README.md

---
language: 
- en
widget:
- text: "Generate a dialogue between two people about the following topic: A local street market bustles with activity, #Person1# tries exotic food for the first time, and #Person2#, familiar with the cuisine, offers insights and recommendations. Dialogue:"
  example_title: "Street Market Example"
tags:
- dialogue
- conversation-generator
- flan-t5-base
- fine-tuned
license: apache-2.0
datasets:
- kaggle-dialogue-dataset
---

# Omaratef3221/flan-t5-base-dialogue-generator

## Model Description
This model is a fine-tuned version of Google's `t5` specifically tailored for generating realistic and engaging dialogues or conversations. 
It has been trained to capture the nuances of human conversation, making it highly effective for applications requiring conversational AI capabilities.

## Intended Use
`Omaratef3221/flan-t5-base-dialogue-generator` is ideal for developing chatbots, virtual assistants, and other applications where generating human-like dialogue is crucial. 
It can also be used for research and development in natural language understanding and generation.

## How to Use
You can use this model directly with the transformers library as follows:

### Download the model
```python
model_name = "Omaratef3221/flan-t5-base-dialogue-generator"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
```

### Use with example
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "Omaratef3221/flan-t5-base-dialogue-generator"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

prompt = '''
Generate a dialogue between two people about the following topic:
A local street market bustles with activity, #Person1# tries exotic food for the first time, and #Person2#, familiar with the cuisine, offers insights and recommendations.
Dialogue:
'''
# Generate a response to an input statement
input_ids = tokenizer(prompt, return_tensors='pt').input_ids
output = model.generate(input_ids, top_p = 0.6, do_sample=True, temperature = 1.2, max_length = 512)
print(tokenizer.decode(output[0], skip_special_tokens=True).replace('. ', '.\n'))
```