TinyModelsTinyModels

Explore Community Classifiers

Real ML models built in minutes, not months. Try them instantly or remix to build your own.

Build a sentiment classifier for product reviews

Sentiment classifier for short (1-2 sentence) general e-commerce product reviews. Positive = clearly favorable/satisfied, Negative = clearly unfavorable/disappointed, Neutral = factual statements, mixed sentiment, or lukewarm reactions. Edge cases: 'works as expected' is Neutral, 'not bad/it's okay' is Neutral, mixed pros/cons like 'good quality but expensive' is Neutral, 'could be better' is Negative.

PositiveNegativeNeutral
100.0%By B.

Classify emails into spam, promotions, important

Email classifier that categorizes emails into spam (malicious/unwanted), promotions (marketing/newsletters), or important (personal/work communications)

spampromotionsimportant
50.0%By B.

Create a spam vs ham classifier. Generate 5 examples per label and then train the model.

Spam vs ham classifier for personal emails, focusing on identifying promotional spam vs legitimate wanted emails

spamham
60.0%By B.

Create a sentiment classifier with labels: positive, negative. Generate 5 examples per label and sho

A sentiment classifier that categorizes text as positive or negative sentiment

positivenegative
100.0%By B.

Classify customer support emails into urgent, normal, or spam. Generate 5 examples per label and tra

Classifies customer support emails for a SaaS company into urgent (billing issues, account locked, service down), normal (feature questions, how-to help), or spam (promotional, unrelated messages)

urgentnormalspam
46.7%By B.

Classify tweets as positive or negative. Generate 5 examples per label and train immediately.

Classifies product review tweets as positive or negative sentiment, treating sarcasm as negative

positivenegative
90.0%By B.

Classify support tickets into urgent, normal, low priority. Generate 5 examples per label.

IT helpdesk ticket priority classifier that categorizes tickets as urgent (system down/security issues), normal (bug fixes), or low (feature requests) based on subject line and description content

urgentnormallow
53.3%By B.

Create a movie review classifier with labels: positive, negative, neutral. Generate 5 examples per l

Classifies movie reviews into positive, negative, or neutral sentiment

positivenegativeneutral
100.0%By B.

Create a sentiment classifier with labels: positive, negative. Generate 3 examples per label and tra

Sentiment classifier for product reviews - classifies general product reviews as positive or negative sentiment

positivenegative
100.0%By B.

Create a spam filter classifier with labels: spam, not_spam. Generate 5 examples per label and train

Email spam filter that identifies malicious/unwanted emails while preserving legitimate newsletters and communications

spamnot_spam
50.0%By B.

Classify customer emails into: billing, technical_support, general_inquiry. Generate 5 examples per

Classifies customer emails for a SaaS software company into billing issues, technical support requests, and general inquiries

billingtechnical_supportgeneral_inquiry
66.7%By B.

Create a sentiment classifier for app store reviews with labels: positive, negative. Generate 5 exam

Sentiment classifier for app store reviews - classifies reviews as positive or negative

positivenegative
100.0%By B.

Create a sentiment classifier with labels: positive, negative, neutral. Generate 10 examples per lab

Sentiment classifier for tech product reviews. Classifies reviews as positive (satisfied/happy), negative (dissatisfied/frustrated), or neutral (factual/objective statements).

positivenegativeneutral
100.0%By B.

Create a sentiment classifier with positive, negative, neutral labels. Generate 5 examples per label

Sentiment classifier for general product reviews with positive, negative, and neutral labels. Mixed sentiments are classified as neutral.

positivenegativeneutral
86.7%By B.

Create a classifier to categorize tech support questions into: hardware, software, network. Generate

IT helpdesk classifier that categorizes tech support questions into hardware issues (physical device problems), software issues (application/OS problems), and network issues (connectivity problems)

hardwaresoftwarenetwork
86.7%By B.

Create sentiment classifier with labels: positive, negative, neutral. It's for product reviews. Gene

Sentiment classifier for product reviews that categorizes text as positive, negative, or neutral based on customer sentiment

positivenegativeneutral
100.0%By B.

Classify customer support tickets as urgent, normal, or low priority. Generate 20 examples per label

Classifier that categorizes customer support tickets by priority level - urgent for critical issues needing immediate attention, normal for standard requests, and low for non-critical inquiries

urgentnormallow
90.0%By B.

I want to classify customer feedback into positive, negative, and neutral. Generate 10 examples per

Customer feedback sentiment classifier for SaaS analytics tool product reviews

positivenegativeneutral
95.0%By B.

I want to classify bug reports into categories: bug, feature_request, question

Classifier for categorizing bug reports into bugs, feature requests, and questions

bugfeature_requestquestion
85.0%By B.

Classify emails as spam or not_spam. Generate 5 examples per label.

Email spam classifier that distinguishes between spam emails and legitimate emails

spamnot_spam
85.0%By B.

Build Your Own

Create a text classifier in minutes through natural conversation

Get Started Free →