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jamiehudson
/
725_model_v2

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
  • 'it might sound strange, but in my opinion, sams intelligence intimidates him from expressing himself and creating personal art. for example, since product is a masterpiece in the sense, the bar is set very high, so he might even subconsciously be unable to put anything out less'
  • 'lately, i really enjoy the genre of joke that makes you say the punchline in your head.'
  • 'any idea in regard to the product product not being seen? i have 1 device with it, the rest are missing it. same wufb policies.'
pit
  • "brand or brand are behaving like lazy interns. when you need something useful from them like researching and consolidating a large bunch of information they'll just tell you to look it up yourself or right away refuse to do the work. useless."
  • 'the moment i found out what exactly product does i just uninstalled product and went back to 10'
  • "at least 80% of the product stuff posted here has produced erroneous results, and many have utilized ip theft/copyright infringement in informing the model. we're not going to spend community time on it at this point."
peak
  • "man, product/whatever is my new best friend. i like product but the integration of product into office and product is a lot of fun. i just spent the day feeding it my training presentation i'm preparing in my day job and it was very helpful. almost better than humans."
  • "excited to share my experience with product, an incredible language model by brand! from answering questions to creative writing, it's a powerful tool that amazes me every time."
  • 'product in product is a game changer!! here is a list of things it can do: it can answer your questions in natural language. it can summarize content to give you a brief overview it can adjust your pcs settings it can help troubleshoot issues. 1/2'

Evaluation

Metrics

Label Accuracy F1 Precision Recall
all 0.8996 [0.5217391304347826, 0.5142857142857142, 0.9478260869565217] [0.42857142857142855, 0.4090909090909091, 0.9775784753363229] [0.6666666666666666, 0.6923076923076923, 0.919831223628692]

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_v2")
# 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 5 29.1484 90
Label Training Sample Count
pit 44
peak 62
neither 150

