README.md

---
license: mit
language:
- en
base_model:
- google-t5/t5-base
datasets:
- li2017dailydialog/daily_dialog
metrics:
- rouge
---
# T5-base-ddg

This model is a fine-tuned version of `T5` for open eneded dialog generation. It was finetuned on the Daily Dialog dataset for 35 epochs using 
Cyclic attention and custom loss.

## Model Usage

Below is an example of how to load and use this model for summarization:

```python
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Set the device (use GPU if available)
device = 'cuda' if torch.cuda.is_available() else 'cpu'

# Load the model and tokenizer from Hugging Face
tokenizer = T5Tokenizer.from_pretrained("Vijayendra/T5-base-ddg")
model = T5ForConditionalGeneration.from_pretrained("Vijayendra/T5-base-ddg").to(device)

# Define your prompts
input_prompts = [
    "I am having a bad day at work",
    "What should I do about my stress?",
    "How can I improve my productivity?",
    "I'm feeling very anxious today",
    "What is the best way to learn new skills?",
    "How do I deal with failure?",
    "What do you think about the future of technology?",
    "I want to improve my communication skills",
    "How can I stay motivated at work?",
    "What is the meaning of life?"
]

# Generate responses
generated_responses = {}
for prompt in input_prompts:
    inputs = tokenizer(prompt, return_tensors="pt", max_length=40, truncation=True, padding="max_length").to(device)
    
    model.eval()
    with torch.no_grad():
        generated_ids = model.generate(
            input_ids=inputs['input_ids'],
            attention_mask=inputs['attention_mask'],
            max_length=100,
            num_beams=7,
            repetition_penalty=2.5,
            length_penalty=2.0,
            early_stopping=True
        )

    # Decode the generated response
    generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
    generated_responses[prompt] = generated_text

# Display the input prompts and the generated responses
for prompt, response in generated_responses.items():
    print(f"Prompt: {prompt}")
    print(f"Response: {response}\n")





from transformers import T5Tokenizer, T5ForConditionalGeneration
from datasets import load_dataset
import torch

# Set the device (use GPU if available)
device = 'cuda' if torch.cuda.is_available() else 'cpu'

# Load your T5 model and tokenizer
tokenizer = T5Tokenizer.from_pretrained("Vijayendra/T5-base-ddg")
model = T5ForConditionalGeneration.from_pretrained("Vijayendra/T5-base-ddg").to(device)

# Load the dataset - Replace with your dataset name
dataset = load_dataset('daily_dialog', split='test')

# Generate 10 responses from the test set
def generate_responses(dataset, num_responses=50):
    responses = []
    for i, data in enumerate(dataset):
        if i >= num_responses:
            break

        # Get the input prompt and reference response
        input_text = data['dialog'][0]  # Assuming the first dialog is the input prompt
        reference_text = data['dialog'][1]  # Assuming the second dialog is the expected response
        
        # Tokenize and generate response
        inputs = tokenizer(input_text, return_tensors="pt", max_length=40, truncation=True, padding="max_length").to(device)
        model.eval()
        with torch.no_grad():
            generated_ids = model.generate(
                input_ids=inputs['input_ids'],
                attention_mask=inputs['attention_mask'],
                max_length=40,
                num_beams=7,
                repetition_penalty=2.5,
                length_penalty=2.0,
                early_stopping=True
            )
        
        # Decode generated response
        generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
        
        # Append input, generated response, and reference
        responses.append({
            "Input Prompt": input_text,
            "Generated Response": generated_text,
            "Reference Response": reference_text
        })

    return responses

# Get the responses
responses = generate_responses(dataset)

# Print the results
for idx, response in enumerate(responses):
    print(f"Prompt {idx+1}: {response['Input Prompt']}")
    print(f"T5 Model Response: {response['Generated Response']}")
    print(f"Reference Response: {response['Reference Response']}\n")