t5 base qa summary emotion

Introduction

The T5-BASE-QA-SUMMARY-EMOTION model is a text-to-text generation model developed by Kiri-AI. It is fine-tuned for tasks such as question answering, summarization, and emotion detection using the T5 architecture. The model supports English and is built on PyTorch, utilizing datasets like CoQa, Squad v2, GoEmotions, and CNN/DailyMail.

Architecture

The model is based on the T5 (Text-to-Text Transfer Transformer) architecture, which is versatile for various text generation tasks. T5 is implemented using the Transformers library and requires PyTorch for execution. The model has been fine-tuned on multiple datasets, achieving notable scores on question-answering tasks, although summarization and emotion detection scores are yet to be evaluated.

Training

The model was trained and fine-tuned using the following datasets:

• CoQa: A conversational question-answering dataset.
• Squad v2: A popular question-answering dataset with unanswerable questions.
• GoEmotions: A dataset for emotion detection.
• CNN/DailyMail: A dataset for summarization tasks.

The model achieved an F1 score of 79.5 on the Squad 2 dev set and 70.6 on the CoQa dev set.

Guide: Running Locally

To run the model locally, follow these steps:

1. Install Dependencies: Ensure you have transformers>=4.0.0 and torch installed. Use pip to install if necessary:

   pip install transformers torch
   

2. Load the Model: Utilize the Transformers library to load the model and tokenizer:

   from transformers import T5ForConditionalGeneration, T5Tokenizer
   
   model = T5ForConditionalGeneration.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")
   tokenizer = T5Tokenizer.from_pretrained("kiri-ai/t5-base-qa-summary-emotion")
   

3. Perform Inference: Use the model for specific tasks like question answering, summarization, or emotion detection. Follow the examples given in the documentation for guidance on structuring input and processing output.

4. Cloud GPUs: For better performance, consider using cloud-based GPUs such as AWS EC2, Google Cloud Platform, or Azure. These services provide scalable resources to handle extensive computations required for model inference.

License

The T5-BASE-QA-SUMMARY-EMOTION model is licensed under the Apache 2.0 License. This allows for both personal and commercial use, distribution, modification, and private use of the model.