Cohere Cohere Classify AI technology Top Builders

Explore the top contributors showcasing the highest number of Cohere Cohere Classify AI technology app submissions within our community.

Cohere classify

Cohere classify is a large language model that classify text content. Classify organizes information for more effective content moderation, analysis, and chatbot experiences.

General
Relese date	November 15, 2021
Author	Cohere
Documentation	https://docs.cohere.ai/reference/classify
Type	Autoregressive, Transformer, Language model
Discord	https://discord.gg/lablab-ai-877056448956346408

Start building with Cohere Classify

To see what others are building with Cohere Classify, check out the community built Cohere Use Cases and Applications.

Cohere Classify Tutorials

👉 Discover more Cohere Classify Tutorials on lablab.ai

Cohere Classify Boilerplates

Kickstart your development with a Cohere Classify based boilerplate. Boilerplates is a great way to headstart when building your next project with Classify.

Next(js) with Frontend

Cohere Classify Libraries

A curated list of libraries and technologies to help you build great projects with Cohere Classify.

Cohere Node SDK simplifying interacting with api in node.js environments
Cohere Classify Go SDK simplifying interacting with api in GoLang environments
Cohere Classify Python SDK simplifying interacting with api in GoLang environments

Awesome Cohere Classify resources

Complimentary resources that will help you build even better applications

Cohere Playground Interact with Cohere API through their playground
Langchain Toolset for building applications powered by LLM

Edit on Github

Cohere Cohere Classify AI technology Hackathon projects

Discover innovative solutions crafted with Cohere Cohere Classify AI technology, developed by our community members during our engaging hackathons.

YouthGovAI

The AI-Powered Youth Engagement Hub uses xAI’s Grok model to analyze and categorize youth-submitted ideas, aligning them with government priorities. Interactive submission portals empower youth to propose solutions with tools like templates and examples. A collaborative decision-making dashboard enables officials to review, discuss, and prioritize ideas through voting, feedback, and analytics. Sector-specific challenges address key areas such as public health, education reform, and climate adaptation, encouraging targeted, innovative solutions. Recognition dashboards showcase implemented ideas, track their impact, and celebrate youth contributions, fostering engagement and measurable progress.

Gléssi-Tché

GLESSI-TCHE a le potentiel de révolutionner l'accès à l'information et aux services pour les agriculteurs béninois, en leur offrant une solution intégrée à l'IA et adaptée à leurs besoins uniques. Fonctionne dans les langues locales comme le Fon, le Yoruba et le Dendi, supprimant les barrières linguistiques. Accessible via des téléphones portables ou même des appareils simples et en combinant technologie avancée et sensibilité locale, GLESSI-TCHE peut jouer un rôle crucial dans l'autonomisation des agriculteurs et l'amélioration de la productivité agricole au Bénin. Les fonctionalités de GLESSI-TCHE: *Accès aux informations agricoles cruciales : Obtenez des réponses à vos questions sur la plantation, le choix des cultures, la lutte antiparasitaire, la gestion des sols, les prévisions météorologiques, les prix du marché, les techniques post-récolte. * Diagnostic des maladies et des parasites : Décrivez les symptômes ou prenez des photos pour qu'Agrispeaker diagnostique les problèmes potentiels et recommande des solutions. * Système d'alerte précoce : Recevez des alertes en temps réel sur les risques météorologiques tels que les sécheresses, les inondations et les invasions de parasites pour vous préparer et minimiser les pertes. * Optimisation de l'irrigation : Utilisez les recommandations d'Agrispeaker basées sur les données locales pour économiser l'eau et améliorer les rendements des cultures. * Accès au marché : Connectez-vous directement avec les acheteurs pour obtenir de meilleurs prix pour vos produits et réduire la dépendance vis-à-vis des intermédiaires.

