You can deploy this model and start earning money today!

praisethefool/
human_tech-fields-multilabelclassifier

Name: human_tech-fields-multilabelclassifier
Rating: 2.5 (11 reviews)

setfit

safetensors

mpnet

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

sentence-transformers/paraphrase-mpnet-base-v2

finetune

model-index

Views

No views yet

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 MultiOutputClassifier instance is used for 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.

Model Details

Model Description

Model Type: SetFit
Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2
Classification head: a MultiOutputClassifier instance
Maximum Sequence Length: 512 tokens

Model Sources

Repository: SetFit on GitHub
Paper: Efficient Few-Shot Learning Without Prompts
Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Evaluation

Metrics

Label	Accuracy
all	0.5517

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("praisethefool/human_tech-fields-multilabelclassifier")
# Run inference
preds = model("5 DeSci projects disrupting scientific research and development — Crypto Altruism 0 Skip to Content BLOG CATEGORIES DAOs EDUCATION ENVIRONMENT REFI EQUITY INCLUSION FINANCIAL INCLUSION DEFI NFTs PHILANTHROPY SCIENCE DESCI SOCIAL IMPACT SUPPLY CHAIN COMMENTARY PODCASTS CRYPTO ALTRUISM PODCAST THE WEB3 NONPROFIT IMPACT ON OPTIMISM INFOGRAPHICS RESOURCES BECOME A CRYPTO CHARITY DONATING CRYPTO LEVERAGING AI AT YOUR NONPROFIT ABOUT US WHO WE ARE TRANSPARENCY AFFILIATE PARTNERSHIPS CONTACT SUPPORT US Open Menu Close Menu Open Menu Close Menu BLOG CATEGORIES DAOs EDUCATION ENVIRONMENT REFI EQUITY INCLUSION FINANCIAL INCLUSION DEFI NFTs PHILANTHROPY SCIENCE DESCI SOCIAL IMPACT SUPPLY CHAIN COMMENTARY PODCASTS CRYPTO ALTRUISM PODCAST THE WEB3 NONPROFIT IMPACT ON OPTIMISM INFOGRAPHICS RESOURCES BECOME A CRYPTO CHARITY DONATING CRYPTO LEVERAGING AI AT YOUR NONPROFIT ABOUT US WHO WE ARE TRANSPARENCY AFFILIATE PARTNERSHIPS CONTACT SUPPORT US BLOG Folder CATEGORIES Back DAOs EDUCATION ENVIRONMENT REFI EQUITY INCLUSION FINANCIAL INCLUSION DEFI NFTs PHILANTHROPY SCIENCE DESCI SOCIAL IMPACT SUPPLY CHAIN COMMENTARY Folder PODCASTS Back CRYPTO ALTRUISM PODCAST THE WEB3 NONPROFIT IMPACT ON OPTIMISM INFOGRAPHICS Folder RESOURCES Back BECOME A CRYPTO CHARITY DONATING CRYPTO LEVERAGING AI AT YOUR NONPROFIT Folder ABOUT US Back WHO WE ARE TRANSPARENCY AFFILIATE PARTNERSHIPS CONTACT SUPPORT US 5 DeSci projects disrupting scientific research and development Project HighlightsScienceDAOs Mar 30 Written By Drew Simon 2021 was the year of decentralization and this momentum has only increased into 2022 Not only have we seen incredible growth in the decentralized finance DeFi space but we have also seen the emergence of social impact DAOs decentralized media platforms decentralized VC funds and more recently the emergence of a new field – Decentralized Science or DeSci In short “the decentralized science DeSci movement aims to harness new technologies such as blockchain and ‘Web3’ to address some important research pain points silos and bottlenecks” Whereas scientific research has long been viewed as overly bureaucratic and disjointed the DeSci movement aims to improve this by using blockchain to offer greater transparency and to take on the “profit hungry intermediaries” such as scientific journals that have dominated the traditional research spaceFor some resources on DeSci I recommend you check out the following articlesDeSci an opportunity to decentralize scientific research and publicationA Guide to DeSci the Latest Web3 MovementCall to join the decentralized science movementFor this blog post we will be highlighting 5 DeSci projects that are leading the way and positively disrupting scientific research and development1 VitaDAOOne of the best examples of DeSci in action is VitaDAO a Decentralized Autonomous Organization DAO focused on funding longevity research in “an open and democratic manner” Specifically they are focused on the decentralization of drug development focused on the extension of human life and healthspan They fund earlystage research with the goal of turning the research into biotech companiesVitaDAO is government by holders of VITA tokens which can either be purchased or earned through contributions of work or intellectual property With over 4000 members and 9M in funding raised to support scientific research VitaDAO has proven that the DeSci movement is no laughing matterCheck out some of their featured projects here2 SCINETThe SCINET platform which is built on blockchain enables retail and institutional investors to securely invest in scientific research and technology directly In addition to funding promising scientific research they also offer a “blockchainpowered” cloud laboratory for researchers a rigorous decentralized peer review process and enable researches to document their IP on an immutable blockchain3 AntidoteDAOAntidoteDAO is a decentralized community focused on funding cancer research and other cancer initiatives Their ecosystem includes a governance token and NFT collection which both enable individuals to vote on where to allocate funds In addition to providing funding to charities supporting cancer research and cancer patients a core focus of the DAO is on providing 100K seed fund grants to cancer research teams Research projects are first reviewed by the DAO’s Medical Advisory team and then put to the community for a vote Fun fact we have an upcoming podcast episode with AntidoteDAO that when available will be published HERE Crypto Altruism uses Ledger to keep its assets safeYou’ve probably heard the phrase “not your keys not your coins” By choosing a hard wallet like the Nano S Plus to store your crypto you can rest assured that the keys and the crypto are truly yoursGet your Ledger Nano S Plus now by clicking HERE or on the image below 4 LabDAOLabDAO is an emerging organization which is dedicated to operating a communityrun network of wet and dry labs with the goal of advancing scientific research and development A wet lab is one focused on analysing drugs chemicals and other biological matter whereas a dry lab is one focused on applied or computational mathematical analysis LabDAO is a relatively new project that is still in its infancy but has a promising mission and strong community of support around it 5 MoleculeMolecule is a decentralized scientific research funding platform that operates as a marketplace for researchers seeking out funding and individuals looking to invest in scientific research projects They are “connecting leading researchers to funding by turning intellectual property and its development into a liquid and easily investable asset”Researchers can list their research projects on the Molecule marketplace as a means to engage with potential investors and to secure funding for their project Molecule currently has over 250 research projects listed on their marketplace over 4500 DAO community members and 3 “Bio DAOs” with over 10M in funding in their network According to Molecule “The future of life science research will be driven by open liquid markets for intellectual property powered by web3 technology”We cover more amazing DeSci projects in our more recent postTen more DeSci projects disrupting scientific research development and knowledge sharing Buy me a coffee Send a tip in ETH cryptoaltruismethLike what you are reading Consider contributing to Crypto Altruism so we can continue putting out great content that shines a light on the good being done in the crypto and blockchain community SUPPORT CRYPTO ALTRUISM Please note we make use of affiliate marketing to provide readers with referrals to high quality and relevant products and services DeScidecentralizationscienceblockchainlists Drew Simon Previous Previous Crypto Altruism Podcast Episode 39 AntidoteDAO Decentralized funding of cancer research and charitable initiatives Next Next Crypto Altruism Podcast Episode 38 Using NFTs to empower content creators and help kids learn ft Susie Jaramillo CONTENTBLOGPODCASTINFOGRAPHICSCURATED LISTS ABOUTABOUTSUPPORT USCONTACTDISCLAIMERPRIVACY POLICY Buy me a coffee ETHERC20 cryptoaltruismeth 0xac5C0105914F3afb363699996C9914f193aeDD4A Sign up for our monthly newsletter Thank you © Crypto Altruism 2023 FOLLOW")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	20	2568.9241	13352

