Cohere Cohere Classify Top Builders
Explore the top contributors showcasing the highest number of Cohere Cohere Classify app submissions within our community.
Cohere classify is a large language model that classify text content. Classify organizes information for more effective content moderation, analysis, and chatbot experiences.
|Relese date||November 15, 2021|
|Type||Autoregressive, Transformer, Language model|
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.
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
Cohere Cohere Classify Hackathon projects
Discover innovative solutions crafted with Cohere Cohere Classify, developed by our community members during our engaging hackathons.
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.
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.
TaskMate is a solution that can be integrated into any website, providing AI-powered speech interaction with the website. AI plays a significant role in making the solution better because of natural language processing. Speech interaction can address the problems we have identified by providing hands-free interaction, increasing accessibility, improving productivity, and reducing cognitive load. Overall, speech interaction can make it easier to use your phone in a variety of situations and improve accessibility and productivity for all users. We believe that TaskMate has the potential to be a game-changer in the way people interact with websites.
Project Peace is a Multilingual Text Detoxifier. It is an innovative solution to identify and neutralize toxic or harmful language in written text. It utilizes advanced AI algorithms powered by Cohere’s multilingual models to understand and analyze text across multiple languages, and flag potentially toxic language, including the ability to convert that toxic language into neutral and non-toxic one. Project Peace’s ability to process text in multiple languages, allows it to address the problem of toxic language on a global scale. Project Peace can be integrated into online platforms, such as social media websites, online forums, and online communities, to help prevent the spread of toxic language and promote a safer online environment. It can be used by businesses and organizations to monitor and control the language used on their website and even in their customer care services. It can also be used by governments and public institutions to monitor and control the language used in online communication channels and to promote social harmony and inclusion. It can be used by educators and schools to help prevent bullying and toxic language in online learning environments, ensuring that students have a safe and supportive learning environment. private individuals as well who want to promote a safer and more inclusive online environment, or who want to ensure that the language they use online is respectful and non-toxic. Project Peace has an appealing future by its scalability and customization. By integrating it with the existing social platforms, it can be made accessible to a wide range of users. Moreover, it has the potential to become an industry standard for detecting and detoxifying toxic texts. The goal of the project remains to create a safer online community by reducing the spread of hate speech, cyberbullying, and other forms of harmful language.
Usually firms have excess liquidity from their operations and people have savings that want to invest so that they can protect themselves from inflation or to generate a passive income. The problem is that in order to invest in the financial markets they either have to hire an specialist or they need to manage their investments themselves and this can expose them to high risks since they aren't usually experts on the field. There have been cases of companies even going into bankruptcy for preciselly investing in the financial markets with poor management or little understanding of the complexity of the markets. Here's where we come in, our solution powered by AI enalbes firms and people to invest in the financial markets without having to hire an expensive investment manager or having to learn themselves. It is a virtual portfolio manager. First the user has to provide financial information and a news or article related to him, with this an ML classifier using the Cohere API determines the risk profile of the user. With this information the platform generates a tailored portfolio, out of a selected financial asset universe specific to each risk profile. The available assets vary between different types of US stocks, FOREX pairs and even cryptocurrencies for the more risk taker profiles. Once the portfolio has been created it reports an overview of its composition as well as a backtest of its performance on the market. At this point the user has the option to decide whether to pursue a passive portfolio management strategy or an active one with just the click of a button. If he prefers a passive strategy the platform will take positions for a classic buy and hold strategy of the selected assets. If, in contrast, the user selects the use of trading bots then he will opt for an active portfolio management and trained DL bots will be buying and selling the assets at convenience for a better portfolio performance.