marvingoenner/470finalprojectmodel

470 Final Project Model -> Summary Model

Model Overview

This repository contains a fine-tuned T5-small model for abstractive conversational text summarization.
Given a multi-speaker dialogue, the model generates a concise natural-language summary that captures the main points of the conversation.

• Base model: google-t5/t5-small
• Task: Abstractive text summarization
• Model type: Encoder–decoder transformer (T5)

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Dataset

The model was fine-tuned on the SAMSum dataset, which consists of chat-style conversations paired with human-written summaries.

• Dataset name: knkarthick/samsum
• Fields:
  • dialogue: conversation text (input)
  • summary: reference summary (target)
• Splits: train / validation / test

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Training Details

• Epochs: 3
• Learning rate: 3e-4
• Batch size: 8
• Max input length: 512 tokens
• Max target length: 128 tokens
• Training framework: Hugging Face Transformers (Seq2SeqTrainer)
• Hardware: GPU (Google Colab)

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Evaluation

The model was evaluated on the test split of the SAMSum dataset using ROUGE metrics.

• ROUGE-1: 0.4538
• ROUGE-2: 0.2123
• ROUGE-L: 0.3762

(Replace the values with the scores obtained in the notebook.)

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Intended Uses

This model can be used for:

• Summarizing chat conversations or dialogues
• Demonstrations of abstractive summarization
• Educational purposes in NLP and machine learning

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Limitations

• The model may omit important details in long or complex conversations.
• Generated summaries may occasionally be imprecise or incomplete.
• The model is trained on informal dialogue and may not generalize well to other domains.

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How to Use

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

repo_id = "marvingoenner/470finalprojectmodel"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSeq2SeqLM.from_pretrained(repo_id)

dialogue = "Amanda: I baked cookies. Jerry: Sounds great! Amanda: I will bring some tomorrow."
inputs = tokenizer("summarize: " + dialogue, return_tensors="pt", truncation=True)
output_ids = model.generate(**inputs, max_new_tokens=64)

print(tokenizer.decode(output_ids[0], skip_special_tokens=True))