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NLP

Intellibot

Start up Profile Intellibot simplifies RPA deployment with its proprietary AI-enabled Robotic Process Automation (RPA) platform, which incorporates machine learning, natural language processing, and computer vision. Intellibot enables organizations to create their own software robots that are easily trainable and can work on an organization’s day to day business processes, thus, becoming their…
March 4, 20240Likes0Comments
Machine Learning

Meta AI Introduces Multi-Line AI-Assisted Code Authoring

CodeCompose, an AI-powered code authoring tool utilized by tens of thousands of developers at Meta, has undergone scaling from providing single-line to multiline suggestions. This transition involved addressing unique challenges to enhance usability. Initially, multi-line suggestions were found to disrupt workflow by constantly shifting around existing code, potentially decreasing productivity and satisfaction. Additionally, generating multi-line…
March 4, 20240Likes0Comments
Machine Learning

How Effective are Self-Explanations from Large Language Models like ChatGPT in Sentiment Analysis? A Deep Dive into Performance, Cost, and Interpretability

Language models like GPT-3 are designed to be neutral and generate text based on the patterns they’ve learned in the data. They don’t have inherent sentiments or emotions. If the data used for training contains biases, these biases can be reflected in the model’s outputs. However, their output can be interpreted as positive, negative, or…
March 4, 20240Likes0Comments
Text Classification

twitter-roberta-base-sentiment-latest

Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022) This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark. The original Twitter-based RoBERTa model can be found here and the original reference paper is TweetEval. This model is suitable for English. …
March 4, 20240Likes0Comments
VLLMs

TinyLLaVA-2.0B

TinyLLaVA: A Framework of Small-scale Large Multimodal Models 🎉 News [2024.02.25] Update evaluation scripts and docs! [2024.02.25] Data descriptions out. Release TinyLLaVA-1.5B and TinyLLaVA-2.0B! [2024.02.24] Example code on inference and model loading added! [2024.02.23] Evaluation code and scripts released! [2024.02.21] Creating the TinyLLaVABench…
March 4, 20240Likes0Comments
Document Question Answering

layoutlmv2-base-uncased-finetuned-infovqa

layoutlmv2-base-uncased-finetuned-infovqa This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: Loss: 2.0870 Model description More information needed Intended uses & limitations More information needed Training and evaluation data More information needed Training procedure Training…
March 4, 20240Likes0Comments