peguses_chat_sum

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Model Details

Model Description

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• Developed by: Mudasir692
• Model type: transformer
• Language(s) (NLP): python
• License: MIT
• Finetuned from model [optional]: Peguses

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Bias, Risks, and Limitations

Model might not generate coherent summary to large extent.

[More Information Needed]

Recommendations

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How to Get Started with the Model

import torch from transformers import PegasusForConditionalGeneration, PegasusTokenizer

Load the saved model and tokenizer

model_path = "peguses_chat_sum" device = torch.device("cpu")

Load the model and tokenizer from the saved directory

model = PegasusForConditionalGeneration.from_pretrained(model_path) tokenizer = PegasusTokenizer.from_pretrained(model_path)

Move the model to the correct device

model = model.to(device)

How to Get Started with the Model

from transformers import PegasusForConditionalGeneration, PegasusTokenizer

model = PegasusForConditionalGeneration.from_pretrained("Mudasir692/peguses_chat_sum") tokenizer = PegasusTokenizer.from_pretrained("Mudasir692/peguses_chat_sum") input_text = """ #Person1#: Hey Alice, congratulations on your promotion! #Person2#: Thank you so much! It means a lot to me. I’m still processing it, honestly. #Person1#: You totally deserve it. Your hard work finally paid off. Let’s celebrate this weekend. #Person2#: That sounds amazing. Dinner on me, okay? #Person1#: Sure! Just let me know where and when. Oh, by the way, did you tell your family? #Person2#: Yes, they were so excited. Mom’s already planning to bake a cake. #Person1#: That’s wonderful! I’ll bring a gift too. It’s such a big milestone for you. #Person2#: You’re the best. Thanks for always being so supportive. """ inputs = tokenizer(input_text, return_tensors="pt") model.eval() outputs = model.generate(**inputs, max_new_tokens=100) generated_summary = tokenizer.decode(outputs[0], skip_special_tokens=True) print("generated summary", generated_summary)