Multi-Lingual Customer Service Chat Assistance

Our project introduces an innovative multi-lingual customer service assistant, designed to transform the way businesses interact with their global customer base. Utilizing state-of-the-art AI from Cohere for language detection, intent processing, and response generation, our chatbot seamlessly communicates in English, German, and Italian. It's engineered to handle common customer service queries like order tracking, shipping, and returns, offering succinct, empathetic solutions. Leveraging Weaviate's database capabilities, we maintain robust customer feedback systems, enhancing continuous learning and service improvement. Our chatbot utilizes advanced Langchain Technologies and Helsinki NLP models for accurate translations, ensuring no language barrier hinders customer interaction. Hosted on Streamlit, this assistant represents a leap forward in accessible, efficient, and inclusive customer service solutions, perfectly suited for diverse businesses in today's global marketplace.

WeCare Caretaker Assistant

We have built a solution for agencies which provide the caretaker services for parents who are in search of babysitters for their child. When users call the agency after business hours or when agents are not available for assistance, we are routing them to leave a voicemail with their babysitter requirement and contact number. With this solution, agents can focus on more complex tasks rather than manually retrieving voicemails, analysing them and coming up with a resolution. When the caller dials the agency phone number during office closed hours or peak hours when agents are not available to serve them, we route the caller to the voicemail menu where we ask them to leave a voicemail with babysitting requirements and their contact details, etc. Once the voicemail is available, we extract it and convert this speech to text using OpenAI’s whisper API which gives us the voicemail transcription. After that, we meticulously perform the prompt engineering for ChatGPT API to provide us all the required information from voicemail like intent, sentiment, babysitting date and time, etc in JSON format. Using this information, we query the EmployeeSchedule table which is in the H2 database. Once we have the information about availability of babysitters, we query RedisJSON to get the employee profile information like employee name, contact details, date of birth, languages spoken, image, etc. We then build a PDF document using itext library. This PDF containing available babysitter information will be sent on the caller’s WhatsApp. After this, we send an SMS to the agency as an alert notification about the customer enquiry and ask them to get in touch with the customer. Github link - https://github.com/technocouple/technocouple-caretaker-assistant Video link - https://drive.google.com/drive/folders/1NBew2U0Xgtm04ubQszjLvZV92fowR6-D?usp=sharing Presentation - https://drive.google.com/file/d/1TBMSU5Ohyn1v2P2u_RqbZOpuCvWv1Crq/view?usp=share_link DEMO is at the end of the video.

Top Applicant Speedpass

Finding top applicants is a time consuming, rigorous process that involves juxtaposing many resumes for comparison. This process is messy and may overlook qualified candidates in favor of time. TAS is the ultimate time-saving intelligent tool that allows recruiters to select their top candidates to interview. Instead of reviewing each submission one-by-one, a list of resumes is uploaded to TAS and analyzed. All resume data is parsed via OCR to allow recruiters to input both text and image-based PDFs. The recruiter then specifies an assessment question ("Which of these candidates for Finance Manager has experience in the Aviation industry?") to which GPT-3's assessment of that criteria will be provided back to the recruiter.

Creator Alde

This app is a powerful tool designed to assist content creators who have a substantial following of over 5000 viewers or followers. By leveraging the YouTube API, the app can efficiently read through all the comments and classify them according to their sentiment, toxic content, or spam. After classifying the comments, the app creates a word embedding using cohere AI, which helps to identify patterns and group similar comments together. These comments are then grouped into three clusters using KMeans. By clustering the comments, content creators can quickly analyze and understand the nature of the comments and identify any common themes or topics that resonate with their audience. Using the analysis of the clustered comments, the app generates a summary of the main themes and extracts the primary topics. These topics are presented to the content creators, along with suggested content ideas for future posts. This helps content creators to understand the interests and preferences of their audience better and create content that resonates with their viewers. In conclusion, this app provides content creators with a streamlined approach to understanding their audience's preferences and interests by analyzing the comments on their posts. By classifying, clustering, and summarizing comments, the app provides a valuable tool for generating new content ideas that will engage and excite their audience, leading to increased views and followers.