---
language: en
license: mit
tags:
- summarization
- fine-tuned
- dialogue
- transformers
- phi-2
model_name: phi-2-dialogue-summarization
datasets:
- neil-code/dialogsum-test
library_name: transformers
metrics:
- rouge
base_model:
- microsoft/phi-2
---

# Phi-2 Dialogue Summarization Model

## Model Description
This is a fine-tuned version of **Phi-2**, optimized for **dialogue summarization**. The model is trained on a dataset containing human conversations and their respective summaries, allowing it to generate concise and coherent summaries of dialogue-based texts.

## Intended Use
- Summarizing conversations from various sources, including transcripts and chat logs.
- Extracting key points from spoken or written dialogue.
- Assisting in text compression for NLP applications.

## Training Details
- **Base Model**: `microsoft/phi-2`
- **Fine-tuning Method**: PEFT (Parameter Efficient Fine-Tuning)
- **Dataset**: neil-code/dialogsum-test
- **Evaluation Metrics**: ROUGE scores for summary quality assessment. rouge1: 2.01%, rouge2: -0.29%, rougeL: 1.32%, rougeLsum: 2.53%.

## Limitations & Biases
- The model may struggle with highly technical or domain-specific dialogues.
- Potential biases present in the training data could affect summary quality.
- Summarization may sometimes miss nuances in highly informal conversations.

## How to Use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your-username/phi-2-dialogue-summarization"
tokenizer = AutoTokenizer.from_pretrained(NikkeS/Phi-2-dialogsum-finetuned)
model = AutoModelForCausalLM.from_pretrained(NikkeS/Phi-2-dialogsum-finetuned)

prompt = "Summarize the following conversation:\n\n#Person1#: Hello! How are you?\n#Person2#: I'm good, thanks. How about you?\n\nSummary:"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
output = model.generate(input_ids, max_length=100)

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