Turkish News Summarizer (IT5-Large)

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

• 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:

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