LinkedCringe v0.2: e5-small

fine-tuned on LinkedCringe v0.2 from intfloat/e5-small

This is an initial test/work-in-progress, but not bad thus far.

Model

This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:

Fine-tuning a Sentence Transformer with contrastive learning.
Training a classification head with features from the fine-tuned Sentence Transformer.

Labels

This model has been trained (using methods described above) to predict a single class label for `

# numeric id: text label 
{
    1: 'cringe',
    2: 'relevant',
    3: 'info',
    4: 'noise'
}

Usage

To use this model for inference, first install the SetFit library:

python -m pip install setfit

basic inference

You can then run inference as follows:

from setfit import SetFitModel

# Download from Hub and run inference
model = SetFitModel.from_pretrained("pszemraj/e5-small-LinkedCringe-setfit-skl-20it-2e")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])

# manually refer to labels above
preds

Class object with utils

create a"custom" wrapper class with the labels:

from setfit import SetFitModel
from typing import List, Dict


class PostClassifier:
    DEFAULT_ID2LABEL = {1: "cringe", 2: "relevant", 3: "info", 4: "noise"}

    def __init__(
        self,
        model_id: str = "pszemraj/e5-small-LinkedCringe-setfit-skl-20it-2e",
        id2label: Dict[int, str] = None,
    ):
        """Initialize PostClassifier with model name and/or label mapping."""
        self.model = SetFitModel.from_pretrained(model_id)
        self.id2label = id2label if id2label else self.DEFAULT_ID2LABEL

    def classify(self, texts: List[str]) -> List[str]:
        """Classify list of texts, return list of corresponding labels."""
        preds = self.model(texts)
        return [self.id2label[int(pred)] for pred in preds]

    def predict_proba(self, texts: List[str]) -> List[Dict[str, float]]:
        """Predict label probabilities for a list of texts, return a list of probability dictionaries."""
        proba = self.model.predict_proba(texts)
        return [
            {self.id2label.get(i + 1, "Unknown"): float(pred) for i, pred in enumerate(pred)}
            for pred in proba
        ]

    def __call__(self, texts: List[str]) -> List[str]:
        """Enable class instance to act as a function for text classification."""
        return self.classify(texts)

instantiate & classify :

# import PostClassifier if you defined it in another script etc
model_name="pszemraj/e5-small-LinkedCringe-setfit-skl-20it-2e"
classifier = PostClassifier(model_name)

# classify some posts (these should all be cringe maaaaybe noise)
posts = [
    "🚀 Innovation is our middle name! We're taking synergy to new heights and disrupting the market with our game-changing solutions. Stay tuned for the next paradigm shift! 💥 #CorporateRevolution #SynergisticSolutions",
    "🌟 Attention all trailblazers! Our cutting-edge product is the epitome of excellence. It's time to elevate your success and ride the wave of unparalleled achievements. Join us on this journey towards greatness! 🚀 #UnleashYourPotential #SuccessRevolution",
    "🌍 We're not just a company, we're a global force for change! Our world-class team is committed to revolutionizing industries and making a lasting impact. Together, let's reshape the future and leave a legacy that will be remembered for ages! 💪 #GlobalTrailblazers #LegacyMakers",
    "🔥 Harness the power of synergy and unlock your true potential with our transformative solutions. Together, we'll ignite a fire of success that will radiate across industries. Join the league of winners and conquer new frontiers! 🚀 #SynergyChampions #UnleashThePowerWithin",
    "💡 Innovation alert! Our visionary team has cracked the code to redefine excellence. Get ready to be blown away by our mind-boggling breakthroughs that will leave your competitors in the dust. It's time to disrupt the status quo and embrace the future! 🌟 #InnovationRevolution #ExcellenceUnleashed",
    "🌐 Welcome to the era of limitless possibilities! Our revolutionary platform will empower you to transcend boundaries and achieve unprecedented success. Together, let's shape a future where dreams become realities and ordinary becomes extraordinary! ✨ #LimitlessSuccess #DreamBig",
    "💥 Brace yourselves for a seismic shift in the industry! Our game-changing product is set to revolutionize the way you work, think, and succeed. Say goodbye to mediocrity and join the league of pioneers leading the charge towards a brighter tomorrow! 🚀 #IndustryDisruptors #PioneeringSuccess",
    "🚀 Attention all innovators and disruptors! It's time to break free from the chains of convention and rewrite the rulebook of success. Join us on this exhilarating journey as we create a new chapter in the annals of greatness. The sky's not the limit—it's just the beginning! 💫 #BreakingBarriers #UnleashGreatness",
    "🌟 Unlock the secret to unprecedented achievements with our exclusive formula for success. Our team of experts has distilled years of wisdom into a powerful elixir that will propel you to the zenith of greatness. It's time to embrace the extraordinary and become a legend in your own right! 💥 #FormulaForSuccess #RiseToGreatness",
    "🔑 Step into the realm of infinite possibilities and seize the keys to your success. Our groundbreaking solutions will unlock doors you never knew existed, propelling you towards a future filled with limitless growth and prosperity. Dare to dream big and let us be your catalyst for greatness! 🚀 #UnlockYourPotential #LimitlessSuccess"
]


post_preds = classifier(posts)
print(post_preds)

eval - detailed

***** Running evaluation *****
{'accuracy': 0.8,
 'based_model_id': 'intfloat/e5-small',
 'tuned_model_id': 'e5-small-LinkedCringe-setfit-skl-20it-2e'}


# 10-post results

['cringe',
 'cringe',
 'info',
 'cringe',
 'cringe',
 'cringe',
 'cringe',
 'cringe',
 'cringe',
 'cringe']

BibTeX entry and citation info

Note: this is for setfit and not this checkpoint.

@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}
}