 -
0.0993 4250 0.0008 -
0.1005 4300 0.0 -
0.1016 4350 0.0 -
0.1028 4400 0.0 -
0.1040 4450 0.0 -
0.1051 4500 0.0 -
0.1063 4550 0.0 -
0.1075 4600 0.0 -
0.1086 4650 0.0 -
0.1098 4700 0.0 -
0.1110 4750 0.0 -
0.1121 4800 0.0 -
0.1133 4850 0.0 -
0.1145 4900 0.0 -
0.1157 4950 0.0 -
0.1168 5000 0.0 -
0.1180 5050 0.0 -
0.1192 5100 0.0 -
0.1203 5150 0.0008 -
0.1215 5200 0.001 -
0.1227 5250 0.0 -
0.1238 5300 0.0 -
0.1250 5350 0.0057 -
0.1262 5400 0.0014 -
0.1273 5450 0.0001 -
0.1285 5500 0.0001 -
0.1297 5550 0.0001 -
0.1308 5600 0.0001 -
0.1320 5650 0.0001 -
0.1332 5700 0.0 -
0.1343 5750 0.0 -
0.1355 5800 0.0004 -
0.1367 5850 0.0 -
0.1378 5900 0.0001 -
0.1390 5950 0.0 -
0.1402 6000 0.0 -
0.1414 6050 0.0 -
0.1425 6100 0.0 -
0.1437 6150 0.0 -
0.1449 6200 0.0 -
0.1460 6250 0.0 -
0.1472 6300 0.0 -
0.1484 6350 0.0 -
0.1495 6400 0.0 -
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0.1542 6600 0.0 -
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0.1589 6800 0.0 -
0.1600 6850 0.0 -
0.1612 6900 0.0 -
0.1624 6950 0.0 -
0.1635 7000 0.0 -
0.1647 7050 0.0 -
0.1659 7100 0.0 -
0.1671 7150 0.0 -
0.1682 7200 0.0 -
0.1694 7250 0.0 -
0.1706 7300 0.0 -
0.1717 7350 0.0 -
0.1729 7400 0.0 -
0.1741 7450 0.0 -
0.1752 7500 0.0 -
0.1764 7550 0.0 -
0.1776 7600 0.0 -
0.1787 7650 0.0 -
0.1799 7700 0.0 -
0.1811 7750 0.0 -
0.1822 7800 0.0 -
0.1834 7850 0.0 -
0.1846 7900 0.0 -
0.1857 7950 0.0 -
0.1869 8000 0.0 -
0.1881 8050 0.0 -
0.1892 8100 0.0 -
0.1904 8150 0.0 -
0.1916 8200 0.0 -
0.1928 8250 0.0 -
0.1939 8300 0.0 -
0.1951 8350 0.0 -
0.1963 8400 0.0127 -
0.1974 8450 0.0001 -
0.1986 8500 0.0 -
0.1998 8550 0.0 -
0.2009 8600 0.0249 -
0.2021 8650 0.0003 -
0.2033 8700 0.0 -
0.2044 8750 0.0003 -
0.2056 8800 0.0003 -
0.2068 8850 0.0002 -
0.2079 8900 0.0 -
0.2091 8950 0.0 -
0.2103 9000 0.0001 -
0.2114 9050 0.0 -
0.2126 9100 0.0 -
0.2138 9150 0.0 -
0.2149 9200 0.0 -
0.2161 9250 0.0 -
0.2173 9300 0.0 -
0.2185 9350 0.0 -
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0.2290 9800 0.0 -
0.2301 9850 0.0 -
0.2313 9900 0.0 -
0.2325 9950 0.0 -
0.2336 10000 0.0 -
0.2348 10050 0.0 -
0.2360 10100 0.0 -
0.2371 10150 0.0 -
0.2383 10200 0.0 -
0.2395 10250 0.0 -
0.2406 10300 0.0 -
0.2418 10350 0.0 -
0.2430 10400 0.0 -
0.2442 10450 0.0 -
0.2453 10500 0.0 -
0.2465 10550 0.0 -
0.2477 10600 0.0 -
0.2488 10650 0.0 -
0.2500 10700 0.0 -
0.2512 10750 0.0 -
0.2523 10800 0.0 -
0.2535 10850 0.0 -
0.2547 10900 0.0 -
0.2558 10950 0.0 -
0.2570 11000 0.0 -
0.2582 11050 0.0 -
0.2593 11100 0.0 -
0.2605 11150 0.0 -
0.2617 11200 0.0 -
0.2628 11250 0.0 -
0.2640 11300 0.0 -
0.2652 11350 0.0 -
0.2663 11400 0.0 -
0.2675 11450 0.0 -
0.2687 11500 0.0 -
0.2699 11550 0.0 -
0.2710 11600 0.0 -
0.2722 11650 0.0 -
0.2734 11700 0.0 -
0.2745 11750 0.0 -
0.2757 11800 0.0 -
0.2769 11850 0.0 -
0.2780 11900 0.0 -
0.2792 11950 0.0 -
0.2804 12000 0.0 -
0.2815 12050 0.0 -
0.2827 12100 0.0 -
0.2839 12150 0.0 -
0.2850 12200 0.0 -
0.2862 12250 0.0 -
0.2874 12300 0.0 -
0.2885 12350 0.0 -
0.2897 12400 0.0 -
0.2909 12450 0.0 -
0.2920 12500 0.0 -
0.2932 12550 0.0 -
0.2944 12600 0.0 -
0.2956 12650 0.0 -
0.2967 12700 0.0 -
0.2979 12750 0.0 -
0.2991 12800 0.0 -
0.3002 12850 0.0 -
0.3014 12900 0.0 -
0.3026 12950 0.0 -
0.3037 13000 0.0 -
0.3049 13050 0.0 -
0.3061 13100 0.0 -
0.3072 13150 0.0 -
0.3084 13200 0.0 -
0.3096 13250 0.0 -
0.3107 13300 0.0 -
0.3119 13350 0.0 -
0.3131 13400 0.0 -
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0.3318 14200 0.0 -
0.3329 14250 0.0 -
0.3341 14300 0.0 -
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0.3563 15250 0.0 -
0.3575 15300 0.0 -
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0.4007 17150 0.0 -
0.4019 17200 0.0 -
0.4030 17250 0.0 -
0.4042 17300 0.0 -
0.4054 17350 0.0 -
0.4065 17400 0.0 -
0.4077 17450 0.031 -
0.4089 17500 0.1234 -
0.4100 17550 0.0569 -
0.4112 17600 0.0006 -
0.4124 17650 0.0003 -
0.4135 17700 0.0007 -
0.4147 17750 0.0002 -
0.4159 17800 0.025 -
0.4170 17850 0.0032 -
0.4182 17900 0.0 -
0.4194 17950 0.0 -
0.4206 18000 0.0 -
0.4217 18050 0.0 -
0.4229 18100 0.0002 -
0.4241 18150 0.0 -
0.4252 18200 0.0 -
0.4264 18250 0.0 -
0.4276 18300 0.0002 -
0.4287 18350 0.0001 -
0.4299 18400 0.0 -
0.4311 18450 0.0002 -
0.4322 18500 0.0001 -
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0.4357 18650 0.0 -
0.4369 18700 0.0001 -
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0.5140 22000 0.0 -
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Framework Versions

  • Python: 3.10.12
  • SetFit: 1.0.3
  • Sentence Transformers: 2.5.1
  • Transformers: 4.38.2
  • 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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