Update README.md

README.md

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----
-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# Khmer mT5 Summarization Model (1024 Tokens)
 
+## Introduction
 
+This repository contains a fine-tuned mT5 model for Khmer text summarization, extending the capabilities of the original [khmer-mt5-summarization](https://huggingface.co/songhieng/khmer-mt5-summarization) model. The primary enhancement in this version is the support for summarizing longer texts, with training adjusted to accommodate inputs up to 1024 tokens.
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+- **Base Model:** `google/mt5-small`
+- **Fine-tuned for:** Khmer text summarization with extended input length
+- **Training Dataset:** `kimleang123/khmer-text-dataset`
+- **Framework:** Hugging Face `transformers`
+- **Task Type:** Sequence-to-Sequence (Seq2Seq)
+- **Input:** Khmer text (articles, paragraphs, or documents) up to 1024 tokens
+- **Output:** Summarized Khmer text
+- **Training Hardware:** GPU (Tesla T4)
+- **Evaluation Metric:** ROUGE Score
 
-[More Information Needed]
+## Installation & Setup
 
-#### Metrics
+### 1️⃣ Install Dependencies
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+Ensure you have `transformers`, `torch`, and `datasets` installed:
 
-[More Information Needed]
+```bash
+pip install transformers torch datasets
+```
 
-### Results
+### 2️⃣ Load the Model
 
-[More Information Needed]
+To load and use the fine-tuned model:
 
-#### Summary
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
+model_name = "songhieng/khmer-mt5-summarization-1024tk"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+```
 
+## How to Use
 
-## Model Examination [optional]
+### 1️⃣ Using Python Code
 
-<!-- Relevant interpretability work for the model goes here -->
+```python
+def summarize_khmer(text, max_length=150):
+    input_text = f"summarize: {text}"
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=1024)
+    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
 
-[More Information Needed]
+khmer_text = "កម្ពុជាមានប្រជាជនប្រមាណ ១៦ លាននាក់ ហើយវាគឺជាប្រទេសនៅតំបន់អាស៊ីអាគ្នេយ៍។"
+summary = summarize_khmer(khmer_text)
+print("Khmer Summary:", summary)
+```
 
-## Environmental Impact
+### 2️⃣ Using Hugging Face Pipeline
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+For a simpler approach:
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+```python
+from transformers import pipeline
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+summarizer = pipeline("summarization", model="songhieng/khmer-mt5-summarization-1024tk")
+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'])
+```
 
-## Technical Specifications [optional]
+### 3️⃣ Deploy as an API using FastAPI
 
-### Model Architecture and Objective
+You can create a simple API for summarization:
 
-[More Information Needed]
+```python
+from fastapi import FastAPI
 
-### Compute Infrastructure
+app = FastAPI()
 
-[More Information Needed]
+@app.post("/summarize/")
+def summarize(text: str):
+    inputs = tokenizer(f"summarize: {text}", return_tensors="pt", truncation=True, max_length=1024)
+    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}
 
-#### Hardware
+# Run with: uvicorn filename:app --reload
+```
 
-[More Information Needed]
+## Model Evaluation
 
-#### Software
+The model was evaluated using **ROUGE scores**, which measure the similarity between the generated summaries and the reference summaries.
 
-[More Information Needed]
+```python
+from datasets import load_metric
 
-## Citation [optional]
+rouge = load_metric("rouge")
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+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)
 
-**BibTeX:**
+trainer.evaluate()
+```
 
-[More Information Needed]
+## Saving & Uploading the Model
 
-**APA:**
+After fine-tuning, the model can be uploaded to the Hugging Face Hub:
 
-[More Information Needed]
+```python
+model.push_to_hub("songhieng/khmer-mt5-summarization-1024tk")
+tokenizer.push_to_hub("songhieng/khmer-mt5-summarization-1024tk")
+```
 
-## Glossary [optional]
+To download it later:
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+```python
+model = AutoModelForSeq2SeqLM.from_pretrained("songhieng/khmer-mt5-summarization-1024tk")
+tokenizer = AutoTokenizer.from_pretrained("songhieng/khmer-mt5-summarization-1024tk")
+```
 
-[More Information Needed]
+## Summary
 
-## More Information [optional]
+| **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 |
 
-[More Information Needed]
+## Contributing
 
-## Model Card Authors [optional]
+Contributions are welcome! Feel free to **open issues or submit pull requests** if you have any improvements or suggestions.
 
-[More Information Needed]
+### Contact
 
-## Model Card 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.
 
-[More Information Needed]
+**Built for the Khmer NLP Community**