🚀 T5-Small Dialogue Summarizer (Fine-Tuned on SAMSum)

A sequence-to-sequence transformer model fine-tuned to convert conversational logs, transcripts, team syncs, and multi-turn messenger discussions into crisp, third-person narrative summaries.

---

📌 Model Details

• Model Architecture: Sequence-to-Sequence Transformer (T5-small)
• Base Model: google-t5/t5-small (~60.5 Million parameters)
• Training Method: Full Parameter Supervised Fine-Tuning (SFT)
• Task Conditioning Prefix: summarize:
• Target Domain: Messenger chat threads, customer support tickets, and team standup dialogues
• Language: English (en)

---

🎯 Intended Uses & Limitations

💡 Intended Use

• Condensing conversational dialogues and meeting transcripts into structured key takeaways.
• Translating first-person statements ("I fixed it") into clear third-person reporting ("Alex fixed the issue").
• Extracting actionable outcomes, timelines, bug fixes, and meeting consensus from chat threads.

⚠️ Limitations

• Speaker Formatting: Best results occur when turns follow standard conversational labeling (e.g., Speaker: message).
• Sequence Truncation: Input sequences are bounded at 512 tokens; dialogues exceeding this cutoff require chunking to avoid dropping later context.
• Abstractive Hallucination: May occasionally misattribute specific timestamps or numerical figures if conversational threads are ambiguous.

---

💻 How to Use

🐍 Standard Transformers Pipeline

import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Set your Hugging Face repository identifier
repo_id = "YOUR_HF_USERNAME/t5-small-samsum"

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

# Sample dialogue input
dialogue = """
Hassan: Did you get the chance to verify the memory leak fix?
Sara: Yes, I ran stress tests against the auth service. Pod memory stayed below 45%.
Hassan: Awesome. Can you deploy it to staging and ping the QA channel?
Sara: Done. Build 142 is live on staging now.
"""

# Format input with task prefix
input_text = f"summarize: {dialogue.strip()}"
inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)

# Generate summary using beam search
with torch.no_grad():
    output_tokens = model.generate(
        inputs["input_ids"],
        max_new_tokens=60,
        min_length=10,
        num_beams=4,
        early_stopping=True,
    )

summary = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
print("Summary:\n", summary)