Update README.md

README.md

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----
-license: mit
----
+---
+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))