Training Hyperparameters

batch_size: (8, 8)
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: True
warmup_proportion: 0.1
l2_weight: 0.01
seed: 42
evaluation_strategy: steps
eval_max_steps: -1
load_best_model_at_end: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0017	1	0.2236	-
0.0694	40	-	0.1379
0.0868	50	0.1722	-
0.1389	80	-	0.1440
0.1736	100	0.0536	-
0.2083	120	-	0.1412
0.2604	150	0.0293	-
0.2778	160	-	0.1343
0.3472	200	0.0234	0.1406
0.4167	240	-	0.1266
0.4340	250	0.0176	-
0.4861	280	-	0.1118
0.5208	300	0.0193	-
0.5556	320	-	0.1095
0.6076	350	0.0162	-
0.625	360	-	0.0926
0.6944	400	0.0223	0.0995
0.7639	440	-	0.0923
0.7812	450	0.018	-
0.8333	480	-	0.0814
0.8681	500	0.0045	-
0.9028	520	-	0.0801
0.9549	550	0.0074	-
0.9722	560	-	0.0794

Framework Versions

Python: 3.11.12
SetFit: 1.1.2
Sentence Transformers: 3.4.1
Transformers: 4.51.3
PyTorch: 2.6.0+cu124
Datasets: 3.5.1
Tokenizers: 0.21.1

Citation

BibTeX

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}
}