---
license: mit
language:
- en
base_model:
- google-t5/t5-base
datasets:
- abisee/cnn_dailymail
metrics:
- rouge
---
# T5-Base-Sum

This model is a fine-tuned version of `T5` for summarization tasks. It was trained on various articles and is hosted on Hugging Face for easy access and use.

## 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=400, 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 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")