A fine-tuned T5 model for conversational FAQ-style responses. Given a user question, the model generates a natural-language answer, making it suitable for lightweight chatbot and Q&A applications.
Model description
This model is a fine-tuned version of [t5-small / t5-base] (Google's T5 text-to-text transformer), adapted for conversational question-answering. It takes a user query as input and generates a relevant text response, framed as a text-to-text generation task.
Intended uses & limitations
Intended uses:
FAQ-style chatbots for websites, apps, or customer support
Educational/demo projects exploring conversational AI with T5
Quick prototyping of Q&A systems
Limitations:
Trained on a limited dataset, so responses may be generic or repetitive outside the training domain
Does not maintain multi-turn conversational context (treats each query independently)
English only
Not suitable for safety-critical or factual/medical/legal advice use cases
May occasionally produce inaccurate or nonsensical answers (hallucination risk common to generative models)
Training data
The model was fine-tuned on a [custom FAQ dataset / dataset name, e.g. "a collection of customer support Q&A pairs"]. [Add: dataset size, source, and any preprocessing steps if known.]
How to use
python
1from transformers import pipeline
23pipe = pipeline("text2text-generation", model="UMAR798/t5-chatbot")4response = pipe("What are your business hours?")5print(response)
