songhieng
/

khmer-mt5-summarization

@@ -1,199 +1,151 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+license: mit
+datasets:
+- kimleang123/khmer-text-dataset
+language:
+- km
+base_model:
+- google/mt5-small
+pipeline_tag: summarization
 ---
+# Khmer mT5 Summarization Model
+## 📌 Introduction
+This repository contains a **fine-tuned mT5 model for Khmer text summarization**. The model is based on Google's [mT5-small](https://huggingface.co/google/mt5-small) and fine-tuned on a dataset of Khmer text and corresponding summaries.
+Fine-tuning was performed using the Hugging Face `Trainer` API, optimizing the model to **generate concise and meaningful summaries of Khmer text**.
+---
+## 🚀 Model Details
+- **Base Model:** `google/mt5-small`
+- **Fine-tuned for:** Khmer text summarization
+- **Training Dataset:** `kimleang123/khmer-text-dataset`
+- **Framework:** Hugging Face `transformers`
+- **Task Type:** Sequence-to-Sequence (Seq2Seq)
+- **Input:** Khmer text (articles, paragraphs, or documents)
+- **Output:** Summarized Khmer text
+- **Training Hardware:** GPU (Tesla T4)
+- **Evaluation Metric:** ROUGE Score
+---
+## 🔧 Installation & Setup
+### 1️⃣ Install Dependencies
+Ensure you have `transformers`, `torch`, and `datasets` installed:
+```bash
+pip install transformers torch datasets
+```
+### 2️⃣ Load the Model
+To load and use the fine-tuned model:
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+model_name = "your-username/khmer-mt5-summarization"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+```
+---
+## 📌 How to Use
+### 1️⃣ Using Python Code
+```python
+def summarize_khmer(text, max_length=150):
+    input_text = f"summarize: {text}"
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512)
+    summary_ids = model.generate(**inputs, max_length=max_length, num_beams=5, length_penalty=2.0, early_stopping=True)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+khmer_text = "កម្ពុជាមានប្រជាជនប្រមាណ ១៦ លាននាក់ ហើយវាគឺជាប្រទេសនៅតំបន់អាស៊ីអាគ្នេយ៍។"
+summary = summarize_khmer(khmer_text)
+print("🔹 Khmer Summary:", summary)
+```
+### 2️⃣ Using Hugging Face Pipeline
+For a simpler approach:
+```python
+from transformers import pipeline
+summarizer = pipeline("summarization", model="your-username/khmer-mt5-summarization")
+khmer_text = "កម្ពុជាមានប្រជាជនប្រមាណ ១៦ លាននាក់ ហើយវាគឺជាប្រទេសនៅតំបន់អាស៊ីអាគ្នេយ៍។"
+summary = summarizer(khmer_text, max_length=150, min_length=30, do_sample=False)
+print("🔹 Khmer Summary:", summary[0]['summary_text'])
+```
+### 3️⃣ Deploy as an API using FastAPI
+You can create a simple API for summarization:
+```python
+from fastapi import FastAPI
+app = FastAPI()
+@app.post("/summarize/")
+def summarize(text: str):
+    inputs = tokenizer(f"summarize: {text}", return_tensors="pt", truncation=True, max_length=512)
+    summary_ids = model.generate(**inputs, max_length=150, num_beams=5, length_penalty=2.0, early_stopping=True)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return {"summary": summary}
+# Run with: uvicorn filename:app --reload
+```
+---
+## 📊 Model Evaluation
+The model was evaluated using **ROUGE scores**, which measure how similar the generated summaries are to the ground truth summaries.
+```python
+from datasets import load_metric
+rouge = load_metric("rouge")
+def compute_metrics(pred):
+    labels_ids = pred.label_ids
+    pred_ids = pred.predictions
+    decoded_preds = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
+    decoded_labels = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)
+    return rouge.compute(predictions=decoded_preds, references=decoded_labels)
+trainer.evaluate()
+```
+---
+## 💾 Saving & Uploading the Model
+After fine-tuning, the model was uploaded to Hugging Face Hub:
+```python
+model.push_to_hub("your-username/khmer-mt5-summarization")
+tokenizer.push_to_hub("your-username/khmer-mt5-summarization")
+```
+To download it later:
+```python
+model = AutoModelForSeq2SeqLM.from_pretrained("your-username/khmer-mt5-summarization")
+tokenizer = AutoTokenizer.from_pretrained("your-username/khmer-mt5-summarization")
+```
+---
+## 🎯 Summary
+| **Feature** | **Details** |
+|------------|------------|
+| **Base Model** | `google/mt5-small` |
+| **Task** | Summarization |
+| **Language** | Khmer (ខ្មែរ) |
+| **Dataset** | `kimleang123/khmer-text-dataset` |
+| **Framework** | Hugging Face Transformers |
+| **Evaluation Metric** | ROUGE Score |
+| **Deployment** | Hugging Face Model Hub, API (FastAPI), Python Code |
+---
+## 🤝 Contributing
+Contributions are welcome! Feel free to **open issues or submit pull requests** if you find any improvements.
+### 📬 Contact
+If you have any questions, feel free to reach out via [Hugging Face Discussions](https://huggingface.co/) or create an issue in the repository.
+📌 **Built for Khmer NLP Community** 🇰🇭 🚀