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---
license: mit
language:
- en
base_model:
- google-t5/t5-base
datasets:
- li2017dailydialog/daily_dialog
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=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")
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