Training Hyperparameters

  • batch_size: (32, 32)
  • num_epochs: (3, 3)
  • 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 0.2383 -
0.0119 50 0.2395 -
0.0237 100 0.2129 -
0.0356 150 0.1317 -
0.0474 200 0.0695 -
0.0593 250 0.01 -
0.0711 300 0.0063 -
0.0830 350 0.0028 -
0.0948 400 0.0026 -
0.1067 450 0.0021 -
0.1185 500 0.0018 -
0.1304 550 0.0016 -
0.1422 600 0.0014 -
0.1541 650 0.0015 -
0.1659 700 0.0013 -
0.1778 750 0.0012 -
0.1896 800 0.0012 -
0.2015 850 0.0012 -
0.2133 900 0.0011 -
0.2252 950 0.0011 -
0.2370 1000 0.0009 -
0.2489 1050 0.001 -
0.2607 1100 0.0009 -
0.2726 1150 0.0008 -
0.2844 1200 0.0008 -
0.2963 1250 0.0009 -
0.3081 1300 0.0008 -
0.3200 1350 0.0007 -
0.3318 1400 0.0007 -
0.3437 1450 0.0007 -
0.3555 1500 0.0006 -
0.3674 1550 0.0007 -
0.3792 1600 0.0007 -
0.3911 1650 0.0008 -
0.4029 1700 0.0006 -
0.4148 1750 0.0006 -
0.4266 1800 0.0006 -
0.4385 1850 0.0006 -
0.4503 1900 0.0006 -
0.4622 1950 0.0006 -
0.4740 2000 0.0006 -
0.4859 2050 0.0005 -
0.4977 2100 0.0006 -
0.5096 2150 0.0006 -
0.5215 2200 0.0005 -
0.5333 2250 0.0005 -
0.5452 2300 0.0005 -
0.5570 2350 0.0006 -
0.5689 2400 0.0005 -
0.5807 2450 0.0005 -
0.5926 2500 0.0006 -
0.6044 2550 0.0006 -
0.6163 2600 0.0005 -
0.6281 2650 0.0005 -
0.6400 2700 0.0005 -
0.6518 2750 0.0005 -
0.6637 2800 0.0005 -
0.6755 2850 0.0005 -
0.6874 2900 0.0005 -
0.6992 2950 0.0004 -
0.7111 3000 0.0004 -
0.7229 3050 0.0004 -
0.7348 3100 0.0005 -
0.7466 3150 0.0005 -
0.7585 3200 0.0005 -
0.7703 3250 0.0004 -
0.7822 3300 0.0004 -
0.7940 3350 0.0004 -
0.8059 3400 0.0004 -
0.8177 3450 0.0004 -
0.8296 3500 0.0004 -
0.8414 3550 0.0004 -
0.8533 3600 0.0004 -
0.8651 3650 0.0004 -
0.8770 3700 0.0004 -
0.8888 3750 0.0004 -
0.9007 3800 0.0004 -
0.9125 3850 0.0004 -
0.9244 3900 0.0005 -
0.9362 3950 0.0004 -
0.9481 4000 0.0004 -
0.9599 4050 0.0004 -
0.9718 4100 0.0004 -
0.9836 4150 0.0004 -
0.9955 4200 0.0004 -
0.0000 1 0.2717 -
0.0013 50 0.0686 -
0.0026 100 0.088 -
0.0000 1 0.1796 -
0.0013 50 0.0584 -
0.0026 100 0.1018 -
0.0039 150 0.128 -
0.0052 200 0.0761 -
0.0065 250 0.0216 -
0.0078 300 0.1652 -
0.0091 350 0.0384 -
0.0104 400 0.0062 -
0.0117 450 0.0442 -
0.0130 500 0.0452 -
0.0143 550 0.0081 -
0.0156 600 0.0205 -
0.0169 650 0.0125 -
0.0182 700 0.0012 -
0.0195 750 0.0011 -
0.0208 800 0.0315 -
0.0221 850 0.0009 -
0.0009 1 0.0006 -
0.0429 50 0.0008 -
0.0858 100 0.0005 -
0.1288 150 0.0015 -
0.1717 200 0.0013 -
0.2146 250 0.0237 -
0.2575 300 0.0304 -
0.3004 350 0.0005 -
0.3433 400 0.0013 -
0.3863 450 0.03 -
0.4292 500 0.0005 -
0.4721 550 0.0006 -
0.5150 600 0.0005 -
0.5579 650 0.0005 -
0.6009 700 0.0004 -
0.6438 750 0.0004 -
0.6867 800 0.0004 -
0.7296 850 0.0004 -
0.7725 900 0.0004 -
0.8155 950 0.0003 -
0.8584 1000 0.0004 -
0.9013 1050 0.0003 -
0.9442 1100 0.0004 -
0.9871 1150 0.0003 -
1.0300 1200 0.0003 -
1.0730 1250 0.0004 -
1.1159 1300 0.0003 -
1.1588 1350 0.0005 -
1.2017 1400 0.0003 -
1.2446 1450 0.0003 -
1.2876 1500 0.0003 -
1.3305 1550 0.0003 -
1.3734 1600 0.0003 -
1.4163 1650 0.0003 -
1.4592 1700 0.0003 -
1.5021 1750 0.0005 -
1.5451 1800 0.0003 -
1.5880 1850 0.0003 -
1.6309 1900 0.0003 -
1.6738 1950 0.0005 -
1.7167 2000 0.0003 -
1.7597 2050 0.0007 -
1.8026 2100 0.0003 -
1.8455 2150 0.0003 -
1.8884 2200 0.0003 -
1.9313 2250 0.0003 -
1.9742 2300 0.0003 -
2.0172 2350 0.0003 -
2.0601 2400 0.0003 -
2.1030 2450 0.0003 -
2.1459 2500 0.0003 -
2.1888 2550 0.0002 -
2.2318 2600 0.0003 -
2.2747 2650 0.0004 -
2.3176 2700 0.0002 -
2.3605 2750 0.0003 -
2.4034 2800 0.0002 -
2.4464 2850 0.0002 -
2.4893 2900 0.0002 -
2.5322 2950 0.0002 -
2.5751 3000 0.0002 -
2.6180 3050 0.0004 -
2.6609 3100 0.0004 -
2.7039 3150 0.0003 -
2.7468 3200 0.0003 -
2.7897 3250 0.0003 -
2.8326 3300 0.0003 -
2.8755 3350 0.0003 -
2.9185 3400 0.0003 -
2.9614 3450 0.0005 -

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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