---
language: tr
license: cc-by-nc-4.0
tags:
- summarization
- t5
- turkish
- news
- abstractive-summarization
metrics:
- rouge
---

# Turkish News Summarizer (IT5-Large)

This model has been trained on a corpus of **270,000+ real, non-synthetic, and exclusively human-written** Turkish news articles and their corresponding summaries. The model is optimized to adhere to the morphological structure and formal register of the Turkish news language.

## Dataset Specifications

-   **Volume:** 273,046 news articles and summaries.
    
-   **Type:** Authentic content derived from real-time news feeds, written by professional editors.
    
-   **Domains:** Politics, Economy, Sports, Technology, and General Agenda.
    

## Training Parameters and Infrastructure

The training process was executed using high-end hardware and advanced optimization techniques:

-   **Hardware:** NVIDIA B200 (Blackwell) - 180GB VRAM.
    
-   **Base Model:** `gsarti/it5-large` (740M Parameters).
    
-   **Precision:** BF16 Mixed Precision & TF32 Core Acceleration.
    
-   **Effective Batch Size:** 160 (Per device batch: 80, Gradient Accumulation: 2).
    
-   **Optimizer:** AdamW (Fused Kernel).
    
-   **Learning Rate:** 4e-5 (with Cosine Decay Scheduler).
    
-   **Process Optimization:** Gradient Checkpointing enabled.
    

## Training Metrics and Loss Curve

The following graph illustrates the convergence of the Cross-Entropy Loss during the training phase:
![Training Loss Curve](./loss_plot.png) 

## Inference Example

```
from transformers import T5TokenizerFast, AutoModelForSeq2SeqLM

tokenizer = T5TokenizerFast.from_pretrained("{REPO_NAME}")
model = AutoModelForSeq2SeqLM.from_pretrained("{REPO_NAME}")

def summarize_news(text):
    input_text = "summarize: " + text
    inputs = tokenizer(input_text, return_tensors="pt", max_length=1024, truncation=True)
    outputs = model.generate(
        inputs.input_ids, 
        max_length=150, 
        num_beams=4, 
        early_stopping=True
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

raw_text = "Insert news text here..."
print(summarize_news(raw_text))

```

## License and Terms of Use

This model is released under the CC-BY-NC 4.0 license for research and development purposes. For commercial applications, the rights of the original data owners must be respected.