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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Mudasir692 |
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- **Model type:** transformer |
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- **Language(s) (NLP):** python |
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- **License:** MIT |
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- **Finetuned from model [optional]:** Peguses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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## Bias, Risks, and Limitations |
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Model might not generate coherent summary to large extent. |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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import torch |
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from transformers import PegasusForConditionalGeneration, PegasusTokenizer |
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# Load the saved model and tokenizer |
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model_path = "peguses_chat_sum" |
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device = torch.device("cpu") |
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# Load the model and tokenizer from the saved directory |
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model = PegasusForConditionalGeneration.from_pretrained(model_path) |
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tokenizer = PegasusTokenizer.from_pretrained(model_path) |
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# Move the model to the correct device |
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model = model.to(device) |
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## How to Get Started with the Model |
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from transformers import PegasusForConditionalGeneration, PegasusTokenizer |
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model = PegasusForConditionalGeneration.from_pretrained("Mudasir692/peguses_chat_sum") |
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tokenizer = PegasusTokenizer.from_pretrained("Mudasir692/peguses_chat_sum") |
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input_text = """ |
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#Person1#: Hey Alice, congratulations on your promotion! |
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#Person2#: Thank you so much! It means a lot to me. I’m still processing it, honestly. |
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#Person1#: You totally deserve it. Your hard work finally paid off. Let’s celebrate this weekend. |
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#Person2#: That sounds amazing. Dinner on me, okay? |
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#Person1#: Sure! Just let me know where and when. Oh, by the way, did you tell your family? |
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#Person2#: Yes, they were so excited. Mom’s already planning to bake a cake. |
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#Person1#: That’s wonderful! I’ll bring a gift too. It’s such a big milestone for you. |
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#Person2#: You’re the best. Thanks for always being so supportive. |
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""" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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model.eval() |
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outputs = model.generate(**inputs, max_new_tokens=100) |
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generated_summary = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print("generated summary", generated_summary) |
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