Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Komprehend AI offers an extensive range of document classification and NLP APIs designed specifically for software developers. Our advanced NLP models leverage a vast dataset of over a billion documents, achieving top-notch accuracy in various common NLP applications, including sentiment analysis and emotion detection. Explore our free demo today to experience the effectiveness of our Text Analysis API firsthand. It consistently delivers high accuracy in real-world scenarios, extracting valuable insights from open-ended text data. Compatible with a wide range of industries, from finance to healthcare, it also supports private cloud implementations using Docker containers or on-premise deployments, ensuring your data remains secure. By adhering to GDPR compliance guidelines meticulously, we prioritize the protection of your information. Gain insights into the social sentiment surrounding your brand, product, or service by actively monitoring online discussions. Sentiment analysis involves the contextual examination of text to identify and extract subjective insights from the material, thereby enhancing your understanding of audience perceptions. Additionally, our tools allow for seamless integration into existing workflows, making it easier for developers to harness the power of NLP.
Description
Tev1 is a rapid decision-making and classification model developed by Together AI, which has been fine-tuned from Qwen3.5 4B. Its user-friendly interface allows for the input of a state, a question, and between 2 to 24 labeled choices, enabling the model to output a single letter that can be mapped back to a semantic key by software. This design is particularly beneficial for various production tasks, such as managing support ticket routing, assessing automated returns, enforcing policy guidelines, sentiment analysis, document categorization, and integrating precise decision-making steps into larger agent workflows. With a parameter count of 4.7B and a 32.8K-token context window, the model operates with non-thinking generation to ensure fast and reliable classification. It has been trained on a diverse set of 37,840 examples covering areas such as intent detection, boolean comprehension, sentiment evaluation, policy application, as well as categorizing news and academic papers. The recommended configuration suggests using a temperature setting of 0 along with a minimal output budget, ensuring that the responses are both consistent and straightforward to interpret. In summary, Tev1 stands out as a versatile tool that efficiently addresses a wide range of classification needs.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
Docker
Yes
Python
No
Quickwork
Yes
Qwen3.5
No
TypeScript
No
Unremot
Yes
Integrations
Docker
No
Python
Yes
Quickwork
No
Qwen3.5
Yes
TypeScript
Yes
Unremot
No
Pricing Details
$79 per month
Free Trial
No
Free Version
Yes
Pricing Details
$0.04 per 1M tokens
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Komprehend
Country
India
Website
komprehend.io
Vendor Details
Company Name
Together AI
Founded
2022
Country
United States
Website
www.together.ai/models/tev1-4b-experimental
Product Features
Emotion Recognition
Facial Emotions
No
Facial Expression Analysis
No
Machine Learning
No
Photo Emotions
No
Speech Emotions
No
Video Emotions
No
Written Text Emotions
No
Natural Language Processing
Co-Reference Resolution
No
In-Database Text Analytics
No
Named Entity Recognition
No
Natural Language Generation (NLG)
No
Open Source Integrations
No
Parsing
No
Part-of-Speech Tagging
No
Sentence Segmentation
No
Stemming/Lemmatization
No
Tokenization
No
Text Mining
Boolean Queries
No
Document Filtering
No
Graphical Data Presentation
No
Language Detection
No
Predictive Modeling
No
Sentiment Analysis
No
Summarization
No
Tagging
No
Taxonomy Classification
No
Text Analysis
No
Topic Clustering
No