library_name: transformers
tags: []
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Uses
''' 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")